Tool catalogue

Choose by constraint, not hype.

Curriculum tools, their practical uses, current review status, and the constraints to check before they touch real work.

AI Lab app candidates
145tools in the catalogue

app · free-tier

Zapier

Zapier is the most widely adopted no-code automation platform, used by 3 million+ businesses to link 9,000+ apps via point-and-click "Zaps" — each Zap being a trigger → action chain that fires automatically. No programming knowledge is required: pick a trigger app (Google Forms, Gmail, Slack), choose an event, then chain one or more actions in other apps. Multi-step Zaps, conditional paths (Filters), and data formatting are available on paid plans. Since 2024–2025 Zapier has added AI-native features: a Copilot builder, AI Agents, Zapier Tables (a lightweight database), and MCP integration so tools like Claude can trigger automations directly. For biology researchers and lab managers, practical entry points are automatic logging of survey or form responses into Google Sheets, routing email alerts when instrument data arrives, syncing reference managers, or linking Slack/email to a task tracker — all without touching code.

cloudCommercial

app · free-tier

Devin Desktop

Devin Desktop (formerly Windsurf) is an AI-native code editor (built on VS Code) whose centrepiece is Cascade — an autonomous agent that reads your whole project, plans changes across multiple files, runs terminal commands, installs dependencies, and iterates until the code works, without you describing each step. Unlike Copilot, which completes single lines, Cascade handles whole tasks: "add a CSV export button" or "refactor this analysis to use pandas". It stays aware of what you type and what runs in the terminal, so it can pick up mid-task. Windsurf began as Codeium and, after a much-publicised 2025 acquisition, was renamed Devin Desktop by its new owner Cognition (makers of the Devin agent) in 2026; existing installs updated over the air, with plans and settings carried over. For biology students with some Python, it can turn "parse this BLAST output and plot an e-value histogram" into working, runnable code in minutes.

cloudCommercial

model · free

Whisper (open source)

Whisper is OpenAI's speech-to-text model, and unlike ChatGPT the weights themselves were released openly (MIT license), so anyone can run the exact same model on their own computer for free, forever, with no internet connection and no data ever leaving the machine. Two community projects make this practical: whisper.cpp runs efficiently on ordinary CPUs and Apple Silicon (Metal), while faster-whisper uses a GPU to run about 4x faster than the original implementation. Both are the same underlying model as OpenAI's paid transcription API — you're not sacrificing much accuracy for the privilege of running it locally. This is the best open-source option for turning a recorded lecture, interview, or field recording into text, and a natural fit for a course that wants students comfortable with tools that cost nothing and don't require sending data to a company.

Self-hostedOpen source

model · free

Wan 2.2

Wan 2.2 is Alibaba's Tongyi Lab open-source video model, released under a permissive Apache-2.0 license — you can download the weights, run them on your own hardware, and use the output commercially with no per-clip fee. It uses a mixture-of-experts (MoE) architecture and is the most versatile open model available: it handles both text-to-video and image-to-video (animate a still photo), producing 720p-1080p clips around 5 seconds long. This is the model to reach for if you want to understand how video generation actually works under the hood, or if you're generating enough clips that per-clip API costs would add up. The real cost is hardware: you need a GPU with at least 24GB of VRAM to run it comfortably, which usually means a rented cloud GPU (see the Inference providers chapter) rather than a laptop. Setup typically goes through ComfyUI, a node-based interface for running these models.

Self-hostedOpen source

runtime · free

vLLM

vLLM is the engine you reach for when 'a model on my laptop' has to become 'a model the whole lab hits at the same time.' Built at UC Berkeley's Sky Computing Lab and now maintained by 2,000+ contributors, its core trick is PagedAttention — managing the GPU memory used for each conversation's attention cache the way an OS pages virtual memory, so many concurrent requests share a GPU without fragmenting it — combined with continuous batching and prefix caching. The result is dramatically higher throughput per GPU than a naive single-request server under real concurrent load. It speaks an OpenAI-compatible API (plus an Anthropic Messages API and gRPC), runs 200+ Hugging Face model architectures, and supports FP8/INT8/INT4/GPTQ/AWQ quantization to fit bigger models in less VRAM. For a lab: one GPU serving ten people's simultaneous questions instead of one.

Self-hostedOpen source

app · free-tier

v0

v0 is Vercel's AI agent for building real code and full-stack apps. From a natural-language prompt it generates React + Next.js using shadcn/ui components and Tailwind CSS, shows a production-accurate live preview, and deploys to Vercel in one click. A major relaunch in February 2026 turned it into a full agent: it can search the web (with inline source links), use a browser to test and critique the apps it builds, run terminal commands in an isolated sandbox, fix its own errors, import existing GitHub repositories, and edit code in a built-in VS Code-style editor. It wires up one-click databases (Supabase, Neon, Upstash, Vercel Blob) and AI models (through the Vercel AI Gateway, no API key required) so apps can be genuinely data-driven — though it stays a React/Next.js tool at heart, strongest on polished front-ends. You own the output: every project syncs to GitHub as standard Next.js source you can clone and run anywhere.

cloudCommercial

runtime · free

Text Generation Inference

Text Generation Inference (TGI) is an open‑source toolkit that lets you deploy and serve popular open‑source large language models such as Llama, Falcon, BLOOM or GPT‑NeoX. It runs on Nvidia, AMD, Intel GPUs, AWS Trainium/Inferentia, Google TPUs or CPUs via Docker, and provides features like tensor parallelism, token streaming (SSE), continuous batching and quantization. The software is free to use; you only need the hardware and any cloud‑provider costs for the underlying compute.

Self-hostedCommercial

app · free-tier

Tavus (CVI)

Tavus built its Conversational Video Interface (CVI) specifically for realtime, back-and-forth video conversation with an AI avatar — this is the category leader for "talk to a video AI face and have it respond naturally," not for generating pre-recorded clips. You give it a persona and connect it to an LLM; it handles the low-latency video generation so the avatar listens, thinks, and replies on camera with normal conversational timing. Growth-tier plans support many concurrent sessions, which matters if you are building something like a virtual tutor or interviewer that many students talk to at once. Free tier (25 min) is enough to feel the difference between this and a pre-recorded HeyGen clip. Worth mentioning: Simli is a much cheaper realtime alternative (~$0.009/min) for teams that want the interactive pattern without Tavus's price tag, at some cost in polish.

cloudCommercial

app · paid

Synthesia

Synthesia targets corporate training and internal communications rather than individual creators. Its signature feature is turning a PowerPoint deck or a script into a full training video with a presenter avatar, in 140+ languages, with one click to export as SCORM for an LMS. It is the tool an HR or L&D team reaches for to produce "here is our new policy" videos at scale without hiring actors or camera crews. Compared to HeyGen, Synthesia leans further into templates, brand kits, and team collaboration features (approval workflows, shared avatar libraries) rather than experimentation. It does not offer a realtime interactive avatar product — everything is pre-rendered. For students, the useful lesson here is less "try this yourself" and more "this is what happens when avatar video gets adopted inside a company": localization at scale, compliance-friendly training content, and video produced by people with zero video-editing skill.

cloudCommercial

model · paid

Sora 2

Sora 2 is OpenAI's flagship text-to-video model: describe a scene in a sentence and it renders up to 60 seconds of footage at 720p (1080p on the Pro tier), holding a character's face and clothing consistent across cuts — the single hardest problem in AI video. It's built into ChatGPT Plus ($20/month) and Pro ($100/month) as a simple prompt box, and exposed as an API for developers who want to generate video programmatically. For a student this looks like: paste a description of a cell dividing or a protein folding, get back a short clip you can drop into a slide deck. The catch is that Sora doesn't generate audio — you still need a separate voice or music tool — and OpenAI has already signaled the API layer is not a long-term bet, so treat this as a snapshot of the state of the art, not a permanent platform choice. Cinematic quality is the headline feature: camera moves, lighting, and physics look convincingly like real footage rather than a slideshow of frames.

cloudCommercial

app · free

Stable Diffusion 3.5 + ComfyUI

Stable Diffusion 3.5 (Stability AI, October 2024, still the current open-weight line in mid-2026) ships in three sizes — Large (8B), Large Turbo (a faster distilled version), and Medium (2.6B, tuned for consumer GPUs) — and runs natively in ComfyUI, the node-based workflow app that is now the de facto standard local interface for it. Instead of one text box, ComfyUI gives you a visual pipeline: chain a base generation into an upscaler, a ControlNet, a LoRA, or a custom model, one node at a time, and save the whole graph to rerun later. Under the Stability AI Community License, both the weights and your generations are free — including commercial use — for any individual, lab, or organization earning under $1M in annual revenue; above that, an Enterprise license is required. For a Heidelberg lab or student project this is the only option here where nothing — no prompt, no image — ever leaves your own machine.

Self-hostedCommercial

provider · paid

Scaleway

Scaleway is a French cloud provider (part of the Iliad group) offering two EU-sovereign paths to model inference. Generative APIs is a pay-per-token, OpenAI-compatible endpoint serving open models (Llama, Mistral, DeepSeek and others) from French data centers — the "buy" path, comparable to Groq or OpenRouter but EU-hosted throughout. GPU Instances is the "build" path: rent an H100-class GPU by the hour and run vLLM, Ollama, or any engine yourself — the same self-hosting playbook as a rented Hetzner box, just from a French provider under EU jurisdiction the whole way. Either way, Scaleway markets itself explicitly on European sovereignty and a lower carbon footprint than the US hyperscalers — relevant for an institution answering both a GDPR checklist and a sustainability one.

cloudCommercial

model · free

SadTalker

SadTalker is the open-source answer to "animate one photo into a talking head": feed it a still image and an audio clip, and it generates 3D-aware head motion (not just mouth flapping) synced to the audio, using a research pipeline published alongside an academic paper. It runs entirely on your own machine or a rented GPU — no subscription, no per-minute fee, no vendor. The tradeoff is exactly what you would expect from open source: you need to set up a Python environment, download model weights, and have a GPU with enough VRAM, or rent one (a cloud GPU for an hour or two is cheap but is a real step most SaaS users skip). Quality is good but not quite at HeyGen or D-ID polish level. For students, this is the "how does the free version actually work" lesson — and a stepping stone to LivePortrait (Kuaishou), a newer open-source sibling that pushes closer to realtime with noticeably better motion quality, if you want the more advanced option.

Self-hostedOpen source

app · paid

Runway Gen-4

Runway is the tool professional filmmakers and VFX artists reach for: Gen-4 generates roughly 12 seconds at up to 1080p, but its real strength is control — you can direct camera motion explicitly, keep a character or object consistent across multiple separate shots, and combine generated footage with real footage in the same timeline. It grew out of Runway's earlier green-screen and motion-tracking AI tools, so the interface assumes you're editing a sequence, not just prompting a single clip. Pricing starts at $12/month for the Standard plan (625 credits), with higher tiers scaling credits for heavier use. For a student this is overkill for a single illustrative shot but the right choice if you're building a short explainer video with several connected scenes — say, a multi-step process like a virus entering a cell, replicating, and being cleared by the immune system, all as one consistent visual sequence.

cloudCommercial

app · byok

Retell AI

Retell AI doesn't try to be the model — it's the orchestration layer that wires your chosen speech-to-text, LLM, and text-to-speech providers together into a phone-ready voice agent, handling call routing, telephony (SIP/Twilio), conversation state, and interruption logic for you. You bring your own API keys for each component, so you keep full control and full visibility over exactly what each piece costs and how it behaves — nothing is hidden inside a black box. This transparency is why it's become popular specifically for phone-based agents (appointment booking, support lines, outbound calling), where reliability and auditability matter more than squeezing out the last few milliseconds. Vapi is a close competitor with a similar bring-your-own-stack philosophy, if you want to compare. Retell is the right choice when you want to control your stack precisely and need real telephone integration, not just a browser demo.

cloudCommercial

app · paid

Poe

Poe is Quora's AI aggregator: a single "points" currency you spend chatting with official bots for GPT-5.5, Claude Opus 4.7, Gemini 3.5 Flash, Grok, and image/video models like Nano-Banana-Pro and Veo-3.1, plus thousands of user-built "bots" (prompt wrappers and small apps on top of those models). It is the simplest, oldest version of this category — no autonomous agent, no browsing, no scheduled tasks, just model access. Pricing runs from a $4.99/mo Starter tier (10,000 points/day) to a $239.58/mo Pro Max tier (8.25M points/month), plus a free tier with a small daily allowance. Quora, Inc. is the data controller; official first-party bots do not train on your chats or use them beyond producing the response, but third-party developer bots can, so which bot you pick inside Poe matters as much as which platform you pick.

cloudCommercial

app · free

Pipecat

Pipecat is an MIT-licensed open-source framework for building the same kind of cascaded voice pipeline ElevenLabs or Retell offer commercially — voice activity detection, speech-to-text, an LLM, and text-to-speech — except you own every piece and pay nothing beyond the component costs (or $0 if you run local models). It reached v1.0 in April 2026 and ships with 60+ integrations, so you can plug in whichever STT, LLM, and TTS you already trust, including fully local ones. It's transport-agnostic, meaning it can carry audio over WebRTC via Daily or LiveKit, or other transports, so it isn't tied to one hosting vendor. LiveKit Agents (Apache-2.0) is the closest sibling project — also open source, also WebRTC-based, worth comparing if you're choosing a foundation. Pipecat is the pick when you want maximum control, zero vendor lock-in, and are comfortable doing the integration work yourself.

Self-hostedOpen source

model · free

Phi-4-mini

Phi-4-mini is Microsoft's 3.8-billion-parameter open model — small enough to run on a modest laptop (about 3 GB at 4-bit) yet capable on everyday tasks: summarising, drafting, classification and simple code. It has a 128K-token context window, far larger than most models its size, so it can read long documents. Released under the permissive MIT licence, it is the go-to 'fastest local' option when you want instant answers and do not need frontier-level reasoning.

Self-hostedCommercial

app · paid

Perplexity Max

Perplexity Max is the top-of-line ($200/mo, $2,000/year) subscription of the answer-engine already taught in "Frontier chatbots" (see the `perplexity` entry) — included here because Max is where Perplexity stops being a single-model product and becomes an aggregator: it bundles sonar-pro, gpt-5.4, claude-opus-4.8, gemini-2.5-pro and grok-4, unlimited Pro Search, unlimited Labs, and full Comet access under one price. Independent reporting (not confirmed on Perplexity's own pricing page, which returned an access error when fetched directly this session) describes a "Model Council" feature that dispatches a query across several of these models and synthesizes one answer, plus autonomous, scheduled "Background Assistants" backed by a credit pool — treat those two specifically as reported-but-not-primary-confirmed until re-checked.

cloudCommercial

app · free-tier

Perplexity

Perplexity AI launched on December 7, 2022, founded by Aravind Srinivas, Denis Yarats, Johnny Ho, and Andy Konwinski — all ex-OpenAI, Google, or Berkeley researchers. By early 2026 the company had reached a $21 billion valuation. It operates as an answer engine rather than a link aggregator: you ask a question in natural language, it searches the live web using its Sonar engine, synthesizes the top sources, and returns a cited answer with clickable references. For biology students the killer use case is literature triage: ask for "three recent papers on CRISPR off-target effects with DOIs" and get a structured, linked summary in seconds rather than manually sifting Google Scholar. Pro subscribers get access to more powerful models (Claude Opus 4.7, GPT-5.5, Gemini 3.1 Pro) and can attach files for analysis. The free tier covers most daily search needs with rate-limited model access.

cloudCommercial

provider · paid

OVHcloud

OVHcloud is a French, publicly listed cloud infrastructure company — one of the largest cloud providers headquartered in Europe — and its AI Endpoints product is a serverless, pay-per-use inference API for open-weight models (Llama, Mistral and others), hosted entirely in OVHcloud's own European data centers. Like IONOS and Scaleway, it targets the same OpenAI-compatible base-URL swap as Groq or OpenRouter, but every request stays under EU/French jurisdiction with no dependency on a US hyperscaler. OVHcloud also rents bare GPU instances for teams that want to run their own serving stack (vLLM, Ollama) rather than a managed endpoint. For a research institute, the appeal is that OVHcloud is a large, independently audited European company with a two-decade infrastructure track record — not a startup betting its first funding round on GDPR compliance.

cloudCommercial

runtime · free

OpenVINO GenAI

OpenVINO GenAI is an open‑source Python/C++/Node.js library that lets you run LLMs, vision‑language models, diffusion image generators, speech‑to‑text and text‑to‑speech on top of the OpenVINO Runtime. Install it with a single pip command (pip install openvino-genai) and run inference on CPUs, Intel GPUs or NPUs without extra dependencies. The library is Apache‑2.0 licensed and includes tokenizers and optimization features such as speculative decoding and KV‑cache eviction.

Self-hostedCommercial

provider · free-tier

OpenRouter

OpenRouter is an API aggregator that gives you a single endpoint and a single API key to access 200+ language models: Claude, GPT-4o, Gemini, Llama 3, Mistral, Hermes, Phi-3, and dozens more — including a substantial catalogue of genuinely free models (google/gemma-2-9b-it, meta-llama/llama-3.1-8b-instruct, mistralai/mistral-7b-instruct, and others). For n8n workflows in this course, OpenRouter is the recommended LLM backend: add a single HTTP Request node, set the Authorization header once, and switch between any model by changing one parameter. This makes it trivial to compare Claude vs. Llama vs. Gemini on the same task, or to build a router that tries a fast cheap model first and falls back to Claude only when needed. OpenRouter also provides unified token usage tracking across all providers in one dashboard.

cloudCommercial

app · free

opencode + voice

A hands-free way to drive opencode, the open-source terminal coding agent, entirely by voice — with speech recognition and voice output both running on hardware you control, not a cloud API. You start opencode with a local port open, then run a small Rust CLI (opencode-voice) that listens on your microphone, transcribes with Whisper on your own CPU, and forwards the text to opencode. For opencode to answer out loud, a community plugin adds a 'speak' tool the agent can call, routed to a text-to-speech engine — anything from CPU-friendly Piper to a GPU voice-cloning model. None of this is official SST tooling: it's independent open-source projects wired together via opencode's HTTP server and plugin system. For life-science students the appeal is privacy-by-architecture — review or fix code by voice near sensitive data without your spoken words hitting a third-party server — and a genuinely hands-free workflow when your hands are on a pipette, not a keyboard.

Self-hostedOpen source

app · byok

opencode

opencode is an open-source AI coding agent built by Anomaly (formerly known as the SST team, led by Dax Raad) that runs in your terminal, as a desktop app, or as an IDE extension. It reached 160,000+ GitHub stars and 7.5 million monthly developers as of May 2026, placing it among the most-adopted open-source coding agents. The core value proposition: bring your own API key from any of 75+ supported providers (Anthropic, OpenAI, Google, Mistral, or local models via Ollama), or connect your existing GitHub Copilot or ChatGPT subscription. You pay your provider directly — there is no intermediate markup. Two built-in agent modes switch with Tab: "build" executes code changes, "plan" explores and reads without modifying files. A $10/month "Go" plan provides generous access to curated open-source models (DeepSeek, Qwen, Kimi) without managing your own keys — useful as a cost-controlled entry point.

Self-hostedOpen source

app · free-tier

openclaw

openclaw (originally Clawdbot, Nov 2025; renamed Moltbot Jan 27 2026; then openclaw) is an open-source personal AI agent created by Peter Steinberger (@steipete), who announced on February 14, 2026, that he was joining OpenAI and the project would move to a foundation while remaining open-source. The project reached 247,000 GitHub stars by that date — a remarkable growth trajectory in under four months. The concept is simple: run an AI agent on your own machine and talk to it through messaging apps you already use — WhatsApp, Telegram, Signal, Discord, Slack, Google Chat, iMessage, or 15+ others. The agent can manage email, read and write calendar events, browse the web autonomously, control smart home devices, run shell scripts, and maintain a persistent memory that personalizes across sessions. It supports Claude, GPT, and local models via Ollama, so you can choose which AI model handles your tasks. The Go plan on opencode.ai is unrelated — openclaw itself is MIT-licensed and free.

Self-hostedOpen source

model · byok

OpenAI TTS

OpenAI's TTS models (tts-1 for speed, tts-1-hd for quality) turn text into speech at a price that undercuts almost every competitor — around $0.015 per million characters for tts-1 — which matters if you want to narrate a lot of content, like turning every lecture slide deck into audio. It plugs neatly into anything already using ChatGPT/GPT-4o, so if you're already scripting content with GPT you can pipe the output straight to speech without adding a new vendor. OpenAI also offers gpt-4o-transcribe on the speech-to-text side, so the same account can handle both directions of a voice pipeline. It won't win a blind listening test against ElevenLabs, but for straightforward narration — audio versions of readings, a voice for a chatbot, accessibility read-aloud — it's the practical default because it's cheap and simple.

cloudCommercial

model · paid

OpenAI Realtime API

OpenAI's Realtime API lets an app stream microphone audio straight to the gpt-realtime model and get spoken audio back — no separate transcription or text-to-speech step to wire up yourself. Round-trip latency is typically 150-400ms, so it feels like an actual conversation rather than a walkie-talkie exchange. Under the hood it behaves like a single API even though speech understanding and generation are tightly coupled to the model, which is why tone, interruptions, and back-channel sounds ('mm-hm') come through more naturally than a bolted-together pipeline. It supports function calling mid-conversation, so the agent can look things up or trigger actions while still talking. This is the option to reach for when you want the most polished, least-assembly-required voice agent and don't mind paying a premium and staying inside OpenAI's ecosystem.

cloudCommercial

runtime · free-tier

Open WebUI

Open WebUI is the piece that makes a local model feel like a product instead of a curl command: a self-hosted, ChatGPT-style chat app your whole team logs into, with role-based access control and user groups so an admin can decide who sees which models. It talks to Ollama, LocalAI, vLLM, LM Studio, or any OpenAI-compatible endpoint — so it sits on top of whatever engine your lab is running, unchanged. Beyond chat: Channels (shared, real-time spaces where people and models collaborate in one timeline), a Notes workspace, multi-model side-by-side conversations, and — on the Enterprise tier — LDAP/SSO/SCIM provisioning and usage-analytics dashboards. For a lab, it's the difference between 'ask Dave to SSH in and check the model' and everyone just opening a URL.

Self-hostedCommercial

app · free-tier

Onyx

Onyx (onyx-dot-app, MIT open source) is the application layer for LLMs: an all-in-one AI chat and enterprise search that connects to 50+ apps — Google Drive, Slack, GitHub, Confluence, Salesforce and more — and answers with agentic RAG (hybrid search, contextual retrieval and LLM-built knowledge graphs), each claim cited back to the source. On top of search it adds custom AI agents with their own instructions, knowledge and actions; deep research; a sandboxed code interpreter; web search; and Actions/MCP to reach external tools. It works with any model — self-hosted (Ollama, vLLM, LiteLLM) or the frontier (Anthropic, OpenAI, Gemini) — and runs as managed Onyx Cloud or self-hosted on your own Docker/Kubernetes, with SSO and role-based access. Its angle, versus AnythingLLM, is connector-fed, team-scale search across living apps rather than files you upload.

Self-hostedOpen source

runtime · free

ONNX Runtime GenAI

ONNX Runtime GenAI is an open‑source extension that lets you execute generative AI models (LLMs, diffusion, speech) on‑device using ONNX Runtime. It provides Python, C#, C/C++, Java and Objective‑C bindings and supports CPU, CUDA, DirectML, OpenVINO, WebGPU and other accelerators across Linux, Windows, macOS, Android and iOS. The library is free under the MIT license; you install it via `pip install onnxruntime-genai` or build from source for custom hardware.

Self-hostedCommercial

runtime · free

Ollama

Ollama runs open large language models locally from the command line and exposes them through an OpenAI-compatible API on localhost:11434. Free and open source, it handles model download, quantization, and GPU acceleration for you, and runs as a long-lived service — a private endpoint your scripts, apps, or whole lab can call without sending data to the cloud or paying per token. It carries a curated model registry plus any GGUF model. For a biology student it is the way to give analysis scripts a local LLM: point your Python at localhost and have it summarize results or draft figure captions, fully offline.

Self-hostedOpen sourceDocker

model · paid

Nano Banana Pro (Gemini 3 Pro Image)

"Nano Banana Pro" is Google's public nickname for Gemini 3 Pro Image, released as a preview in November 2025 and reaching general availability in June 2026. Unlike a single-purpose diffusion model, it runs on Google's Gemini 3 multimodal reasoning stack — so it can ground an image in real facts pulled via Search (an accurate chart of real data, a correctly structured molecule diagram), follow long multi-part instructions in one pass, and render dense, accurate text and data labels across languages. It is Google's direct replacement for the Imagen line, which is being retired: Imagen 4's endpoints shut down 17 August 2026, with Google recommending migration to this Gemini image family (the cheaper sibling is gemini-3.1-flash-image). Access is via the Gemini app, the Gemini API, or Vertex AI — useful if a lab already runs on Google Cloud/Workspace.

cloudCommercial

app · free

n8n

n8n is an open-source, node-based workflow automation platform that lets you chain APIs, AI models, databases, and files into working pipelines — without writing a backend. Each "node" is a building block: an HTTP request, a code block, an AI call, an IF-branch, a file writer. Chains of nodes become workflows. n8n is the backbone of this entire course: you will connect PubMed APIs, call LLMs, parse XML, route messages via Mattermost, and build multi-stage research pipelines — all inside its visual canvas. Unlike tools that wrap AI features for non-technical users, n8n exposes the full plumbing: you see every API call, every error, every output. That transparency is deliberate — it trains real systems thinking. First released in 2019 by Jan Oberhauser; v1.0 in 2023; fair-code licensed (free self-host, cloud from $20/mo).

cloudSelf-hostedOpen sourceDocker

model · free

Moshi (Kyutai)

Moshi, from French lab Kyutai, is a genuinely different architecture from everything else on this page: instead of chaining separate speech-to-text, language model, and text-to-speech stages, it's one model trained end-to-end on audio that listens and speaks simultaneously (full-duplex), the way humans actually converse — it can be interrupted mid-word and react instantly because there's no hand-off between separate components. That collapses latency to under 200ms and makes overlapping speech and natural back-channeling ('yeah', 'right') possible in a way cascaded pipelines struggle to fake. It's released under CC-BY-4.0 and can be self-hosted entirely in the EU, which matters if data residency or privacy is a concern. Ultravox is the other notable open speech-native model, taking a slightly different approach (audio feeds directly into an LLM, skipping a separate ASR step). Both require a decent GPU to run at real-time speed — this is the frontier-research end of open source, not a weekend project.

Self-hostedOpen source

model · free

Ministral 3

Ministral 3 is Mistral AI's family of small open-weight models — the 8B is a capable daily-driver that runs on a normal laptop, with a 256K context window, image input and a permissive Apache-2.0 licence (free for commercial use). Because Mistral is a European company and you run the weights yourself, nothing leaves your machine — the simplest GDPR-safe setup for a European business. The family scales from a ~3B edge model to a 14B, and a coding-focused sibling, Devstral Small 2 (24B, Apache-2.0), targets software work.

Self-hostedCommercial

model · paid

Mistral Medium 3.5

Mistral Medium 3.5 is the current flagship-class model from Mistral AI, a French company that hosts inference on EU servers — so for a European business it is the natural GDPR-safe managed-cloud choice. It is a 128B multimodal model (text + images) with a 256K context window and strong coding and agentic performance, at a fraction of US-flagship prices. Call it on Mistral's EU-hosted 'La Plateforme', via EU regions of the big clouds, or self-host the weights (a modified-MIT licence) for full control.

cloudCommercial

model · paid

MiniMax-M2

MiniMax-M2 is a sparse mixture-of-experts model (230B total, only 10B active per token) built for coding and agentic work — multi-file edits, code-run-fix loops, and long tool chains. At ~205K context and ~115–130 GB in 4-bit, it actually fits on a 128 GB unified-memory box or a small multi-GPU rig, so you can genuinely self-host it — and its 'modified MIT' licence permits commercial use. It is also very cheap via the MiniMax API. Note the newer MiniMax-M2.7 is stronger but ships under a non-commercial licence.

Self-hostedCommercial

app · free-tier

Make

Make (formerly Integromat) is a visual drag-and-drop platform for connecting apps and automating multi-step workflows — no code required. You design "scenarios": flowchart-like canvases where data travels from a trigger (a new row in Google Sheets, an incoming email) through a chain of actions (send to Slack, update Airtable, call an API, run an AI model). With 3,000+ pre-built app integrations — including Google Workspace, Notion, PubMed via HTTP, Slack, and 400+ AI services — Make covers the connective tissue of a modern lab without writing a single line of code. Its key differentiator over Zapier is a fully visual canvas with branching logic, loops, error handlers, and data transformation built right into the diagram. For biology researchers, practical starting points include automating literature-alert emails into a shared database, triggering data-export pipelines when an instrument writes a file, or routing student form submissions to grading spreadsheets automatically.

cloudCommercial

app · free-tier

Lovable

Lovable was founded in Stockholm in 2023 by Anton Osika and Fabian Hedin under the name "GPT Engineer App," rebranded to Lovable in December 2024, and achieved unicorn status ($1.8 billion valuation) in July 2025 after raising $200 million Series A. By December 2025 it had raised an additional $330 million at a $6.6 billion valuation and was approaching 8 million users. The platform lets you describe an application in chat, watch a live preview update in real time, and push the generated code to a GitHub repository with one click. Technically it generates React + TypeScript + Tailwind CSS applications with Supabase as the backend (authentication and database). You can refine the app iteratively — connect Figma designs, describe UI tweaks, or add integrations like Stripe and Resend. For biology students, Lovable shines at building shareable tools: a lab results visualizer, a gene expression dashboard, or a booking form — and the resulting code is portable.

cloudCommercial

runtime · free

LocalAI

LocalAI is the front door: one small core binary that speaks the OpenAI and Anthropic APIs, and routes each request to the right backend behind the scenes — llama.cpp for one model, vLLM for another, whisper.cpp for audio, Stable Diffusion for images — all through the same endpoint. Backends ship as OCI images and run as isolated gRPC processes, so a crash in one never takes down the others, and you install/upgrade a backend without touching the core. That's the concrete team win: n8n, AnythingLLM, or your own scripts all point at one stable URL forever, even as you swap which engine actually answers underneath — a new GPU, a new quantization, a different model family, none of it requires reconfiguring every downstream tool. It runs on NVIDIA, AMD, Intel, or Vulkan GPUs with autodetection, or CPU-only, and as of 2026 adds WebRTC real-time audio, MCP-based agents, and speaker diarization.

Self-hostedOpen source

runtime · free

llm-d

llm‑d is an open‑source, CNCF‑sandbox project that lets you serve large language models at scale on any accelerator (GPU, TPU, XPU, CPU, etc.) using Kubernetes. It provides ready‑made Helm charts and “well‑lit paths” that integrate model servers such as vLLM and SGLang, turning single‑node engines into production‑grade serving clusters. The software is free under the Apache 2.0 license and runs wherever you can run a Kubernetes cluster.

Self-hostedCommercial

runtime · free

llama.cpp

llama.cpp is the C/C++ engine that Ollama itself is built on — Ollama wraps it in a friendly Go layer (model pull, versioning, scheduling) that hides the knobs. This chapter teaches the layer underneath: llama.cpp's own server, for when a lab needs something Ollama's abstraction doesn't expose — a specific quantization, a build flag for an unusual CPU, or a single static binary with zero dependencies you can drop on hardware Ollama doesn't officially support. It reads the GGUF model format, and its stated goal is state-of-the-art performance on 'a wide range of hardware, locally and in the cloud': Apple Metal, x86 (AVX/AVX2/AVX512/AMX), RISC-V, NVIDIA CUDA, AMD HIP, Intel SYCL, Vulkan, and CPU+GPU hybrid — often the only realistic engine on a machine with no supported GPU at all. Its own server exposes the same OpenAI-compatible endpoints (chat, completions, embeddings), so it slots into this stack exactly where vLLM would, just with different trade-offs.

Self-hostedOpen source

runtime · free

llama-swap

A lab GPU rarely has room to keep five models loaded at once — llama-swap is the small, single-purpose fix: a zero-dependency Go proxy that sits in front of llama.cpp, vLLM, or any OpenAI/Anthropic-compatible server, and starts, stops, or swaps the right backend process based on the `model` field in the incoming request. A YAML file maps each model name to the shell command that runs it; ask for a model that isn't loaded and llama-swap starts it (stopping another if VRAM is tight), with TTL-based auto-unload and 'groups' for the models you do want to keep resident together. It doesn't make any engine faster — it makes a single GPU behave like it's serving a whole model catalogue instead of one fixed model, which is exactly the gap between a personal Ollama box and a lab that wants to offer five different models without five GPUs.

Self-hostedOpen source

model · free

Llama 4 Scout

Llama 4 Scout is Meta's open mixture-of-experts model — 109B total parameters but only 17B active per token — built for a very large context window and strong general use. Quantized, it fits on a single high-end (80 GB) GPU. It is multimodal and broadly capable, backed by Meta's huge ecosystem of fine-tunes and tooling. The catch is licensing: Llama 4 uses Meta's Community Licence, which (unlike Apache/MIT) adds conditions for the largest platforms.

Self-hostedCommercial

app · free

Langflow

Langflow is a free, open-source visual tool for building AI applications without writing much code. You drag pre-made blocks — an LLM, a document store, a chat input, an agent, a web-fetch tool — onto a canvas and wire them to design how information flows. It's a friendly front end over Python and LangChain: every block is real Python you can open and edit, but beginners never have to. A built-in Playground lets you chat with your flow and watch the agent reason and pick tools in real time. Typical builds are RAG chatbots that answer questions from your own PDFs or a website, and multi-step agents. Finished flows export as an API or MCP server, so a scientist could turn a literature-Q&A bot or a data-lookup assistant into something a whole lab can use.

Self-hostedOpen sourceDocker

model · free

Kokoro 82M (open source)

Kokoro is a genuinely surprising result: an 82-million-parameter, Apache-2.0-licensed text-to-speech model that produces voice quality competitive with systems many times its size, while running comfortably on a CPU or a modest 2-4GB of GPU memory. That makes it realistic to run on a student laptop, embed in a small app, or self-host without renting a server. Its even lighter sibling, Piper, trades a little quality for extreme efficiency — it's designed to run on hardware as small as a Raspberry Pi, the natural choice if you're building something like an offline audio kiosk or an accessibility device. Worth knowing: some other open TTS models with excellent voice-cloning (XTTS-v2, F5-TTS) are released under non-commercial licenses, so they're fine for a course project but not for anything you'd sell or deploy commercially — Kokoro's Apache-2.0 license doesn't have that restriction.

Self-hostedOpen source

app · free

KNIME

KNIME Analytics Platform is a free, open-source visual workbench where you build data pipelines by connecting "nodes" on a canvas — no programming required. Each node performs one step: read a CSV, filter rows, run a statistical test, plot a chart, export results. You chain them into a workflow that runs top-to-bottom, reproducibly, every time. With 300+ connectors (Excel, databases, cloud storage, REST APIs) and built-in machine learning, it covers the full data-science lifecycle from raw data to publication-ready output. A growing AI/LLM extension lets you wire in language-model nodes for text classification, summarisation, and RAG pipelines — all still drag-and-drop. For biology researchers at COS Heidelberg, KNIME is ideal for cleaning messy instrument exports (mass-spec, NGS, flow-cytometry CSVs), building a reproducible no-code analysis pipeline, and sharing that exact workflow as a "recipe" with collaborators — no Python environment needed.

Self-hostedOpen source

app · byok

jcode

jcode is an open-source terminal coding agent — a Rust "harness" built by Solo Systems (MIT-licensed, 8,600+ GitHub stars within months of its January 2026 launch) whose whole design is to squeeze the most useful work out of whatever model you point it at. The model-access story is what sets it apart: bring your own API key, reuse a Claude or OpenAI subscription you already pay for via OAuth, or run fully local models through Ollama or LM Studio — so your code and your costs can stay on your own machine. Beyond a normal chat agent it adds persistent memory (it keeps learning your project across sessions), side panels, background tasks, and swarm coordination — running several agents in parallel to scale test-time compute on a hard task. It speaks MCP, ships its own open "jcode bench", and installs with a single command on macOS, Linux, or Windows.

Self-hostedOpen source

runtime · free

Jan

Jan is an open-source (Apache-2.0), offline-first desktop assistant — a ChatGPT-style app whose models run 100% locally, so no data leaves your machine. It ships a built-in model Hub for one-click downloads and a familiar chat UI, and it is the most privacy-conscious of the local options: you can work fully offline, then optionally add an OpenAI, Anthropic, or Groq key per thread if you want to call a cloud model from the same app. For a biology student it is the tool for sensitive work — draft notes about unpublished results with the network off — combining privacy, openness, and a friendly interface.

Self-hostedOpen source

provider · paid

IONOS AI Model Hub

IONOS — Europe's largest hosting and cloud provider, headquartered in Germany — runs the AI Model Hub: a pay-as-you-go, OpenAI-compatible inference API serving open-weight models (Llama, Mistral and others) entirely from German, ISO 27001-certified data centers. For a German or EU institution the pitch is a straightforward answer to "where does the data sit": the same base-URL swap used for Groq or OpenRouter, but every request now stays on infrastructure governed by German law, with no US CLOUD Act exposure to explain to a data-protection officer. It sits at the EU-managed-SaaS rung of the data-residency spectrum — no hardware to operate, but a vendor and infrastructure both squarely inside the EU. It suits a lab or SME that wants hosted convenience without leaving GDPR's home turf, complementing rather than replacing a fully self-hosted setup for the most sensitive workloads.

cloudCommercial

model · free

HunyuanVideo

HunyuanVideo is Tencent's large open-source text-to-video model, built as a full-attention transformer rather than the more compute-efficient architectures some competitors use. That architectural choice buys quality — 1280x720 output with strong visual fidelity for around 5-second clips — at the cost of hardware demands: you need 60GB or more of VRAM to run it, which in practice means a multi-GPU rented setup, not a single consumer card. It's free to download and use (no per-clip fee), and like Wan 2.2 it's typically run through ComfyUI. For a student, HunyuanVideo is worth knowing about as the 'high-end open' option — useful if you're already renting serious GPU capacity for other work (training a model, running a large LLM locally) and want to add video generation to the same rig, but Wan 2.2 or LTX-Video are more practical starting points given the lower hardware bar.

Self-hostedOpen source

app · paid

HeyGen

HeyGen is the tool most people mean when they say "AI avatar video." Upload a photo (or use a stock avatar), type or paste a script, pick a voice, and it renders a lip-synced talking-head video in minutes — no camera, no studio. Its "Avatar IV" model produces natural mouth movement and expression from a single still photo. Beyond pre-recorded clips, HeyGen also ships an Interactive Avatar / Streaming Avatar API: you can wire an avatar to an LLM and have it hold a live, low-latency video conversation — useful for kiosks, virtual presenters, or a "talking chatbot" front end. The free tier is enough to try it; anything resembling regular use (a course, a product demo library, a support bot) needs a paid plan. Good starting point for students because the pre-recorded workflow requires zero technical setup — it is the closest thing to "PowerPoint with a face."

cloudCommercial

app · free

Hermes

Hermes Agent is a self-hosted, open-source autonomous AI agent from Nous Research — "the agent that grows with you." Unlike a chat window you start fresh each time, Hermes keeps a persistent memory of your preferences, projects, and environment across every session, so you stop re-explaining context. When it solves a hard problem it writes a reusable "skill" document (compatible with the agentskills.io standard) so it never forgets how to do it again. You reach it through the messaging apps you already use — Telegram, Discord, Slack, WhatsApp, Signal — or the CLI, all from one gateway process, and a built-in cron scheduler lets it run unattended tasks (daily digests, nightly backups, weekly audits) described in plain language. It runs on local models for full privacy or cloud models for more power, and deploys six ways (local, Docker, SSH, Singularity, and the serverless Modal/Daytona backends that hibernate when idle). For a researcher it is the tool when you want a private, always-on assistant that accumulates context about your work instead of forgetting it. (Note: distinct from the Hermes large language models, also by Nous Research — this is the agent that runs on top of a model.)

Self-hostedOpen sourceDocker

provider · free-tier

Groq

Groq is a hardware + inference company that runs open-weight models (Llama 3, Gemma 2, Mistral, Whisper) on custom LPU (Language Processing Unit) chips at exceptional speed — typically 500-800 tokens per second, compared to 50-100 on GPU-based inference. The speed difference is visceral: a 500-word response appears in under a second instead of five. For biology students the practical implication is fast iteration: testing a prompt, seeing the result, adjusting, and re-running takes seconds rather than minutes. Groq is especially valuable for batch-style pipelines in n8n where you need to process 50+ papers quickly: at 500 tok/s you can summarise 50 abstracts in under two minutes. Free tier available at console.groq.com with generous daily limits for the 8B models; paid tier for heavier use. Important: use your own free API key — the shared course key is restricted.

cloudCommercial

model · free

gpt-oss-20b

gpt-oss-20b is OpenAI's smaller open-weight model — a mixture-of-experts design with 21B total parameters but only ~3.6B active per token, so it runs fast and fits in about 16 GB of memory. It ships natively in the 4-bit MXFP4 format, has a 128K context window, and offers strong tool-use and reasoning for its size, under an Apache-2.0 licence. It is the sweet spot: a genuinely capable model you can run privately on a normal laptop or a single consumer GPU.

Self-hostedCommercial

model · paid

GPT-5.4 nano

GPT-5.4 nano is OpenAI's cheapest current tier — about $0.20 / $1.25 per 1M tokens — built for simple, high-volume calls: classification, extraction, routing, short drafts. Like Haiku and Gemini Flash-Lite it is the 'cheap leg' you route bulk work to while a flagship handles the hard steps. It plugs into OpenAI's large tool and integration ecosystem. Closed and cloud-only; OpenAI now offers EU data-residency endpoints (about a 10% uplift) for models released from March 2026, which helps the GDPR story — confirm your specific model and region qualify.

cloudCommercial

model · paid

GLM-5.2

GLM-5.2 is Zhipu AI's (Z.ai) flagship open-weight model and, as of mid-2026, the strongest open model for coding and long-horizon agentic work — within a few points of Claude Opus on agent benchmarks, at roughly a sixth of the price. It is a large mixture-of-experts model (~753B total parameters, ~40B active per token) with a 1M-token context and a reasoning mode, released under the permissive MIT licence. The weights are genuinely downloadable, but at ~1.5 TB it is cluster-scale, so most people use it via the Z.ai API or OpenRouter. A smaller GLM-4.7-Flash (~31B) is the self-hostable option.

Self-hostedCommercial

app · free-tier

GitHub

GitHub is the platform where most of the world's code is hosted, reviewed, and shipped: repositories, pull requests, Issues, and GitHub Actions for CI/CD, plus Codespaces for a cloud dev environment you can open from any browser. It's also fast becoming the place where AI agents do real work, not just where humans write code. The Copilot coding agent takes a GitHub Issue, works in an isolated sandbox, and opens a pull request on its own; GitHub Actions can run AI steps as part of a build; and GitHub's official MCP server lets any MCP-compatible agent (Claude included) read and write repos, Issues, and PRs directly. The catch is the "proprietary incumbent" trade-off: GitHub is free to start and has enormous network effects (open-source projects, collaborators, integrations all assume you're there), but it's owned by Microsoft, closed-source, and there's no self-hosting your way out short of the expensive Enterprise Server tier.

cloudOpen source

app · free

Gitea

Gitea is a self-hosted alternative to GitHub: you install it on your own server (or a cheap VPS) and get the same core workflow — git repos, pull requests, issue tracking, code review — without a single commit leaving your own infrastructure. It ships as one lightweight Go binary or Docker image, so it runs comfortably on hardware that GitHub-scale tools would never fit on. Gitea Actions gives you GitHub-Actions-compatible CI/CD, so workflow files mostly copy over unchanged, meaning you can run AI coding agents, test suites, and deploy pipelines entirely on infrastructure you control. It also bundles a package registry (npm, PyPI, Docker images, and more) so a lab or small team can host code, packages, and CI in one place. For biology students, the practical case is control: unpublished thesis code, sensitive datasets, or a pre-print analysis pipeline stay on a server you or your institution owns, not a third-party cloud.

Self-hostedCommercialDocker

app · paid

Genspark

Genspark (Genspark Inc., a MainFunc Inc. affiliate with entities in Singapore, the UK and elsewhere) markets itself as moving from "AI tool" to "AI employee": Super Agent combines nine core LLMs with 80+ specialised tools to write reports, build slide decks, make calls, and generate video, and 2026 added Genspark Claw — its own always-on messaging-app agent, directly parallel to Abacus's Claw. A free tier gives 100 credits/day; paid tiers (Plus $24.99/mo, Pro $249.99/mo) scale credits 100x and add 1TB+ storage. Genspark states a Zero Training and Zero Data Retention policy and cites GDPR compliance and SOC 2 Type II for enterprise clients, though no EU-region hosting option is published for the consumer tiers.

cloudCommercial

model · paid

Google Gemini Live

Gemini Live is Google's answer to the same problem as OpenAI's Realtime API — stream audio in, get spoken audio back — but priced roughly 3-4x cheaper per minute, which matters a lot once you're running voice agents at any real volume. Latency sits around 100-200ms, on par with or better than the competition. Billing is unified per-token (audio, text, and tool calls all count against the same token meter) rather than a separate per-minute voice tariff, which makes cost easier to reason about if you're already used to Gemini's text pricing. It integrates with the rest of the Gemini API, so tool use, grounding, and multimodal input (you can show it a video frame while talking) come for free. For a student prototyping a cheap always-on voice tutor or lab-notebook assistant, this is usually the best cost-to-quality ratio among the managed options.

cloudCommercial

model · paid

Gemini 3.1 Flash-Lite

Gemini 3.1 Flash-Lite is Google's ultra-cheap, low-latency tier — about $0.25 / $1.50 per 1M tokens — and unlike most budget models it is fully multimodal (text, image, video, audio and PDF in) with a 1M-token context window. That makes it a strong cheap leg for high-volume multimodal work: bulk document, image or transcript processing. It runs on Google's cloud, with EU data residency available through Vertex AI regions (e.g. Frankfurt). Closed and cloud-only; step up to Gemini 3.1 Pro when a task needs the frontier tier.

cloudCommercial

app · free-tier

Gemini

Gemini is Google DeepMind's flagship large language model, publicly launched December 6, 2023, developed through a merger of Google Brain and DeepMind under CEO Demis Hassabis. Google co-founder Sergey Brin was pulled out of retirement as a core contributor. The model is natively multimodal — it processes text, images, audio, and video in a single prompt — and by 2026 its 2.5 Flash variant with "thinking mode" has become genuinely competitive with Claude and GPT on complex reasoning benchmarks. The practical advantage for students using Google's ecosystem is deep Workspace integration: Gemini drafts full Google Docs from an outline, auto-fills Sheets tables from natural-language descriptions, summarizes Gmail threads, and generates Meet meeting notes automatically. Google AI Plus ($20/month) unlocks Gemini's most capable models; Workspace Business Standard (€13.60/user/month billed annually) bundles AI features across Docs, Sheets, and Gmail.

cloudCommercial

model · free-tier

FLUX.2

FLUX.2 (Black Forest Labs, the team behind the original Stable Diffusion) is a frontier image family released November 2025 and split into four tiers by openness: klein (added January 2026 — a 4B model under Apache 2.0, fully free and commercially unrestricted, runs on a single consumer GPU like an RTX 3090/4070), dev (a 32B open-weight model that needs a paid commercial license to use beyond research), and flex/pro/max (proprietary, API-only, the sharpest photorealism and multi-image editing). That spread — a genuinely free Apache-2.0 tier all the way up to an enterprise API — makes it the natural "which one is actually free" answer among the closed frontier vendors, and it runs natively in ComfyUI from day one. Its standout capability is multi-reference editing: feed it several reference images and one instruction, and it composes a single coherent edit.

cloudSelf-hostedCommercial

app · free-tier

Flowise

Flowise is an open-source, visual tool for building AI agents and chatbots without writing code. Instead of programming, you drag boxes called "nodes" onto a canvas and connect them — one node for a language model, one for your documents, one for a search tool — so you literally see how information flows. A non-programmer can assemble a working assistant: e.g. a chatbot that answers questions from your own PDFs of lab protocols or papers (a technique called RAG, retrieval-augmented generation). You build and test in the browser, then publish the agent as a chat widget you embed on a site or call from other software. It sits between hand-written code and rigid pre-made apps — more flexible than a fixed chatbot builder, far gentler than coding an agent from scratch. Supports 100+ models, vector databases, and tools.

Self-hostedOpen sourceDocker

provider · free-tier

EUrouter

EUrouter provides a drop‑in replacement for the OpenAI API that routes requests to over 100 AI models hosted on EU servers, guaranteeing data never leaves Europe. It offers smart routing for cost, latency or quality and includes per‑key limits, spend caps and real‑time observability. Pricing starts free (up to 10 K requests/month) with paid plans at €39/mo, €99/mo and custom enterprise options. The service runs as a cloud API reachable at https://www.eurouter.ai/api/v1.

cloudCommercial

app · free-tier

ElevenLabs Conversational AI

ElevenLabs built its reputation on the most natural-sounding text-to-speech on the market, and its Conversational AI product wraps that voice engine into a full pipeline: speech-to-text, your choice of LLM, then ElevenLabs' own TTS for the reply. Because it's cascaded rather than a single speech-native model, it's easier to swap the LLM brain in and out, and easier to debug when something goes wrong — you can inspect the transcript at each stage. The trade-off is you're stacking three costs (STT, LLM, TTS) and three hops of latency, though ElevenLabs has tuned the pipeline hard to keep it feeling responsive. This is the pick when voice quality — the actual timbre, emotion, and naturalness of what comes out of the speaker — matters more to your use case than shaving the last 100ms of latency.

cloudCommercial

app · free

Dify

Dify is an open-source platform for building LLM-powered applications — chatbots, document Q&A systems, agent pipelines — through a visual interface. It handles the full stack: prompt management, RAG (retrieval-augmented generation) with a built-in vector store, tool calling, conversation memory, and a publishable chat UI. Where n8n excels at API-level automation, Dify specialises in LLM-centric apps that need knowledge bases: you upload PDFs or web pages, Dify chunks and embeds them, and your chatbot can answer questions grounded in that corpus. For biology students this means: upload 20 papers on a topic, deploy a chatbot, and ask it to synthesise findings across the corpus. Dify supports dozens of model providers (OpenAI, Anthropic, Groq, local Ollama) through a single UI. Self-hostable under the Apache 2.0 licence; cloud version free up to 200 messages/day.

cloudSelf-hostedOpen source

model · paid

DeepSeek V4

DeepSeek V4-Flash is a frontier-class model at a fraction of the usual price — roughly $0.14 per 1M input tokens, around 35x cheaper than the big US flagships — with a 1M-token context and a thinking mode. DeepSeek publishes its model weights (reported under the permissive MIT licence), though the full models are cluster-scale, so most people use the cheap hosted API. It is text-only. When cost or open weights matter most, DeepSeek is the value pick.

Self-hostedCommercial

provider · byok

Deepgram (Nova-3)

Deepgram's Nova-3 model is widely regarded as the most accurate proprietary speech-to-text engine available: it has the lowest word-error-rate among commercial transcription APIs and handles both pre-recorded audio (batch) and live microphone streams (real-time) well. For a student this is the tool behind transcribing interviews, lab meeting recordings, or a live captioning feature in an app — anywhere accuracy on technical vocabulary and noisy audio matters more than cost. It's also cheap at scale: batch transcription runs about $0.0043 per minute of audio, and streaming about $0.0077 per minute, which is inexpensive compared to hiring a human transcriber but still an ongoing cost compared to running something locally. Like the other developer-facing tools in this chapter, using it means calling an API rather than clicking a button on a website.

cloudCommercial

app · paid

D-ID

D-ID made its name animating a single photo into a talking head — feed it any portrait and a script or audio clip, and it lip-syncs the face to the speech. That core "Creative Reality Studio" is the pre-recorded side. The more interesting recent development is D-ID Agents (V4 Visual Agents): a realtime avatar wired directly to an LLM, with sub-0.5-second response latency and 4K video, aimed at customer-service and website-greeter use cases. It is a direct competitor to Tavus and HeyGen's interactive product in that space. D-ID's entry price is the lowest of the paid proprietary tools, making it a reasonable second stop for students after HeyGen to compare photo-animation quality and see a different take on the "embed a talking avatar on your website" pattern.

cloudCommercial

app · free-tier

CrewAI

CrewAI is an open-source Python framework for building multi-agent AI systems. You define several role-playing agents (e.g. a "researcher" and a "writer"), give each a goal and tools, and assign them tasks; together they form a "crew" that collaborates to complete complex work. It also offers "Flows" for more structured, event-driven control. Built from scratch with no dependency on LangChain, it emphasizes a lean, fast runtime. Crucially, it is code-first: you write Python (configured via YAML + Python files) rather than dragging boxes on a canvas, so it suits learners comfortable with a little scripting. It sits a step beyond visual no-code tools like Flowise or n8n — more flexible and powerful, but you trade the visual builder for actual code. A paid hosted platform adds a visual editor, deployment, and monitoring on top.

Self-hostedOpen source

app · paid

Claude Cowork

Claude Cowork is the third way to use Claude — a "Tasks" mode inside the Claude Desktop app, next to Chat and Claude Code. It runs the same agentic engine that powers Claude Code, but with no terminal and no coding: you describe an outcome, Claude proposes a plan, and once you approve it works across your local files, folders and apps to hand back a finished deliverable — organised files, a drafted report, a populated spreadsheet, a synthesised literature review. Built for non-technical knowledge workers (research, operations, finance, legal, marketing), it runs shell commands and code in an isolated virtual machine scoped to the folders you authorise, and for bigger jobs coordinates several sub-agents in parallel. Connectors (Gmail, Drive, Chrome, Slack) and job-function plugins add your real tools and know-how, and a /schedule command turns any task into recurring work. For a scientist, it's the assistant that turns a folder of messy data and notes into a clean, finished output.

cloudCommercial

app · paid

Codex

Codex is OpenAI's agentic coding tool — a terminal agent (plus a VS Code extension and managed cloud tasks) that reads your repository, edits files, runs commands, and iterates on test failures until they pass. The CLI is open source (Rust) and runs locally in your own checkout, so your code stays on your machine; the models behind it are OpenAI's frontier models, billed through a paid ChatGPT plan or API credits. For a biology student it shines at refactoring a messy analysis script, adding a feature to a lab tool, or debugging a failing notebook end-to-end.

cloudOpen source

model · paid

Claude Sonnet 5

Claude Sonnet 5 is Anthropic's balanced tier — most of Opus 4.8's coding, agentic and writing quality at a lower price, which makes it the everyday production workhorse for many teams. It has a 1M-token context window, takes images, and supports prompt caching (cache hits at ~10% of input price) and 50%-off batch processing — both big levers for cutting an SME's bill. Introductory pricing is $2 / $10 per 1M tokens through 31 Aug 2026, then $3 / $15. Closed and cloud-only; for GDPR, route via an EU region of AWS Bedrock or Google Vertex.

cloudCommercial

model · paid

Claude Opus 4.8

Claude Opus 4.8 is Anthropic's flagship-class model, widely regarded as the strongest all-rounder for real coding and agentic work, plus careful analysis and writing. It has a 1M-token context window, takes images, and offers an adjustable 'effort' dial to trade speed for depth on hard problems. It is a closed cloud model — no self-hosting — but the quality ceiling and reliability on long, multi-step tasks are why it is the default frontier pick for serious work. Anthropic's even-higher Fable 5 sits above it.

cloudCommercial

model · paid

Claude Haiku 4.5

Claude Haiku 4.5 is Anthropic's fast, low-cost tier — near-frontier quality for well-scoped, high-frequency work at $1 / $5 per 1M tokens. It is the model you route the bulk of cheap, repetitive calls to (classification, extraction, short drafts, routing) while reserving Opus or Sonnet for the genuinely hard steps — the cost-routing pattern that keeps an SME's bill sane. It has a 200K context window, takes images, and supports prompt caching and 50%-off batch. Closed and cloud-only; for strict GDPR prefer an EU cloud region or a self-hosted open model.

cloudCommercial

app · free-tier

Claude Desktop

Claude Desktop is Anthropic's native desktop application for macOS and Windows (no Linux version as of May 2026) that bundles the Claude chat interface, Claude Code, and the Cowork feature into one place. The key advantage over the web interface is desktop extensions: Claude can directly read and write local files, control your browser via the Claude in Chrome connector, connect to Google Drive, Slack, and Outlook, and monitor code changes as you save them. It is available on all plan types — including the free plan — though Claude Code and higher-model access require paid tiers. For biology students, the most useful daily workflow is dragging a PDF or data file into the chat window and asking Claude to summarize, annotate, or extract tables from it — no upload step, no copying URLs. Memory and project preferences sync across your phone and browser.

cloudCommercial

app · paid

Claude Code Plugins

Claude Cowork is the plugin and skill-sharing layer built on top of Claude Code. Where Claude Code is a personal coding agent, Cowork makes it a team tool: organisations author shared "skills" (reusable slash-command behaviours), wire Claude into their specific apps and APIs via MCP connectors, and distribute role-matched configurations to every developer on the team. A skill might be a deployment runbook, a code-review protocol, or a specialised data-pipeline agent — once published, any team member invokes it with a single slash command. Cowork also provides structured coauthoring workflows (documentation, specs, proposals) and a plugin registry for connecting Claude to external services (Slack, GitHub, Linear, Jira, databases). For a biology lab it means: one researcher authors a "literature review" skill, and every student on the project inherits it instantly. The plugin system is open for community contributions.

cloudCommercial

provider · byok

Claude API

The Claude API is Anthropic's developer API: raw programmatic access to the Claude models from your own code. You call client.messages.create() via the anthropic SDK (Python or TypeScript) or the REST endpoint, choosing the model, shaping every prompt, and paying per token across the Haiku, Sonnet, and Opus tiers. It gives you the most control of any Claude product — and the least scaffolding: there is no agent loop or memory unless you build it. For a biology student it is the tool for batch jobs, such as classifying 500 PubMed abstracts by method into structured JSON from your own script.

cloudCommercial

app · byok

Claude Agent SDK

The Claude Agent SDK is the engine behind Claude Code, exposed as a library so you can build your own agents on the same agent loop, built-in tools, and context management. Available in Python and TypeScript, it lets you ship a custom autonomous agent inside your own app — wiring in your own tools and logic while inheriting the planning and memory handling that make Claude Code work. The SDK is free; you pay Claude API tokens for what the agent does. For a lab it is how you would build, say, a protocol-checker agent that reads experiments and flags missing controls.

cloudCommercial

app · free-tier

Claude

Claude is Anthropic's assistant in your browser — no install, just a conversation. It chats through ideas, drafts and edits documents, reads PDFs, CSVs and images you upload, searches the web with sources, and writes and runs small bits of code in live Artifacts. It's the lowest-friction way to use Claude: open a tab and go, with a capable free tier and Pro/Max plans for heavier use. For a biology student it's the everyday thinking partner — explain a paper's statistics, turn rough notes into a methods section, or sanity-check an analysis — though it works one turn at a time rather than running long autonomous jobs.

cloudCommercial

app · free-tier

ChatGPT

ChatGPT is OpenAI's assistant — the broadest feature set of the mainstream chatbots and the most mature enterprise tier. It chats, drafts, and explains; its Advanced Data Analysis runs Python on files you upload, making it genuinely strong for stats and plots; and it searches the web with citations when you turn search on. The catch for scientists is data: consumer and Plus chats train the model by default, with a one-click opt-out in Data Controls, and only Team/Enterprise tiers are contractually no-train. For a biology student it is a capable everyday assistant — just keep patient and unpublished data out of the free tier, and click through every cited link.

cloudCommercial

provider · free-tier

Cerebras

Cerebras is a hardware + inference company that runs open-weight models (Llama 3.x and Llama 4 Scout/Maverick, Qwen3, DeepSeek-R1 distills, gpt-oss) on its wafer-scale CS-3 system — a single chip the size of a dinner plate (the WSE-3) with thousands of times the memory bandwidth of a GPU. The result is the fastest inference measured anywhere: roughly 1,800–2,600 tokens/sec on Llama models and ~3,000 tok/s on gpt-oss-120B, several times faster than GPU-based providers and even faster than Groq on the larger models. For a biology student the payoff is the same as Groq but more extreme: a long answer is finished before you finish reading the question, and a 50-paper batch job in n8n runs in well under a minute. The free tier is unusually generous — 1,000,000 tokens per day with no credit card — and the API is a drop-in for OpenAI's: change the base_url to https://api.cerebras.ai/v1 and your existing code works unchanged.

cloudCommercial

provider · byok

Cartesia (Sonic)

Cartesia's Sonic model is built for one thing: speed. It streams audio back in well under 100ms, which is the difference between a voice agent that feels like a real conversation and one that feels like a phone menu with an awkward pause after every sentence. Sonic-2, released February 2026, covers 15+ languages and is positioned specifically as the latency leader among TTS APIs — competitors like ElevenLabs are excellent but noticeably slower to start speaking. For a student this is less "something you click a button on" and more the engine behind a live voice assistant, an interactive lab tutor, or any app where the AI needs to start talking back almost instantly. It is priced to be cheap at volume, which matters if you are building something that talks a lot rather than narrating a single video.

cloudCommercial

app · free

AnythingLLM

AnythingLLM (by Mintplex Labs) is an all-in-one, private AI workspace: drop in PDFs, documents, or a whole folder and chat with them — with citations — in a familiar ChatGPT-style UI. It is provider-agnostic, so you can run a fully local model (built-in, or via Ollama / LM Studio) or bring your own cloud key (OpenAI, Anthropic, Gemini). When you point it at a local model, nothing leaves your machine. It ships as a one-click desktop app for Mac/Windows/Linux and as a Docker server you can self-host as a shared, always-on team endpoint that exposes an OpenAI-compatible API. Its angle, versus Ollama / LM Studio / Jan, is document-RAG and AI agents bundled in — those tools run models; AnythingLLM turns a model into a private assistant over your own knowledge. Open source under MIT.

cloudSelf-hostedOpen sourceDocker

app · free-tier

Antigravity

Antigravity is Google's agent-first development platform: instead of one assistant in a chat box, it gives AI agents their own space to work. A "Manager Surface" lets you spawn, orchestrate, and watch multiple agents running in parallel across different workspaces, spin up subagents to split a job, and schedule tasks to run in the background on a cron. An "Editor View" is a full AI IDE with tab-completion and inline commands. It ships as a desktop app (Mac/Windows/Linux), a CLI, an SDK, and a managed agent service, and it is model-agnostic — Gemini 3 Pro, Anthropic's Claude Sonnet 4.5, and OpenAI's GPT-OSS all run inside it, with live voice transcription via Gemini Audio. For a researcher it suits bigger, multi-step jobs you want to hand off — refactor a pipeline, build a tool, run an overnight task — and check on later, rather than one quick question.

cloudCommercial

model · paid

Aleph Alpha (Pharia)

Aleph Alpha is a German AI company headquartered in Heidelberg that builds 'specialised large language models for a sovereign Europe', aimed at enterprises and government — including critical use cases. Its current models are the Pharia-1-LLM-7B family, offered for on-premise or private-cloud deployment rather than a self-serve API; there is no public per-token price list (contact sales). It is GDPR-compliant with a German data-protection officer, and emphasises transparency and data sovereignty. For a German SME where data sovereignty is the hard requirement, it is the local, EU-jurisdiction option — the trade-off is enterprise onboarding rather than an instant API key.

cloudCommercial

app · byok

Aider

Aider is a free, open-source command-line tool that turns your terminal into an AI coding session. You point it at one or more files, describe what you want in plain English, and it edits those files and immediately commits the changes to Git with a descriptive message — so every AI change is a clean, reversible commit and nothing is silently overwritten. It supports 100+ programming languages, works with Claude, GPT, Gemini, DeepSeek, and locally-run models, and can map an entire codebase so the AI understands how files relate. Released in 2023 by Paul Gauthier, it has 40,000+ GitHub stars and a large, active community. For biology students: use it to write Python scripts that parse sequencing output, automate repetitive cleaning of CSV exports from lab instruments, or build a small analysis pipeline — with Git tracking every change so you can always roll back.

Self-hostedOpen source

app · paid

Abacus.ai

Abacus.ai (San Francisco, founded 2019 by Bindu Reddy and Arvind Sundararajan) bundles three products behind one login: ChatLLM, a chat surface that routes across GPT, Claude Opus, Gemini, Grok, DeepSeek and 20+ others; Abacus AI Agent ("DeepAgent"), which browses multiple sites, builds and deploys a working web app with a real database, drafts presentations, and connects to Gmail/Google Workspace/Jira; and Claw, an always-on agent that lives inside WhatsApp, Telegram, or Slack, keeps a persistent memory file of your preferences and ongoing work, and runs on a schedule. Everything is metered in a shared credits currency rather than per-product pricing. The company states chats are not used for model training and cites SOC-2 Type II and HIPAA compliance, but publishes no EU-hosting or data-residency option for the consumer tiers.

cloudCommercial