AI & Machine Learning · sub-niche
Prompt version control.
Git-for-prompts that non-engineers can use — a small focused tool that every AI team eventually wants.
Why now
Prompts live in three places (code, prompt files, vendor consoles) and drift constantly. The tool that gives PMs a diff view wins the workflow.
What the signal looks like
Repos with a CLI + dashboard combo, integration test directories for popular frameworks, and a 'compare two prompt versions' demo in the README.
Public examples
We name publicprojects + categories only — never founders we track inside the paid product. The buyer’s edge stays inside the product.
- PromptLayer-style hosted prompt registries
- Vellum-shaped prompt management platforms
- Open-source Git-for-prompts CLIs
What this displaces
A Notion page maintained by one tired prompt engineer.
Our build-vs-invest call
Two-product company: free CLI + paid hosted dashboard with eval integration. The moat is in the eval link — versions without evals are useless. Watch for repos that add eval integration in the same commit as multi-user access.
Common questions about this niche
- Is this a feature of the observability tool?
- Possibly — and the observability tools will try. But the non-engineer surface is a different product.
- Who's the buyer?
- AI PMs and applied AI leads, $200-500 per seat per month.
- What's the wedge?
- Slack + GitHub integration. The PM lives in Slack and reviews diffs in GitHub. Match that.
More inside AI & Machine Learning
- LLM eval harnesses — Reproducible eval suites that an AI-native team can drop into CI and trust by lunchtime.
- Agent orchestration frameworks — The 'LangChain for X' slot is still wide open — pick a vertical, ship the runtime, win the wedge.
- Retrieval-augmented search libraries — RAG-as-a-library — bring-your-own embedding, bring-your-own vector store, win on developer ergonomics.
- Fine-tuning tools for non-ML teams — Take fine-tuning out of the notebook. Product teams want to point at JSONL and get a deployable adapter.