Prompt Management API

A stable endpoint for every AI feature in your product. Update prompts without touching code.

The problem with prompts as strings

Right now, your prompts probably live as strings somewhere in your codebase — env vars, config files, or hardcoded in the function that calls the LLM. That works until it doesn't.

No versioning, no rollback. A wording change goes straight to production. If it breaks something, you're debugging blind.
Every iteration needs a deploy. Want to test a different phrasing? Pull request, review, merge, release. For a sentence change.
No visibility into what anything costs. You can see your total OpenAI bill. You can't see which feature is responsible for 80% of it.

The result: teams get scared to touch prompts. Features stagnate. The AI behaviour you shipped six months ago is still running unchanged — not because it's good, but because no one wants to risk it.

What a prompt management API actually gives you

Endprompt puts each AI behaviour behind a stable API endpoint. Your application calls the endpoint. The prompt lives in Endprompt — versioned, testable, and updatable without any code changes.

The contract between your app and the LLM never changes. What changes is the prompt content, safely, on your terms.

Every endpoint gets:

A full version history — every prompt change recorded, with the ability to roll back to any previous version instantly
An OpenAPI spec — your application integrates once against a stable schema; it never needs to know what's inside the prompt
Cost and latency tracking per endpoint — see exactly what each AI feature costs, broken out from the rest of your LLM spend
A test-before-promote workflow — validate a new prompt version against real inputs before it goes live
Domain expert access — product managers and subject matter experts can update AI behaviour directly, without waiting on an engineer

Key benefits

Integrate once, iterate forever. Your codebase makes one API call. Everything after that — prompt changes, model swaps, A/B tests — happens in Endprompt without touching your application.

Cost tracking by feature, not just by model. Most teams know their monthly OpenAI bill. Almost none know which product feature is responsible for what share of it. Endprompt tracks cost per endpoint, so you can prioritise optimisation where it actually matters.

Up to 67% cost reduction with smart caching. Endprompt caches semantically similar requests automatically. No code changes required. Repeated or near-identical queries hit the cache instead of the model.

Instant rollback. If a prompt update causes a regression, you're one click from the previous version. No deploy, no hotfix, no incident.

Unblock your non-engineering team. When prompts are infrastructure, domain experts — support leads, content strategists, product managers — can own AI behaviour directly. Engineers get their time back.

How it works

Define an endpoint — give your AI behaviour a name, write the initial prompt, and publish it in Endprompt
Integrate once — replace your inline LLM call with a single API call to your Endprompt endpoint
Iterate without deploys — update the prompt in Endprompt, test it against real inputs, promote it when ready
Monitor in production — every request is logged with cost, latency, and output; replay any request to debug regressions
Roll back instantly — if something goes wrong, restore the previous version in one click

Start managing prompts properly

14-day free trial. No card required. Bring your own API keys — you pay your LLM provider directly, no token markup. See pricing →

Start free trial → Read the quickstart docs →