The collaboration layer for AI engineering teams

PromptLayer is an AI engineering platform that combines a prompt registry and visual prompt CMS with LLM evaluations, observability, and agent tracing. Engineering teams use it to version, test, deploy, and monitor prompts, while domain experts can improve prompt behavior without editing application code.

Track every request against the exact prompt version that produced it, then compare quality, usage, cost, and latency by version. PromptLayer connects production logs and traces to prompt versions and evaluation scores, so teams can measure whether a prompt change improved behavior instead of relying on manual spot checks.

Keep prompts in a central registry with immutable version history, diffs, release labels, and rollback. PromptLayer lets teams promote a tested version to development or production, A/B test versions, and restore a known-good prompt without changing application code or waiting for a full redeploy.

Run representative datasets through each prompt or model change and score outputs with deterministic checks, human review, and LLM-as-a-judge graders. PromptLayer supports scheduled and CI-triggered regression evaluations, side-by-side model comparisons, and historical backtests before a new prompt version reaches production.

Log token usage, model cost, and response latency for every LLM request, then group those metrics by prompt version, model, provider, and workflow. PromptLayer provides searchable request logs and agent traces so teams can identify expensive or slow steps and verify the impact of an optimization.

Treat prompt versions like deployable software artifacts: retrieve them through an SDK or REST API, run regression evaluations when they change, and promote release labels only after checks pass. PromptLayer provides versioned prompts, programmatic APIs, webhooks, and evaluation workflows for controlled CI/CD releases.