Langfuse is an excellent open-source tracing and eval platform. Peekr is the layer underneath — in-process compliance and hallucination scoring that enforces 17 regulatory packs inside your own process, before LLM output reaches your app.
No credit card · MIT license · 17 compliance packs on Pro
The architecture
Langfuse
Peekr — in-process, framework-agnostic
Side by side
| Capability | Langfuse | Peekr |
|---|---|---|
| Instrumentation model | @observe decorators / wrap_openai() | Class-level patch, no decorators |
| In-process compliance enforcement | eval infra only, no built-in packs | 17 packs (HIPAA, FDCPA, FINRA…) on Pro |
| Claim-level hallucination scoring | —LLM-as-judge (custom setup) | built-in, every sentence scored |
| Prompt management | versioned prompts from Langfuse hub | —prompt studio (basic) |
| Human annotation queues | Yes | not a labeling platform |
| LLM-as-judge / dataset evals | rich eval dataset management | —claim scoring; no eval dataset mgmt |
| MIT / self-host | MIT, Docker self-host | MIT SDK; VPC self-host on Enterprise |
| Data stays in your process | —traces exported to Langfuse backend | compliance packs enforced in-process |
The compliance gap
Langfuse lets you run LLM-as-judge evals — but enforcement happens post-hoc, not in the call path. Peekr blocks violations and writes a tamper-evident audit log before output reaches your application. 17 regulatory packs on the $99/mo Pro plan.
Where Langfuse wins
Peekr is not a prompt-management platform or a human-annotation pipeline.
You need prompt management
Langfuse lets you version, test, and serve prompts from a central hub — teams iterate on prompts without code deploys. Peekr has a basic prompt studio but is not prompt-management-first.
You want annotation queues and human evals
Langfuse's annotation queue lets reviewers label LLM outputs, build golden datasets, and run regression evals across prompt versions. Peekr is not a human-in-the-loop labeling platform.
You want one self-hosted backend
Langfuse consolidates traces, evals, and prompt management in a single Docker-deployable backend you control. If everything in one self-hosted service is the goal, Langfuse is the more complete solution.
FAQ
For in-process regulatory compliance and claim-level hallucination detection, yes. Peekr runs inside your own process — it patches OpenAI, Anthropic, Gemini, and Bedrock at the class level and enforces 17 compliance packs (HIPAA, FDCPA, FINRA, GDPR…) before LLM output reaches your application code. Langfuse is a strong open-source tracing and eval platform, but does not ship pre-built compliance packs as enforced guardrails. If your priority is annotation queues, prompt management, and LLM eval datasets, Langfuse is the richer fit.
No. Peekr calls peekr.instrument() once before any imports and patches the SDK client class — every OpenAI() or Anthropic() instance is automatically traced. No @observe decorators, no wrapper functions. Langfuse's Python SDK tracing works via @observe decorators or explicit context managers, and wrap_openai() in the JS SDK.
Langfuse does not ship pre-built regulatory compliance packs. It provides evaluation infrastructure where you can build custom LLM-as-judge evaluators — but enforcement on every call, blocking of violations, and tamper-evident audit logging are not built in. Peekr ships 17 regulatory packs (HIPAA, FDCPA, FINRA, GDPR, EU AI Act, and more) enforced in-process on the $99/mo Pro plan: violations are blocked and audit-logged inside your own process.
Auto-instruments OpenAI, Anthropic, Gemini, Bedrock, and more. 17 in-process compliance packs. Claim-level hallucination scoring. MIT-licensed. Free up to 10k spans/month.