Agent Infrastructure
Langfuse
Open source LLM observability for traces, evals, and cost-per-trace breakdowns down to the individual span. MIT-licensed and self-hostable, so the data never leaves your infrastructure.
The AIE Angle
Why Langfuse made the cut
Langfuse is what you reach for when you need engineer-grade visibility into what your LLM stack is actually doing — every trace, every span, every token, every dollar. It's open source under the MIT license and built to be self-hosted, which is the reason it shows up in conversations where the SaaS observability tools get vetoed. If your traces contain customer data, source code, contracts, or anything else your legal team won't let you ship to a third-party vendor, Langfuse lets you keep the whole pipeline inside your own infrastructure. The feature surface covers the parts of AI ops that matter once you're past the prototype stage: traces with parent-child span trees, prompt management with version control, evaluations (LLM-as-judge, human review, programmatic), and cost analytics broken down to the individual call. The team raised a Series A in March 2026 and has been shipping at the pace of a project where the maintainers actually live in the code. Right pick when you need engineer-grade tracing and want to keep prompts and outputs on your own infrastructure. Wrong pick when nobody on your team wants to run another service and a SaaS observability tool would get adopted faster.
Independently tested. No pay-to-play.
The AI Toolbox is curated by practitioners who use these tools in real business workflows. We don't accept payment for placement or favorable reviews.
5 Ways To Use It
Langfuse for business
- 1
Trace every step of a multi-agent workflow — model calls, tool calls, retries — to debug why a chain failed or went over budget.
- 2
Run prompt evaluations (LLM-as-judge, programmatic, human review) before promoting a new prompt to production.
- 3
Track cost per trace, per user, per feature flag — find the customer or prompt template driving 80% of your AI bill.
- 4
Manage prompts as versioned artifacts with rollback and A/B testing, instead of strings buried in code.
- 5
Self-host the whole observability stack inside your own VPC when traces contain regulated or sensitive data.
Common Questions
Langfuse FAQ
The questions business professionals most often ask about Langfuse.
What is Langfuse?+
Langfuse is an open source LLM engineering platform — traces, prompt management, evaluations, and cost analytics for applications built on large language models. It's MIT-licensed and can run as a managed cloud service or self-hosted entirely inside your own infrastructure.
How is Langfuse different from Helicone?+
Helicone is primarily an AI gateway — it sits in the request path and enforces policy. Langfuse is primarily observability — it sits next to your application code via SDK calls and captures rich traces. Many teams run both: Helicone for gateway-level cost caps and rate limits, Langfuse for engineer-grade trace and eval workflows. The two integrate cleanly.
Can I self-host Langfuse?+
Yes. Langfuse ships a fully featured self-hosted version under the MIT license — Docker, Kubernetes, or your platform of choice. The self-hosted version has feature parity with Langfuse Cloud for the core engineering features, which is the whole reason regulated industries pick it.
What SDKs and frameworks does Langfuse integrate with?+
Native SDKs for Python and TypeScript/JavaScript, plus first-class integrations with LangChain, LlamaIndex, Vercel AI SDK, OpenAI SDK, Anthropic SDK, LiteLLM, Haystack, and the OpenTelemetry standard. If you're using any mainstream LLM framework, Langfuse can capture it without rewrites.
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