Loading tool details…
Langfuse delivers Fortune 50-proven LLM observability with unmatched flexibility: full open-source self-hosting, unlimited users on paid plans, comprehensive compliance features, and enterprise-grade capabilities starting at $29/month - the strongest value for production AI teams.
Open-source LLM engineering platform that captures traces, runs evaluations, and helps teams analyze cost, latency, and quality across production AI apps.
Open-source LLM engineering platform that captures traces, runs evaluations, and helps teams analyze cost, latency, and quality across production AI apps.
Langfuse is the open-source de facto standard for LLM observability. It captures every LLM call, tool invocation, retrieval step, and agent decision as a trace, then makes those traces searchable, replayable, evaluatable, and comparable across model or prompt versions. If you have ever tried to debug an agent that misbehaves in production, Langfuse is the tool you wished you had installed on day one. Core capabilities include: distributed tracing with automatic capture from OpenAI, Anthropic, LangChain, LlamaIndex, LiteLLM, Vercel AI SDK, and dozens of other SDKs; a prompt management system with versioning and A/B testing; a dataset and evaluation system for both LLM-as-judge and human labeling; sessions, users, and cost analytics; and a scoring API for wiring in custom quality metrics. Everything is open source under an MIT-style license, and Langfuse is one of the most-starred LLM devtools on GitHub. You can self-host the full stack on Docker or Kubernetes, or use Langfuse Cloud (EU and US regions) for a managed experience. Because it does not require any framework lock-in — every SDK is optional decorators around your existing code — Langfuse tends to be the observability layer teams adopt first and keep even as they swap model providers, frameworks, and vector stores.
Langfuse stands as the definitive open-source LLM observability platform, combining enterprise-grade capabilities with unmatched deployment flexibility. The ClickHouse acquisition (2026) has accelerated development while preserving the open-source foundation that Fortune 50 companies trust. Unlimited users on paid plans, comprehensive compliance features, and full self-hosting capability make it the clear choice for production AI teams seeking observability without vendor lock-in.
Captures complete execution trees of complex AI workflows including multi-agent conversations, tool calling sequences, and RAG pipelines. Each trace shows parent-child relationships between all operations, enabling deep debugging of agent interactions and workflow bottlenecks with full context preservation.
Use Case:
Debug a customer support agent that gives incorrect answers by tracing the exact knowledge retrieval → context filtering → prompt construction → model generation → response formatting chain to identify the failure point.
Enterprise-grade prompt lifecycle management with version control, production trace linking, A/B testing capabilities, and protected deployment labels. Prompts are managed in the UI and linked to real production performance, enabling data-driven optimization without code deployment.
Use Case:
Test a new system prompt for a financial advisor agent by deploying two prompt versions simultaneously and comparing success rates, compliance scores, and customer satisfaction metrics in real-time dashboards.
Comprehensive quality assurance combining automated LLM-as-judge evaluators, categorical scoring, human annotation queues with inline comments anchored to specific text, and experiment management. Build regression datasets from production data for continuous model validation.
Use Case:
Implement systematic quality control for a medical AI assistant by running automated safety evaluations on every response and routing concerning outputs to medical professionals for detailed review with inline annotation tools.
Complete security package including SOC2 Type II, ISO27001, HIPAA compliance with BAA, enterprise SSO (Okta, Azure AD), SCIM API, audit logs, RBAC, and data retention management. Self-hosted option provides air-gapped deployment with full feature parity.
Use Case:
Deploy LLM observability for a healthcare organization requiring HIPAA compliance by using self-hosted Langfuse with encrypted data storage, access controls, and complete audit trails for regulatory reporting.
Granular cost tracking across multiple LLM providers with support for tiered pricing models (context-dependent rates for Claude, Gemini). Provides per-model, per-user, per-feature cost analysis with trend monitoring and budget alerting.
Use Case:
Optimize a multi-model AI application by analyzing cost-per-quality metrics across OpenAI GPT-4, Claude Sonnet, and local models to determine the optimal model routing strategy for different types of user queries.
Complete on-premises deployment using the same infrastructure as Langfuse Cloud (PostgreSQL, ClickHouse, Redis, S3). Includes Docker Compose for development, Kubernetes Helm charts, and Terraform modules for AWS/Azure/GCP with unlimited traces and users.
Use Case:
Deploy enterprise observability for a financial services firm requiring complete data residency by self-hosting Langfuse on internal infrastructure while maintaining access to all prompt management, evaluation, and security features.
Free
$0
$59/mo
$499/mo
Custom
Ready to get started with Langfuse?
View Pricing Options →Langfuse works with these platforms and services:
We believe in transparent reviews. Here's what Langfuse doesn't handle well:
In 2026 Langfuse expanded LLM-as-judge templates for reasoning and agentic tool use, added first-class OpenTelemetry ingest to sit alongside its native SDKs, shipped multi-region residency (EU/US) with per-project pinning, and grew MCP server support so agents can query their own trace history programmatically.
LLM Observability
LangSmith helps developers trace, evaluate, test, and monitor applications built with language models and agent workflows.
AI Monitoring
Open-source LLM observability and gateway platform offering one-line integration, request logging, prompt management, evals, and a multi-provider proxy.
AI Evaluation
Braintrust review 2026: unified evals, prompt workbench, observability, and AI proxy used by OpenAI, Stripe, Notion — features, pricing, pros, cons.
AI Observability
Arize Phoenix is an open-source AI observability and evaluation platform for tracing, testing, and improving LLM applications and agents.
Pricing and features change often. If anything on this Langfuse page is wrong or missing, tell us and include a link to the source. We review every correction.
Learn to build AI agents with no-code tools like Lindy AI, low-code frameworks like CrewAI, or advanced systems with LangGraph. Real examples, cost breakdowns, and 30-day success plan included.
AI agents cost $0.02-$5+ per task, but most businesses overpay by 300% due to hidden waste. Here's what 1,000+ companies actually spend, where money gets wasted, and the proven tactics that cut costs without hurting quality.
The 10 trends reshaping the AI agent tooling landscape in 2026 — from MCP adoption to memory-native architectures, voice agents, and the cost optimization wave. With real tools leading each trend and current market data.
A comprehensive guide to multi-agent AI systems: what they are, why they outperform single agents, the five core architecture patterns, and how to choose the right framework. Practical advice for builders.