Langfuse - 2026 Pricing, Features, Reviews & Alternatives

Langfuse is an open-source AI engineering platform (MIT-licensed) that covers the full LLM application development lifecycle through tracing, prompt management, evaluations, and analytics. Deployed via Langfuse Cloud (US, EU, and HIPAA-ready regions) or self-hosted on Docker Compose or Kubernetes, including air-gapped and on-premises environments, the platform runs the same codebase across all deployment modes with no vendor lock-in. Used by 2,300+ organizations, from individual developers and early-stage startups to Fortune 500 enterprises, it processes billions of observations per month across any industry building production-grade LLM applications and AI agents.

The observability layer captures hierarchical traces of every LLM call, tool invocation, and retrieval step, with support for multi-turn conversation sessions, user tracking, multi-modal inputs, and agent graph visualization for complex agentic workflows. Traces are ingested asynchronously via native Python and JavaScript SDKs, 100+ framework integrations, OpenTelemetry, or LLM gateways such as LiteLLM, with no impact on application response time. Prompt management provides version control, collaborative UI and API editing, one-click deployments and rollbacks without code changes, A/B testing, GitHub integration, and per-version latency, cost, and evaluation metrics. Evaluation tooling includes LLM-as-a-Judge evaluators, heuristic and code-based evaluators, human annotation queues, dataset-based experiments, and custom numeric, boolean, and categorical scoring.

The metrics and data platform delivers custom dashboards, a metrics API, cost and latency monitoring, automated alerts, and extensive data export options, with token and cost tracking across models including pre-configured pricing for OpenAI, Anthropic, and Google. Security and compliance capabilities include SOC 2 Type II and ISO 27001 certification, GDPR compliance, HIPAA-aligned dedicated cloud regions, RBAC, SSO via multiple identity providers, data masking, data retention controls, and data deletion. An MCP server provides structured access for IDE agents to manage prompts, query traces, and interact with platform data via natural language.