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Open-source LLMOps platform uniting prompt management, evaluation and observability for teams shipping reliable LLM apps.
Open-source, OpenTelemetry-native platform for LLM observability, tracing, evaluation and prompt management.
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
No public pricing
No public pricing
Free trial available
- ✦Prompt management as a single source of truth
- ✦Playground for prompt experimentation
- ✦Evaluation to measure changes before production
- ✦Observability and tracing for debugging
- ✦Collaboration across technical and non-technical roles
- ✦Open-source and self-hostable
- ✦OpenTelemetry-native distributed tracing
- ✦Token usage and cost tracking
- ✦LLM evaluations (online/offline)
- ✦Prompt management and versioning
- ✦GPU and vector-DB monitoring
- ✦60+ LLM/framework integrations
- ✦Self-hostable via Docker; export to Grafana/Datadog
- ✦Agent trace capture and conversation intelligence
- ✦Semantic and exact-text search across all traces
- ✦Automatic issue discovery with Slack/email/webhook alerts
- ✦OpenTelemetry-compatible SDK with no lock-in
- ✦Automated evals and golden dataset generation
- ✦Failure-mode clustering and MCP server integration
- ✦Unified access to 100+ LLMs in OpenAI format
- ✦Cost/spend tracking per key, user and team
- ✦Budgets and rate limiting
- ✦Automatic provider fallbacks and retries
- ✦Virtual keys and team management
- ✦Logging and observability integrations
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- →Version and manage prompts centrally
- →Benchmark and evaluate LLM outputs
- →Debug and trace production LLM issues
- →Collaborate across a team on LLM apps
- →Trace and debug LLM applications
- →Monitor AI cost and performance
- →Evaluate prompts and models
- →Add observability without code changes
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy