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Open-source LLMOps platform uniting prompt management, evaluation and observability for teams shipping reliable LLM apps.
Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
Free comparison tool for LLM API prices across providers, with a calculator to estimate token costs.
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
- ✦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 across 100+ LLMs and frameworks
- ✦Online evaluation via LLM-as-a-judge or code
- ✦Offline experiments and regression detection
- ✦Annotation queues for expert review
- ✦Alerts and drift detection
- ✦Prompt management, CLI and docs MCP server
- ✦End-to-end agentic and ML observability
- ✦Real-time guardrails (hallucination, PII, jailbreak)
- ✦Continuous evaluations and custom judges
- ✦Root-cause analysis and decision lineage
- ✦AI governance, risk, and compliance controls
- ✦Flexible SaaS, VPC, or on-prem deployment
- ✦Compare LLM API prices across providers
- ✦Per-million-token input/output rates
- ✦Quality and context-window data
- ✦Token cost calculator
- ✦Sortable, searchable model table
- ✦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
- →Debugging multi-agent systems
- →Monitoring live agent quality at scale
- →Catching regressions before release
- →Human review of edge cases
- →Aligning automated evaluators with domain experts
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →Compare LLM API costs
- →Estimate token spending for a project
- →Pick a cost-effective model
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy