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Free comparison tool for LLM API prices across providers, with a calculator to estimate token costs.
End-to-end evaluation and observability platform for building, testing, and monitoring AI agents and LLM apps.
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
No public pricing
Free trial available
No public pricing
- ✦Compare LLM API prices across providers
- ✦Per-million-token input/output rates
- ✦Quality and context-window data
- ✦Token cost calculator
- ✦Sortable, searchable model table
- ✦Prompt IDE, versioning, and deployment
- ✦Agent simulation and evaluation
- ✦Production tracing and observability
- ✦Pre-built and custom evaluators
- ✦Human-in-the-loop evaluation
- ✦Bifrost LLM gateway
- ✦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
- ✦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
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- →Compare LLM API costs
- →Estimate token spending for a project
- →Pick a cost-effective model
- →Testing and comparing prompts and models
- →Evaluating and simulating AI agents
- →Monitoring agents in production
- →Running human evaluation pipelines
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues
- →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
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy