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Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
End-to-end evaluation and observability platform for building, testing, and monitoring AI agents and LLM apps.
Free comparison tool for LLM API prices across providers, with a calculator to estimate token costs.
Free trial available
Free trial available
No public pricing
- ✦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
- ✦VRAM/GPU-memory calculator for LLMs
- ✦LLM performance rankings and benchmarks
- ✦Model directory and comparison
- ✦AI/ML courses and learning roadmap
- ✦Calculator API and exportable cost reports
- ✦Engineering blog and guides
- ✦Request logging and LLM observability
- ✦AI gateway with routing and automatic fallbacks
- ✦Caching and rate limiting
- ✦Session, user and custom-property analytics
- ✦Prompts, playground and datasets for testing
- ✦Integrations with OpenAI, Anthropic, Azure and more
- ✦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
- ✦Compare LLM API prices across providers
- ✦Per-million-token input/output rates
- ✦Quality and context-window data
- ✦Token cost calculator
- ✦Sortable, searchable model table
- →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
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets
- →Testing and comparing prompts and models
- →Evaluating and simulating AI agents
- →Monitoring agents in production
- →Running human evaluation pipelines
- →Compare LLM API costs
- →Estimate token spending for a project
- →Pick a cost-effective model