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Open-source Python framework, born at Netflix, for building, scaling, and deploying real-world ML, AI, and data science workflows.
End-to-end evaluation and observability platform for building, testing, and monitoring AI agents and LLM apps.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
Free comparison tool for LLM API prices across providers, with a calculator to estimate token costs.
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
No public pricing
Free trial available
No public pricing
Free trial available
- ✦Plain-Python workflow orchestration
- ✦Automatic versioning and experiment tracking
- ✦Scale-out compute with GPUs and parallel instances
- ✦One-command deployment to production
- ✦Runs on AWS, Azure, GCP, or Kubernetes
- ✦Event-based triggering of workflows
- ✦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 and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦Compare LLM API prices across providers
- ✦Per-million-token input/output rates
- ✦Quality and context-window data
- ✦Token cost calculator
- ✦Sortable, searchable model table
- ✦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
- →Developing and debugging ML pipelines locally
- →Scaling model training to cloud GPUs
- →Deploying experiments to production unchanged
- →Building reactive, event-driven data systems
- →Testing and comparing prompts and models
- →Evaluating and simulating AI agents
- →Monitoring agents in production
- →Running human evaluation pipelines
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Compare LLM API costs
- →Estimate token spending for a project
- →Pick a cost-effective model
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages