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Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
AI gateway and observability suite for governing and optimizing LLM apps; strong dev-tool traffic.
Platform to test, evaluate and observe LLM and voice AI agents, with prompt management and red-teaming for production.
Open-source Python framework, born at Netflix, for building, scaling, and deploying real-world ML, AI, and data science workflows.
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
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No public pricing
- ✦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
- ✦AI Gateway for reliable LLM routing
- ✦Prompt Engineering for collaborative prompt management
- ✦Guardrails for enforcing reliable LLM behavior
- ✦Observability Suite for monitoring costs, quality, and latency
- ✦MCP Client for building AI agents with real-world tool access
- ✦Scenario-based agent testing
- ✦LLM evaluation and quality scoring
- ✦Observability for cost and latency
- ✦Prompt management with GitHub sync
- ✦Voice AI simulation
- ✦LLM red-teaming and governance
- ✦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
- ✦Unified proxy and protocol conversion across 100+ LLMs
- ✦Model-level fallback and routing
- ✦Semantic and exact-match AI caching
- ✦Token tracking and quota controls
- ✦Content-safety and data-protection filtering
- ✦MCP service hosting and plugin marketplace
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Monitor costs, quality, and latency of AI applications.
- →Route to 250+ LLMs reliably with a single endpoint.
- →Streamline and scale prompt engineering.
- →Enforce reliable LLM behavior with guardrails.
- →Build agents with access to real-world tools.
- →Catch agent issues before production
- →Evaluate and monitor LLM quality
- →Test voice AI agents at scale
- →Developing and debugging ML pipelines locally
- →Scaling model training to cloud GPUs
- →Deploying experiments to production unchanged
- →Building reactive, event-driven data systems
- →Centralizing access to multiple LLM providers
- →Building and governing AI agent/MCP services
- →Controlling token spend across teams
- →Adding caching and safety to LLM calls