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Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
AI gateway and observability suite for governing and optimizing LLM apps; strong dev-tool traffic.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
Fast, low-cost AI inference provider running LLMs on custom LPU chips via GroqCloud's pay-as-you-go API.
Free trial available
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
- ✦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
- ✦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
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦LPU custom inference hardware
- ✦GroqCloud tokens-as-a-service API
- ✦High-speed, low-latency inference
- ✦Pay-as-you-go token pricing
- ✦Free API key to start
- ✦Broad open-model support
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →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.
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Running LLM inference at high speed
- →Cutting inference costs at scale
- →Powering low-latency AI chat apps
- →Serving models via a hosted API