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LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
Privacy-focused CAPTCHA and bot/fraud-detection service, a drop-in reCAPTCHA alternative for websites and apps.
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- ✦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
- ✦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
- ✦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
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦AI bot detection
- ✦Transaction fraud protection
- ✦Account-takeover (ATO) defense
- ✦Pull-based SMS MFA
- ✦Private Learning ML risk models
- ✦Two-line reCAPTCHA migration
- ✦Hundreds of integrations
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →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
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Blocking bots and spam signups
- →Preventing account takeover
- →Reducing transaction and payment fraud
- →Stopping credential stuffing