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AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
Platform to test, evaluate and observe LLM and voice AI agents, with prompt management and red-teaming for production.
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
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- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦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
- ✦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
- ✦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
- ✦End-to-end agentic and ML observability
- ✦Real-time guardrails (hallucination, PII, jailbreak)
- ✦Continuous evaluations and custom judges
- ✦Root-cause analysis and decision lineage
- ✦AI governance, risk, and compliance controls
- ✦Flexible SaaS, VPC, or on-prem deployment
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets
- →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
- →Catch agent issues before production
- →Evaluate and monitor LLM quality
- →Test voice AI agents at scale
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI