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Anthropic's AI assistant for writing, coding, and analysis across web, mobile, and desktop, plus a developer API.
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
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.
Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.
No public pricing
No public pricing
- ✦Conversational writing and editing
- ✦Code generation and debugging (Claude Code)
- ✦Data analysis and visualization
- ✦Web search plus memory across chats
- ✦Connectors and remote MCP integrations
- ✦Extended thinking for complex tasks
- ✦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
- ✦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
- ✦OpenTelemetry-native distributed tracing across 100+ LLMs and frameworks
- ✦Online evaluation via LLM-as-a-judge or code
- ✦Offline experiments and regression detection
- ✦Annotation queues for expert review
- ✦Alerts and drift detection
- ✦Prompt management, CLI and docs MCP server
- →Drafting and refining written content
- →Building and debugging software
- →Analyzing datasets for insights
- →Research and learning support
- →Team and enterprise automation
- →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
- →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
- →Debugging multi-agent systems
- →Monitoring live agent quality at scale
- →Catching regressions before release
- →Human review of edge cases
- →Aligning automated evaluators with domain experts