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Google's agentic development platform and IDE for building software with autonomous, Gemini-powered coding agents.
ByteDance's Coze (Kouzi): an all-in-one AI office assistant for writing, slides, sheets, design, podcasts and images.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
AI gateway and observability suite for governing and optimizing LLM apps; strong dev-tool traffic.
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Agent-first IDE experience
- ✦Autonomous planning and code execution
- ✦Integrated editor, terminal and browser control
- ✦Powered by Google's Gemini models
- ✦High-level developer supervision
- ✦AI writing
- ✦AI presentation/PPT generation
- ✦AI spreadsheets and tables
- ✦AI design
- ✦AI podcast generation
- ✦AI image generation
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦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
- ✦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
- →Building apps with AI agents
- →Automating multi-step coding tasks
- →Prototyping and iterating on software
- →Assisting developers on complex work
- →Drafting documents
- →Building presentations
- →Generating spreadsheets
- →Creating designs and images
- →Producing podcasts
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →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.
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets