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Chinese AI lab DeepSeek offering free chat apps and low-cost API access to its frontier V-series and R-series reasoning models.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
Open-source Python framework, born at Netflix, for building, scaling, and deploying real-world ML, AI, and data science workflows.
No public pricing
No public pricing
Free trial available
No public pricing
- ✦Free DeepSeek chat (web and app)
- ✦Open API platform
- ✦V-series and R-series reasoning models
- ✦DeepSeek-V4 with long context and stronger agent ability
- ✦OpenAI/Anthropic-compatible API
- ✦Extensive published model lineup
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦Open-Source AI Gateway
- ✦Multi-LLM Management & Cost Optimization
- ✦Efficient and Secure LLMs Invocation
- ✦Unified API Signature for LLMs
- ✦Load Balancer for seamless switching between LLMs
- ✦Fine-Grained Traffic Control for LLMs
- ✦LLM Quota Management
- ✦Real-time LLM Traffic Monitoring
- ✦Caching Strategies for AI in Production
- ✦Flexible Prompt Management
- ✦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
- ✦Plain-Python workflow orchestration
- ✦Automatic versioning and experiment tracking
- ✦Scale-out compute with GPUs and parallel instances
- ✦One-command deployment to production
- ✦Runs on AWS, Azure, GCP, or Kubernetes
- ✦Event-based triggering of workflows
- →Free AI chat and assistance
- →Building apps via API
- →Reasoning and coding tasks
- →Low-cost LLM inference
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Building API portals for secure sharing of internal APIs with partners.
- →Tracking API usage and driving API monetization.
- →Managing and securing API access in compliance with enterprise policies.
- →Connecting to multiple AI large models simultaneously.
- →Optimizing LLM costs and improving efficiency.
- →Protecting against LLM attacks and data leaks.
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Developing and debugging ML pipelines locally
- →Scaling model training to cloud GPUs
- →Deploying experiments to production unchanged
- →Building reactive, event-driven data systems