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Unified pay-per-generation API for 500+ image, video and audio models like FLUX, Kling and Seedance at low cost.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
Developer platform for fast serverless inference and training of open generative models, billed per token or GPU-second.
No public pricing
Free trial available
- ✦Single API for 500+ image, video and audio models
- ✦Pay-per-generation billing with no subscription
- ✦No charge on failed tasks
- ✦Workflows, agents and studio tools
- ✦MCP and CLI integrations, white-label option
- ✦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
- ✦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
- ✦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
- ✦Serverless per-token inference with OpenAI/Anthropic-compatible APIs
- ✦On-demand dedicated and reserved GPU deployments
- ✦Fine-tuning and reinforcement-learning training pipelines
- ✦Large library of open LLM, vision, image and audio models
- ✦Optimized inference engine for throughput and latency
- →Building apps on top of many generative models via one API
- →Generating images, video and audio at scale
- →Cutting model API costs versus direct providers
- →Deploying white-label AI generation studios
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Serving open models in production apps and agents
- →Fine-tuning models on private data
- →Powering code assistants, chatbots and RAG at scale