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Unified pay-per-use API serving 400+ multimodal AI models (image, video, audio, 3D, LLM) through one OpenAI-compatible key.
Unified pay-per-generation API for 500+ image, video and audio models like FLUX, Kling and Seedance at low cost.
Low-cost inference cloud with developer APIs to run open ML models and on-demand GPUs, billed pay-per-use.
General AI agent that executes multi-step tasks end to end — research, slides, design, browsing — instead of only answering questions.
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦400+ AI models via one unified API
- ✦Multimodal coverage: image, video, audio, 3D, LLM
- ✦On-demand, pay-per-use pricing
- ✦Day-0 access to new state-of-the-art models
- ✦OpenAI-compatible single key
- ✦SOC 2 and HIPAA compliance, 99.99% uptime
- ✦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
- ✦Hosted inference for many open models
- ✦Simple REST/OpenAI-compatible API
- ✦Pay-per-token or per-time billing
- ✦On-demand GPU rental
- ✦Broad catalog (Llama, DeepSeek, Qwen, Flux, etc.)
- ✦DeepStart and DeepCluster tooling
- ✦Autonomous multi-step task execution
- ✦Website and app building
- ✦AI slides, design and image generation
- ✦Manus browser operator
- ✦Wide Research mode
- ✦Cross-platform web, desktop and mobile apps
- ✦ChatLLM access to multiple top AI models
- ✦AI agents and automation
- ✦No-code full-stack app creation
- ✦Enterprise generative AI platform
- ✦Structured ML model building
- ✦Optimization and forecasting
- →Integrate video and image generation
- →Access many LLMs through one API
- →Build multimodal AI applications
- →Batch generate and prototype cheaply
- →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
- →Serving open-source models via API
- →Building AI apps cost-efficiently
- →Renting GPUs for inference or training
- →Scaling inference up and down on demand
- →Automate end-to-end digital tasks
- →Produce websites and presentations
- →Conduct broad research
- →Hand off browser tasks to an agent
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents