FedML

TensorOpera AI is a platform for building and running AI agents together with the models behind them. It spans three layers: a studio for creating agents from prompts, workflows, APIs or full frameworks; a model layer with serving, routing, training and fine tuning; and a GPU cloud supplying the compute underneath. One model API key reaches hosted commercial models alongside open weight models the company deploys itself. It is aimed at teams that want agent development and model infrastructure in one place rather than stitched together. Agents can be extended with MCP servers for external tools and data, grounded in custom knowledge bases, and deployed to persistent agent servers with hosting and monitoring. On the model side the platform offers serverless and dedicated endpoints, a router that directs each request to a suitable model, distributed training with experiment tracking across clouds, and federated learning across decentralized GPUs, edge servers and smartphones. A consumer assistant and a mobile AI product sit on top of the same stack. Platform pricing is published as Starter at $0 per month plus compute, Advanced at $199 per month plus compute, and Enterprise on request. Serverless inference is metered per million tokens by model size, starting at $0.10 for language, code and voice models up to 3.5 billion parameters and reaching $1.20 for the largest mixture of experts size listed, with separate rates for embedding, image, video and audio models. Dedicated endpoints are billed by the hour, including $0.44 for an RTX 3090, $1.65 for an A100 80GB and $3.00 for an H100 80GB, with higher rates listed for configurations using faster interconnects. The company built its reputation on FedML, its federated learning platform, which now sits inside the broader TensorOpera product line.