FlexAI

FlexAI is a coding tool. FlexAI offers managed inference on 30+ open models through one OpenAI-compatible API key, with a growth path from serverless calls to agent governance, dedicated GPU endpoints and private cloud deployment.

FlexAI is a managed inference platform that serves more than 30 open-weight models through a single OpenAI-compatible API. The catalog spans text generation, coding, reasoning, vision, embeddings, speech-to-text, text-to-speech and image generation, with families from Qwen, Gemma, Llama, DeepSeek, GLM, Mistral and OpenAI's open GPT-OSS models. Existing code written for the OpenAI SDK can be pointed at the FlexAI endpoint and run unchanged, and the vendor states that leaving again is a configuration change. The product is organised as a path rather than a single service. Token Factory covers serverless model calls priced by usage. AgentOS is the layer for agent loops, where teams bring skills, tools, scoped memory, evals and approvals, and FlexAI runs them across models with routing, governance and audit trails. Dedicated Endpoints move the same API onto reserved GPUs, and AI Factory deploys the stack in a VPC, on-premises or air-gapped. Compute runs on FlexAI-operated NVIDIA and AMD fleets, with an uptime SLA of up to 99.9 percent, and the company lists SOC 2 Type II and GDPR compliance. In practice, a developer can try image generation and chat completions in an in-browser demo without an account, then sign up for an API key. Billing is pay as you go with no commitment beyond usage, though a card is required to create a key. Text models are charged per token, while media models are charged per image, per minute of audio or per generated clip, and dedicated capacity is billed per GPU-hour. FlexAI suits agent-focused product teams, startups that expect to outgrow serverless, and regulated organisations that need private deployment. Compared with model API providers such as Fireworks or hyperscaler services such as AWS Bedrock, its pitch is a single account that scales from serverless calls to private infrastructure. It is limited to open-weight models, so teams that depend on proprietary frontier models will still need another provider alongside it.