Use flat-rate inference streams or build apps with a persistent coding-agent workspace
camelAI's current platform has two main offerings: camelStream for model inference and camelCode for building software with an agent. The website also retains a separately labeled Legacy data-analytics product. A listing of camelAI should distinguish these offerings rather than describing the whole company only through its older analytics workflow.
camelStream provides an API lane with a flat monthly price instead of a token meter. Additional requests queue when the allowed concurrent capacity is occupied. The documentation describes compatibility with common OpenAI and Anthropic request interfaces, making it possible to connect supported existing clients. The service uses a changing model fleet, so purchasing a stream is not the same as reserving a particular model version indefinitely.
camelCode works in a workspace where files, databases and deployed applications can persist between sessions. Users describe what they want to build and the agent works on the implementation, with publishing to a live URL included in the product description. Plans differ in workspaces, deployments, storage and scheduled jobs. A generated application still needs functional, security and data-handling checks before it is used by other people.
The pricing page lists a free camelCode tier and paid Starter and Pro plans. Model credits, bring-your-own-key options and usage after included credits are separate considerations from camelStream's flat-rate lane. Users should check which product is actually providing inference in their setup and which limits apply, rather than assuming every camelAI activity is unlimited under one subscription.
The camelStream fleet documentation states that requests and responses may be retained and used for model training by the service or its inference providers. That condition is significant for confidential code, customer records and other sensitive inputs. Before connecting a tool, review the relevant product terms and privacy policy, then test a small non-sensitive workload for compatibility, queue behavior and output quality. The right configuration depends on both technical needs and acceptable data use.
