Backend Services

Shipping an AI feature needs more than a model, and this category covers the server-side pieces around it: hosted inference endpoints, vector storage and retrieval, embedding pipelines, document parsing and chunking, job queues for long tasks, file storage, caching, rate limiting, authentication and per-customer usage metering. Most are consumed as an API with client libraries, so a small team gets a working backend without provisioning anything itself.

The buyers are product teams adding an AI feature to an existing application and startups that would rather not run infrastructure yet. Comparison points that matter: cold-start behaviour and tail latency under load, which regions are available, data residency and retention terms, whether inputs are used for training, throughput and context limits, language coverage of the SDKs, egress and storage costs, and whether there is a self-hosted path if the terms change.

Load-test with realistic payload sizes rather than a sample document, and read the quota page before the pricing page. The usual traps are quiet rate limits, costs that scale with retries, and proprietary abstractions that make leaving expensive — keep your own copy of embeddings and source files. Billing is typically a free tier, then per request or token, plus storage and seats.