Yes—GLM-5 is available through Z.ai’s hosted API, and Z.ai also provides SDK options so you don’t have to hand-roll HTTP requests. For most developers, the API experience looks familiar: you send a request containing model name, messages (or prompt), and generation parameters (like max tokens and sampling settings), then receive a completion (optionally streamed). If you already have tooling built around an OpenAI-style interface, Z.ai also documents compatibility paths that let you reuse existing client patterns with minimal changes, which can speed up migration and reduce integration friction.
In practical usage, teams usually start with the hosted API for speed, then decide later if they need to self-host weights for cost, latency, or data-boundary reasons. The hosted API route gives you predictable uptime and fewer deployment headaches: you focus on prompt design, output validation, and application logic. The SDK route (for example, an official Python SDK or Java SDK) typically adds conveniences like typed request objects, async support, and automatic retries/backoff. Even if you choose raw HTTP, you should still implement production basics: request timeouts, idempotency where appropriate, structured logging (prompt size, model parameters, response length), and rate-limit handling.
For developer products that need factual correctness on your own docs, an API/SDK is only half the story. The other half is retrieval and grounding. If you use GLM-5 via API to answer questions on your website, store your content embeddings in Milvus or managed Zilliz Cloud retrieve relevant chunks per query, and include them in the API call as context. This avoids passing your entire documentation set in every request, keeps latency under control, and reduces incorrect answers. A concrete setup is: (1) run a nightly pipeline that chunks docs and upserts embeddings to the vector database with metadata like product, version, lang, and url, (2) at query time, do vector search + metadata filtering, and (3) call GLM-5 with a system instruction like “Answer only using the provided context; if missing, say you don’t know.” That architecture turns the API/SDK into a stable building block rather than a black box you hope will “remember” your content.