To deploy GLM-5 in production, the most practical approach is to decide early whether you will use a hosted API or self-host the model weights. If you use Z.ai’s API, production deployment is “standard web service engineering”: build a thin gateway that handles auth, request shaping, retries, timeouts, streaming, and logging, then forwards requests to the model endpoint. Z.ai provides a quick-start and SDK-friendly examples (including OpenAI-style SDK compatibility paths) that make this route fast to ship for most teams. If you self-host, you are operating an inference service: you must download weights, choose an inference engine, provision GPUs, and expose an HTTP interface with rate limits and observability. The right choice depends on your latency/cost goals and whether your prompts and retrieved documents must remain inside your own network boundary.
For self-hosting, start from the official GLM-5 repo guidance: it explicitly calls out serving support in vLLM, SGLang, and xLLM, and provides a simple deployment path (including Docker and nightly builds for vLLM). The typical production shape is: (1) store weights in an internal artifact location, (2) run an inference server container per model variant, (3) put a gateway in front for authentication and request-level policy, and (4) scale horizontally by adding replicas and distributing load. You’ll want GPU sharding (tensor parallelism at minimum) because the checkpoint is large, and you should treat context length as a product decision because KV-cache memory grows quickly at long contexts. In practice, you’ll tune: max context, max new tokens, batch size, and concurrency, then verify p95 latency under realistic traffic. You should also implement canary releases: pin an exact model revision and runtime version, deploy to a small slice of traffic, run regression prompts, and only then roll out broadly.
Most production GLM-5 deployments become significantly more reliable once you add retrieval and grounding instead of relying on the model “remembering” your data. A common architecture is RAG: store your docs, FAQs, and code snippets as embeddings in a vector database such as Milvus or managed Zilliz Cloud retrieve top-k relevant chunks per request (with metadata filters like product/version), and inject those chunks into the GLM-5 prompt. This reduces prompt bloat, improves factual accuracy, and gives you debuggability because you can log the retrieved chunk IDs alongside the model output. If you’re deploying an assistant on a developer website, this also lets you keep answers aligned with the latest docs without retraining the model—re-indexing content in Milvus or managed Zilliz Cloud is usually enough.