To scale inference in Vertex AI, start with right-sizing and isolation. Use separate endpoints for distinct latency/throughput classes (e.g., real-time API vs. internal batch-like calls) and choose machine types and accelerators that match your model’s profile. Set minimum/maximum replica counts per endpoint, enable autoscaling, and define reasonable concurrency per replica based on p95 latency targets. Containerize lightweight pre/post-processing to reduce payload size and CPU burn, and keep models warm by avoiding heavyweight initialization in the request path. Use gRPC for high-throughput scenarios and enable request batching if your model benefits from it (e.g., transformer encoders generating embeddings).
Next, adopt traffic management and observability. Canary new model versions with small traffic slices, monitor p50/p95/p99 latency, error rates, and saturation, then ramp if SLOs hold. Collect detailed traces of preprocessing time, model time, and post-processing time to find bottlenecks. Use Cloud Logging and Monitoring for alerts, track token/compute consumption, and implement circuit breakers and fallbacks (e.g., simplified prompt or cached results) for overload protection. For batch inference, use Vertex AI Batch Prediction or Dataflow-based fan-out to exploit parallelism and keep online endpoints focused on low-latency work.
When your use case includes semantic search or RAG, push retrieval to Milvus so inference servers don’t carry the full load. Generate query embeddings online via a lightweight embedding endpoint, perform ANN search in Milvus with metadata filters, and feed only the top-k context to your generation endpoint. This reduces token budgets and latency while improving answer quality. Cache frequent query embeddings and top-k results with TTLs, precompute embeddings for popular content during off-peak hours, and compress vectors (PQ) if memory is constrained. Finally, run load tests that include the full path—embedding, Milvus search, re-ranking, and generation—so your capacity plan matches real traffic.