TECHNOLOGY PROFILE · TECH-0225
Ray Serve
Scalable model-serving library on Ray.
Reference link only — not reviewed this edition. This profile preserves the prepared catalog’s original descriptions. Advantages depend on the workload; comparisons are editorial, not benchmark results.
Where it has an edge
Composes Python deployments and scales them with distributed application components.
Best-fit problems
Pipelines combining preprocessing, multiple models and application logic.
Tradeoffs & cautions
Ray cluster management is more work than a small standalone API.
What to test before choosing
Replay representative concurrency and context lengths; measure p50/p95 latency, throughput, memory and error rate.
Runtime & hardware
Usually server CPU/GPU
Access & licensing
Open-source core; deployment costs separate
Execution
Local / self-hosted
Lifecycle
Not independently audited
Comparison basis
Editorial problem-fit judgement; no head-to-head benchmark performed
Connected concepts
Alternatives to investigate
BentoML · NVIDIA Triton Inference Server · FastAPI