LLMOPS PART 14: PRACTICAL LLM SERVING AND VLLM IN PRODUCTION
A new LLMOps chapter explains how to serve models in production and walks through practical trade-offs, including vLLM-based deployments. Part 14 of Avi Chawla...
A new LLMOps chapter explains how to serve models in production and walks through practical trade-offs, including vLLM-based deployments.
Part 14 of Avi Chawla’s LLMOps course covers making language models available as a service, comparing API providers with self-hosted inference, deployment topologies, and hands-on serving with vLLM Concepts of LLM Serving. It focuses on how concurrency, cold starts, and scaling decisions shape latency, reliability, and cost.
If you are still wiring data to train models, this complements a build-it-yourself pipeline walkthrough from The New Stack Build it yourself: A data pipeline that trains a real model. Together, they frame training versus serving concerns and where to invest engineering effort.
Serving choices, not just model speed, determine user latency, reliability, and total cost.
A clear framework de-risks the jump from prototypes to production LLM features.
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terminal
Run a side-by-side load test: self-hosted vLLM vs a managed API using real prompts; measure P95 latency, cold starts, throughput, and cost per request.
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Stress your scaling strategy with bursty traffic; verify queueing limits, startup times, failure modes, and cooldowns meet your SLA.
Legacy codebase integration strategies...
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Introduce a gateway traffic split to compare provider and self-hosted paths with feature flags and fast rollback.
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Tighten data handling before self-hosting: redact logs, set retention windows, and isolate PII to match compliance.
Fresh architecture paradigms...
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Hide the model behind a thin service interface so you can swap providers without app changes.
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Design for multi-tenancy from day one: per-tenant quotas, rate limits, usage metering, and tracing.