5 LLM deployment patterns every AI engineer should know, from API calls to hybrid
goyalshaliniuk · x · 2026-10-11
A systematic thread walking through five production deployment patterns for LLM apps:
- API-based (OpenAI/Anthropic/Gemini): fastest to ship, but you depend on provider pricing, availability, and policies.
- Self-hosted open-weight models: run vLLM, Hugging Face TGI, or Ollama on your own infra for maximum control — at the cost of managing GPUs, scaling, and security.
- Serverless inference: managed autoscaling with pay-per-use pricing; watch for cold starts and quotas.
- Dedicated endpoints: predictable performance and isolation for steady workloads, but expensive at low utilization.
- Hybrid: route simple tasks to small self-hosted models, complex reasoning to hosted frontier models, sensitive workloads to private infra, with fallbacks on outages — flexible but operationally complex.
Patterns overlap; choose based on latency, traffic, cost, data, reliability, and ops capacity — not model benchmarks alone.
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