Inference Ownership Readiness Checklist (2026)

Use this to decide whether to keep inference on managed APIs (Bedrock/Azure OpenAI/Vertex) or to own your serving stack (Kubernetes + vLLM/Triton/TensorRT-LLM). This is not a maturity score. It’s a go/no-go list.

1) Product policy (if you can’t answer these, don’t self-host yet)
- Define at least two tiers: Interactive (fast) and Async (throughput).
- Write down one explicit degradation mode per tier (e.g., smaller model, lower max output tokens, tool-use off, or reject fast).
- Decide which users/tenants are protected under overload (paid, internal ops, key accounts).

2) Capacity and scheduling assumptions
- Identify the “hot path” endpoint(s) that dominate volume.
- For each hot endpoint, set hard limits: max input size, max output size, and max concurrency per tenant.
- Decide whether you will consolidate GPUs into one shared pool or keep per-model silos (shared pool is usually the goal).

3) Routing requirements
- Centralize model selection behind a stable internal API. No scattered model IDs in product services.
- Routing inputs you should support on day one: user tier, prompt length bucket, queue depth, and a kill switch for model/tool features.
- Add a fallback plan: if model A is overloaded/unhealthy, where does traffic go (model B, queue, or reject)?

4) Observability (minimum viable truth)
- Track: request rate, queue depth, time-in-queue, time-to-first-token (for streaming), and error rate.
- Track usage in model-native units (tokens or equivalent), not just CPU/GPU utilization.
- Implement per-tenant visibility so one noisy customer can be identified quickly.

5) Reliability controls
- Admission control: enforce concurrency and rate limits per tier and per tenant.
- Define safe failure: what does the user see on overload? Make it explicit.
- Run one controlled load test that forces degradation behavior (not just throughput).

6) Buy vs own decision trigger
Choose managed APIs if:
- Your traffic is spiky and unpredictable, or models change weekly.
- You can’t staff on-call with people who understand GPU scheduling and queueing.
- You don’t need custom routing policies.

Choose owned inference if:
- Inference cost is a primary COGS driver and optimization would matter.
- Latency consistency is a product requirement.
- You need policy control: tiered quality, per-tenant fairness, explicit overload behavior.

If you can complete sections 1–5 for a single endpoint, you’re ready to own that endpoint’s inference path. Expand only after it runs boring for a month.