Self-Hosted LLM to Managed Inference Blueprint
Shift self-hosted GPU LLM serving to a managed inference API to remove operational toil while a provider-agnostic gateway prevents lock-in. Evals prove quality parity and token budgets plus caching keep per-request cost in check.
What and Why
Self-hosting LLMs on GPUs gives control but is expensive and operationally heavy: capacity planning, driver/kernel maintenance, scaling for spiky demand, and on-call for GPU nodes. Managed inference APIs remove that burden and scale elastically. The trade-offs are vendor lock-in, per-token cost, and data governance, which this blueprint manages with an abstraction layer and clear policies.
The goal is to shift undifferentiated GPU operations to a provider while keeping the option to switch models and controlling spend.
Phases
Assessment. Catalogue current models, request volume, latency needs, and any fine-tunes. Classify data sensitivity to set data-residency and retention requirements.
Provider selection. Evaluate managed inference options against quality, latency, cost, region/residency, and fine-tuning support. Run an eval suite (LLM evals) on representative prompts to compare quality objectively.
Abstraction layer. Put a provider-agnostic gateway in front of all LLM calls so models and providers can change without touching application code. Centralize prompts, guardrails, retries, and observability here. Adopt MCP for tool/context access where applicable.
Migration. Route traffic from the self-hosted endpoint to the managed API behind a flag, comparing quality and latency. Migrate fine-tunes or replace with RAG where appropriate.
Cost governance. Add per-team token budgets, caching for repeated prompts, prompt-size limits, and dashboards. Decommission GPU infrastructure once stable.
Key Risks and Mitigations
- Vendor lock-in: a provider-specific integration is hard to leave. Use the abstraction layer and keep an eval harness so switching is a config change.
- Data residency: prompts may contain regulated data. Choose compliant regions, disable training-on-data where required, and redact PII before sending.
- Cost overrun: per-token pricing scales with usage. Enforce budgets, cache, cap context size, and monitor cost per request.
Recommended Tooling
A provider-agnostic LLM gateway/router, an evaluation harness for quality comparison, Redis for prompt/response caching, secret management (Vault) for API keys, LLM observability and cost dashboards, and guardrails for safety.
Success Metrics
Track cost per request versus self-hosted total cost of ownership, p95 latency, operational toil (GPU on-call hours removed), and quality parity from evals.
Prerequisites
An eval dataset, classified data-sensitivity requirements, secure key management, and stakeholder agreement on acceptable providers and regions.