Self-Hosted AI Pitfalls

Teams transitioning from hosted API to self-hosted AI hit recurring pitfalls. Some surface immediately (cold start, capacity); others emerge weeks later (eval drift, cost creep). Awareness helps avoid them.

Pitfalls

• Underestimated ops time: "just run vLLM" vs the reality of monitoring, deploys, incident response
• Cold-start latency: 30-90s vLLM startup; users notice during deploys without blue-green
• No eval baseline before migration: can't prove quality didn't regress vs hosted
• No monitoring before going live: blind to production behaviour
• Frontier-quality regression on hard cases: open-weight covers 90%; the hard 10% needs hosted-API fallback
• Capacity surprise: load test passed; production surfaced patterns the test missed
• Cost creep: caching disabled by accident; KV cache pressure; over-provisioned headroom
• Residency gaps: discovered mid-enterprise-sale that some component still calls US-region service

Avoidance

• Budget realistic ops time (~0.5-1 FTE pro-rated)
• Blue-green deploys hide cold start
• Build eval harness BEFORE migrating; baseline on hosted API
• Observability stack live before traffic cutover
• Always include hosted-API fallback in routing
• Soak test pre-launch (24-72 hours sustained synthetic traffic)
• Verify caching enabled in production; track hit rates
• Audit data flows for residency early in design

Verdict

Self-hosted AI pitfalls are mostly preventable with honest planning. Budget ops time realistically; build observability + eval before traffic; always have fallback; soak test; track caching. The teams that transition smoothly do these consistently; the teams that struggle skip them.