Try not to lower down your goals. Try to put more efforts!!

Self-hosting AI gives you control. It also gives you more to keep alive at 3 a.m. Now your platform team owns SSO, secrets, upgrades, backups, observability—and model access when an endpoint changes. You didn’t remove vendor risk; you moved it onto your on-call. Still worth it?

“You can just build things”!

A fallback model can keep your API green—and quietly change your product. I log model/version, routing reason, latency, and policy version on every trace. Then I test fallback quality, not just 200s. Portable ≠ equivalent. Would your alerts catch a healthy API returning worse ans

One endpoint for hundreds of models isn’t resilience. It’s a new outage domain. If retries outlive your latency budget, a slow gateway can turn provider rate limits into a platform-wide incident. Test failover, timeouts, and blast radius—not just model access. Have you tested yo

65% fewer tokens can still mean a higher bill. Caveman claims its coding-agent proxy cuts token use by compressing communication. In production, I’d track cost per successful task, retries, latency, and regression rate—not token savings alone. Would you ship it?

AI coding agents don’t need another clever prompt. They need an SRE team. Treat every change like a production deploy: run tests, enforce policy checks, canary it, watch error rates, and keep rollback one command away. Better prompts can’t replace verification. What gate would y

If your agent has repo access but no SLO, you’ve automated uncertainty. My starter SLO: ≥95% task success, 0 policy violations, p95 <2m, <1% rollbacks. Track human escalations too. Model uptime won’t tell you it shipped a bad change. What’s your error budget?

65% fewer tokens can still make your agent more expensive. Retries, tool calls, context growth, failed PRs—that’s the bill. Measure cost per merged, verified change. DeepSeek’s $0.75/1K successful search/fetch requests won’t count rework. What do you track?

Your AI coding agent doesn’t need another prompt. It needs a platform. Version skills like internal packages; test them in sandboxes, scope permissions, trace tool calls, then roll out gradually. An unowned skill is production code with no owner. Would you let it touch prod?

No token budget? Your agent isn't production-designed. I’d enforce runtime caps: 8K tokens/task, 2 retries, 10s deadline. Route cheap work to a smaller model. Track tokens, latency, retries and cost per task—prompt tricks can't stop runaway loops. What's your hard stop?

The next AI platform decision is what NOT to centralize. Keep prompts, drafts and inference local; centralize policy, model access and audit. Sync approved artifacts only. Llmspeed lets you test local latency before buying hardware. What would you keep on-device?