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Majority of enterprises currently have AIagent pilots running. Only few of those pilots have made it to production.
That gap is not a model problem. It's not adata problem. In most cases, it's an infrastructure problem - specifically, theinfrastructure that worked fine for a controlled pilot completely breaks downwhen it needs to handle real traffic, real compliance requirements, real SLAs,and real consequences when it goes down.
The model that passed every benchmark in yourpilot environment will behave differently in production. The infrastructurethat's been hiding those differences will surface them all at once.
Pilots are designed to prove a concept.Production is designed to run one. The infrastructure requirements arefundamentally different, and pretending otherwise is how teams end up doingexpensive rewrites after the fact.
In a pilot: you have a fixed team, a fixeddataset, predictable traffic, and someone watching it full-time. Incidents getcaught quickly. Load is light. Latency variance doesn't matter much. Compliancesign-off is deferred to "before we go live."
In production: traffic is unpredictable.Users are real. Your SLA is real. Compliance is non-negotiable. Nobody iswatching it full-time. And the team that built it has moved on to the nextproject.
Before any AI workload moves from pilot toproduction, these nine things need to be in place. Not "planned." Not"on the roadmap." In place.
Best-effort infrastructure is fine for apilot. For production, you need a documented SLA with a number attached. Oursis 99.9999% infrastructure uptime that's built on engineered redundancy, not apromise. Know what your provider's SLA actually says before you go live.
Shared GPU infrastructure works until itdoesn't. Noisy neighbors, spot instance reclamation, and multi-tenant networkcontention are acceptable in a pilot. They're not acceptable when a customer iswaiting on a response. Know your tenancy model and understand what failurelooks like when you share resources.
If your workload touches regulated data, yourcompliance documentation needs to be signed before go-live - not during it.That means BAAs for HIPAA, SOC 2 reports for security reviews, data residencyconfirmation in writing. "We're working on it" is not a complianceposture.
Traditional application monitoring (latency,error rate, uptime) is necessary but not sufficient for AI workloads. You alsoneed visibility into token usage, context window utilization, and model outputquality over time. Hallucination rates that were acceptable in testing becomeproduction incidents when they're customer-facing.
When something breaks in production at 2am,you need a human. Not a ticket queue. Not a documentation portal. A human whoknows your infrastructure and can act on it. This sounds obvious until you'rethe person at 2am with a broken training run and a support ticket thatauto-responds "we'll get back to you within 24 hours."
Your traffic estimate will be wrong. Everyestimate is. Provision for 3x your expected peak and know what the scale pathlooks like when you need more. If your provider requires a 2-week lead time foradditional GPU capacity, that's a production risk you should know about beforeyou go live.
You need to be able to roll back a modelupdate within minutes - not hours. This means versioned model artifacts,blue-green deployment capability, and infrastructure that supports rapidswitching. AI models that degrade silently in production are a real failuremode.
Inference costs scale with traffic in waysthat training costs don't. By the time you're in production and the bills arecoming in, it's too late to optimize the architecture. Build cost monitoringand alerting before go-live so you're not reading bad news for the first timeon an invoice.
If the only person who can debug yourproduction AI environment is the person who built it, you have a single pointof failure. Document the failure modes, the escalation paths, and the recoveryprocedures before go-live. Then hand it to someone who wasn't involved in thebuild and see if they can follow it.
The teams that get stuck between pilot andproduction almost always have the same two gaps: compliance documentation thatisn't finalized, and support infrastructure that was fine for testing butdoesn't meet the standard for production incidents. Both are solvable, but theytake longer to fix than people expect especially compliance, which requiresvendor coordination, legal review, and procurement sign-off.
The teams that ship production-ready AIquickly have usually dealt with infrastructure concerns as a first-classrequirement from day one of the pilot, not as a checklist to complete beforelaunch.
EveryGPU One deployment comes with a documented 99.9999% uptime SLA, single-tenantarchitecture, SOC 2 Type II and HIPAA compliance documentation, 24/7 humansupport, and transparent all-in pricing. Start with a 7-day trial atstninc.com/gpu-one-trial.
