# On-Prem AI Deployment Checklist: 30 Points Before Go-Live > Source: https://netray.co/tools/on-prem-ai-deployment-checklist > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer A 30-point pre-deployment checklist covering use cases, hardware, security, model operations, and rollout for self-hosted enterprise LLMs. ## Category on-prem-ai ## Tool **Kind:** checklist **Groups:** #### Use Case and Data Readiness **Items:** **Text:** Top 2-3 use cases scoped with named business owners and success metrics **Critical:** true Target document repositories identified, deduplicated, and access-mapped **Text:** Data classification reviewed: CUI, export-controlled, and PII flagged for the AI pipeline **Critical:** true Gold evaluation set of 30-50 real task examples written with pass criteria Baseline time-on-task measured for target workflows to enable ROI proof Legal review of model license and acceptable-use terms completed #### Hardware and Infrastructure **Items:** **Text:** GPU capacity sized against measured or modeled peak concurrency, not vendor spec sheets **Critical:** true Rack power, circuits, and cooling verified for sustained GPU load Storage provisioned for model weights, vector indexes, and log retention Network path from user segments to inference endpoints tested for latency and firewall rules Hardware support contracts and spare-parts strategy in place Capacity headroom of at least one growth increment planned beyond launch load #### Security and Compliance **Items:** **Text:** AI stack included in security accreditation or change-approval process with sign-off **Critical:** true **Text:** SSO and role-based access control integrated; no shared or local accounts **Critical:** true Prompt and response logging policy defined, including retention and who can read logs **Text:** Retrieval layer enforces document-level permissions so users cannot query data they cannot open **Critical:** true Egress rules verified: inference stack makes no unapproved external calls Incident response plan updated to cover AI-specific events like data leakage in outputs #### Model Serving and MLOps **Items:** Serving stack (vLLM/TGI) deployed with continuous batching and tested failover **Text:** Quantization level validated against the gold set, not assumed harmless **Critical:** true Load test passed at 2x expected peak concurrency with latency SLOs met Monitoring live for GPU utilization, queue depth, latency, and error rates Offline update pipeline for model weights and containers documented and rehearsed Rollback procedure tested: previous model version restorable within one hour #### Rollout and Adoption **Items:** Pilot group of 20-30 users identified with a named champion per department Training materials and prompt guidance written for actual job workflows **Text:** Acceptable-use policy published and acknowledged by users before access **Critical:** true Feedback channel and triage cadence established for quality issues Usage and value KPIs dashboarded for the executive sponsor Expansion criteria defined: what evidence unlocks the next user wave **Scoring Note:** Score yourself by group. Any unchecked critical item is a launch blocker regardless of overall score - critical items cover the failure modes that cause security incidents or dead-on-arrival deployments. 25+ items checked with all criticals complete means you are genuinely ready for production go-live. ## Content **Intro:** This checklist covers the 30 items that separate successful on-prem AI deployments from stalled ones, distilled from Netray's implementations for aerospace, defense, and discrete manufacturers. Self-hosting an LLM is not the hard part - a capable engineer can serve a model in an afternoon. Production readiness is the hard part: permission-aware retrieval, accreditation sign-off, tested rollback, and an adoption plan. Work through the five groups before go-live, and treat every critical item as a blocking gate, because each one marks a failure mode we have seen sink real deployments. **Sections:** #### How the checklist is organized **Body:** The five groups follow deployment dependency order. Use case and data readiness comes first because infrastructure decisions inherit from workload decisions - sizing hardware before scoping use cases is how GPUs end up idle. Hardware and infrastructure covers the physical layer most teams over-focus on. Security and compliance is where regulated manufacturers stall longest, so its items start early even though they finish late. Model serving and MLOps turns a working demo into an operable service. Rollout and adoption exists because the most common on-prem AI failure is not technical - it is a perfectly functional system nobody uses after week two. #### Why the critical items are critical **Body:** Roughly a quarter of the items are flagged critical because they represent unrecoverable or high-cost failures rather than routine gaps. They share a theme: each is cheap to handle before launch and expensive or reputation-damaging to retrofit after. **Bullets:** - Permission-aware retrieval: a RAG system that leaks documents across access boundaries is a security incident, not a bug - Accreditation sign-off: deploying before security approval can invalidate an ATO and freeze the whole program - Quantization validation: silent quality loss discovered by users destroys trust faster than any outage - Sizing from measured concurrency: spec-sheet sizing produces either idle capex or launch-day queue collapse #### How to use your score **Body:** Run the checklist twice: once at project kickoff to build the workplan, and again as a go/no-go gate two weeks before launch. At kickoff, expect to check fewer than a third of items - the unchecked list is your project plan, already sequenced by group. At the gate, apply the scoring note strictly: all critical items complete plus 25 or more total is a launch; missing criticals mean a delay, and history says the delay is cheaper than the incident. Groups scoring lowest tell you where to add specialist help - security-heavy gaps and MLOps gaps call for different reinforcements. #### Where Netray fits **Body:** Netray delivers on-prem AI deployments against exactly this checklist - it is a condensed version of our internal delivery gates. We handle the items teams most often lack in-house: permission-aware RAG architecture, accreditation-ready documentation for CMMC and ITAR environments, hardened vLLM serving with tested rollback, and adoption programs with measured baselines. Engagements can cover the full checklist or reinforce the specific groups where your self-assessment scored lowest, and every deployment ends with your team trained to operate the stack independently. **Cta:** Work the checklist, then bring your lowest-scoring groups to Netray's on-prem AI team to close them before go-live. **Faq:** #### How long does it take to get from zero checked items to launch-ready? **Answer:** For a mid-market manufacturer with existing IT maturity, 10-16 weeks is typical: two to three weeks on use-case scoping and data readiness, four to six on infrastructure and security in parallel, three to four on serving and MLOps, and two on pilot rollout. Air-gapped or accreditation-bound environments add time on the security group - sometimes substantially - which is why those items should start the first week, not the last. #### Which checklist group do teams most commonly underestimate? **Answer:** Rollout and adoption, by a wide margin. Teams treat go-live as the finish line when it is the starting line: without trained champions, workflow-specific guidance, and a feedback loop, usage peaks in week one and decays to a handful of enthusiasts by month two. The second most-skipped area is the gold evaluation set - without it, every model refresh and quantization decision becomes an argument of opinions instead of a measurement. #### Can I launch a pilot without completing every item? **Answer:** Yes - a scoped pilot with 20-30 users can run before items like expansion criteria or full capacity headroom are complete. What a pilot cannot skip are the critical security items: permission-aware retrieval, access control, accreditation sign-off, and an acknowledged acceptable-use policy apply from the first user, because a pilot that leaks a controlled document is precisely as serious an incident as a production system doing it. ## Search terms on-prem ai deployment checklist, self-hosted llm production checklist, enterprise llm deployment steps, on premise ai go-live checklist, llm security checklist, ai infrastructure readiness checklist --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json