# AI Model Selection Assessment: Frontier API, Open-Weight, or Small Model? > Source: https://netray.co/tools/ai-model-selection-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Score ten decision factors - data sensitivity, task complexity, volume, latency, and internal capability - to see whether a self-hosted open-weight model fits your workload. ## Category on-prem-ai ## Tool **Kind:** assessment **Questions:** #### How sensitive is the data this workload will process? **Help:** Export-controlled technical data and classified information effectively rule out public API inference regardless of vendor assurances. **Options:** ##### ITAR, CUI, or classified - cannot leave our boundary **Score:** 3 ##### Confidential IP, customer designs, or pricing **Score:** 2 ##### Internal business data, no regulatory restriction **Score:** 1 ##### Public or already-published information **Score:** 0 #### How well scoped and repetitive is the task? **Help:** Narrow, repeatable tasks are where small fine-tuned models match or beat frontier models at a fraction of the cost. **Options:** ##### Narrow and highly repetitive, such as field extraction or classification **Score:** 3 ##### Bounded domain with predictable question shapes **Score:** 2 ##### Broad but within one business function **Score:** 1 ##### Open-ended reasoning across many domains **Score:** 0 #### What monthly request volume do you expect at steady state? **Options:** ##### Over 2 million calls per month **Score:** 3 ##### 250,000 to 2 million calls per month **Score:** 2 ##### 25,000 to 250,000 calls per month **Score:** 1 ##### Under 25,000 calls per month **Score:** 0 #### How strict are your latency requirements? **Options:** ##### Sub-second required, embedded in a shop floor or transactional workflow **Score:** 3 ##### Under two seconds for an interactive assistant **Score:** 2 ##### A few seconds is acceptable **Score:** 1 ##### Batch or asynchronous, minutes are fine **Score:** 0 #### Do you have labeled domain data you could fine-tune on? **Help:** A few thousand high-quality domain examples often turn a small open model into the best performer on your specific task. **Options:** ##### Yes, thousands of clean labeled examples from our systems **Score:** 3 ##### Yes, several hundred examples we could clean up **Score:** 2 ##### Raw historical data exists but is unlabeled **Score:** 1 ##### No usable domain data **Score:** 0 #### What in-house capability do you have to run models? **Options:** ##### A platform team already operating GPU infrastructure in production **Score:** 3 ##### Strong infrastructure team, no GPU or ML serving experience yet **Score:** 2 ##### Small IT team stretched across existing systems **Score:** 1 ##### No internal capacity; everything is outsourced **Score:** 0 #### How important is model behavior stability over time? **Help:** Hosted endpoints are updated by the vendor without notice, which can shift outputs in a validated workflow. **Options:** ##### Critical - the workflow is validated and outputs must not drift **Score:** 3 ##### Important - we would need to re-test after any change **Score:** 2 ##### Moderately important **Score:** 1 ##### Not important; we welcome automatic improvements **Score:** 0 #### Does the workload need to run without internet connectivity? **Options:** ##### Yes, fully air-gapped or intermittently connected sites **Score:** 3 ##### Yes for some sites, such as secure facilities or remote plants **Score:** 2 ##### No, but we want the option later **Score:** 1 ##### No, permanent connectivity is guaranteed **Score:** 0 #### How much reasoning depth does the task genuinely require? **Help:** Be honest here. Most enterprise tasks that feel hard are actually retrieval and formatting problems, not reasoning problems. **Options:** ##### Mostly extraction, routing, or summarization of retrieved content **Score:** 3 ##### Multi-step but rule-driven within a known process **Score:** 2 ##### Genuine multi-hop reasoning over conflicting evidence **Score:** 1 ##### Novel problem solving where frontier capability is the differentiator **Score:** 0 #### What is your organization's tolerance for variable vendor cost? **Options:** ##### Low - finance requires a predictable fixed infrastructure line **Score:** 3 ##### Moderate - variable is acceptable with a firm cap **Score:** 2 ##### Comfortable with usage-based spend **Score:** 1 ##### No constraint; operating expense is preferred **Score:** 0 **Bands:** #### Managed frontier API is the right starting point **Min:** 0 **Max:** 32 **Verdict:** Your profile favors a hosted frontier model: low data sensitivity, modest volume, open-ended tasks, or limited internal capability to operate GPU infrastructure. Self-hosting would add cost and operational burden without a corresponding benefit. Start with an API, prove value, and revisit the decision when volume or sensitivity changes. **Recommendations:** - Choose a managed API with a contractual no-training-on-your-data commitment and a documented data residency region. - Instrument token usage per workflow from day one so you can spot the point where economics shift. - Build an abstraction layer over the model call so switching providers later costs days rather than months. - Use prompt caching and a cheaper model tier for simple subtasks before assuming you need the flagship model. - Revisit this assessment when monthly spend passes roughly $10,000 or a regulated use case appears. #### Hybrid: route by task, not by policy **Min:** 33 **Max:** 59 **Verdict:** Your workloads split. Some are sensitive, high-volume, or narrow enough to justify a self-hosted small model, while others still benefit from frontier capability. A hybrid routing architecture almost always beats a single blanket decision in this band, though it requires more engineering discipline to build and maintain. **Recommendations:** - Classify each workload by data sensitivity and reasoning depth, then assign a model tier to each rather than choosing one model. - Pilot a small self-hosted open-weight model on your highest-volume narrow task and measure quality against your golden set. - Build a routing layer with a policy engine so sensitive data never reaches an external endpoint by accident. - Stand up a modest GPU footprint sized for the pilot, not for a speculative future roadmap. - Track cost per workflow separately so the hybrid split can be re-optimized quarterly. #### Self-hosted open-weight models fit your profile **Min:** 60 **Max:** 100 **Verdict:** Data sensitivity, volume, latency, stability, or connectivity requirements all point toward running models inside your own boundary. For workloads like yours a fine-tuned small or mid-size open-weight model frequently matches frontier quality on the specific task while delivering lower latency, predictable cost, and full auditability. **Recommendations:** - Benchmark two or three open-weight candidates on your own golden set rather than on public leaderboards. - Start with the smallest model that clears your quality bar, quantized to INT4 or FP8, before considering larger options. - Design the security boundary first - segmented network, permission-aware retrieval, full audit logging - since it gates production launch. - Use your labeled domain data for fine-tuning or preference tuning to close the remaining quality gap cheaply. - Size initial GPU capacity for the first two workloads with a documented expansion path rather than buying for a three-year plan. ## Content **Intro:** This free AI model selection assessment scores ten decision factors to show whether your workload belongs on a managed frontier API, a self-hosted open-weight model, or a hybrid of both, and it is built for CIOs, AI leads, and enterprise architects at manufacturing and defense organizations. It weighs data sensitivity, task scope, volume, latency, available training data, internal capability, behavioral stability, connectivity, reasoning depth, and cost predictability. The common mistake is treating model choice as a single company-wide policy when the right answer usually differs workload by workload within the same organization. **Sections:** #### How the scoring works **Body:** Each of the ten questions scores zero to three, where three indicates a factor pushing toward self-hosting and zero indicates a factor favoring a managed API. The total converts to a percentage across three bands: 0-32% recommends a frontier API, 33-59% recommends a hybrid routing architecture, and 60-100% recommends self-hosted open-weight models. Data sensitivity carries real weight in practice even though the arithmetic treats questions equally, because a single ITAR or CUI constraint can override every cost argument. If you scored three on data sensitivity but landed in a lower band overall, treat the sensitivity answer as a hard constraint on that specific workload. #### The decision factors that actually drive outcomes **Body:** Across enterprise AI programs a small number of factors reliably determine whether self-hosting pays off. These are the ones worth stress-testing with real data before committing capital, because each is routinely estimated rather than measured. Volume is guessed from a pilot that has not yet hit steady state, task narrowness is assessed by the people who want the interesting model, and internal capability is judged optimistically by a team that has never operated a GPU fleet. Getting honest numbers on these four points changes the recommendation more often than any other analysis, and it costs a few weeks rather than a procurement cycle. **Bullets:** - Data classification is a constraint, not a preference: ITAR and CUI workloads decide themselves. - Task narrowness matters more than volume, because a narrow task lets a small tuned model match frontier quality. - Sustained request volume determines whether fixed infrastructure cost beats per-token pricing, usually above roughly $10,000 monthly. - Internal platform capability is the most commonly underestimated factor; GPU serving is a real operational discipline. #### Interpreting a hybrid result **Body:** Landing in the middle band is not indecision, it is usually the correct architecture. Most manufacturers have a mix: quality-notification triage over controlled technical data belongs on-prem, while an internal policy chatbot over public handbooks does not. The engineering requirement is a routing layer that classifies each request and enforces where it may go, with the policy expressed in code rather than in a document nobody reads. Build the classification and enforcement before the second use case arrives, because retrofitting a data-boundary policy across an established application is far more expensive than designing it in at the start. #### How Netray runs model selection **Body:** Netray evaluates models against your workload rather than against a leaderboard. We start by building a golden question set from real questions your planners, quality engineers, and technicians ask in SyteLine, LN, M3, or ServiceMax, then benchmark frontier and open-weight candidates on accuracy, latency, and cost using that set. For customers with ITAR or CMMC obligations we deploy the selected open-weight model inside the boundary with permission-aware retrieval and full audit logging. The deliverable is a defensible recommendation with measured numbers, not a vendor preference, plus the integration work to put the chosen model into production. **Cta:** Get a benchmarked model recommendation measured against your own questions, your latency targets, and your compliance boundary. **Faq:** #### Can a small open-weight model really match a frontier model? **Answer:** On narrow, well-defined tasks, frequently yes. Extraction, classification, routing, and grounded summarization over retrieved content are largely pattern-matching problems where an 8B or 14B model fine-tuned on a few thousand domain examples often matches or beats a much larger general model, at a fraction of the latency and cost. Where frontier models still lead clearly is open-ended multi-hop reasoning, complex code generation, and tasks requiring broad world knowledge you have no training data for. #### How much volume do I need before self-hosting makes financial sense? **Answer:** As a rough guide, sustained API spend above $10,000 to $20,000 per month starts to justify dedicated infrastructure, assuming you can keep the GPUs meaningfully utilized and you already have platform engineering capability. Below that, the capital, power, and staffing overhead usually outweighs the savings. Regulated workloads break this rule entirely: when data cannot leave your boundary, self-hosting is a compliance requirement and volume is not the deciding variable. #### What if I score high on sensitivity but low on everything else? **Answer:** Then that single workload needs an on-prem or air-gapped deployment even though the rest of your portfolio does not. This is the classic hybrid case for defense and aerospace suppliers. Deploy a modest self-hosted model scoped to the controlled workload, keep everything else on a managed API, and enforce the boundary with a routing policy in code. Do not let one sensitive workload force every other use case onto expensive infrastructure it does not need. ## Search terms AI model selection framework, open weight vs frontier model, small language model enterprise, self-hosted LLM decision, which LLM for enterprise use case, LLM build vs buy assessment, enterprise AI model evaluation criteria --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json