--- title: "Local Models vs APIs: EU Break-Even Calculator" canonical: https://wavect.io/blog/local-models-vs-apis-break-even-eu-2026/ language: en description: "When do local LLMs beat APIs for EU companies? A break-even calculator for GPU utilization, API spend, data residency, ops cost, evals, and workload shape." image: "https://wavect.io/img/blog/headers/header_local-models-vs-apis-break-even-eu-2026.png" --- [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 13 min read · 08 Jul 2026 [**Next**](/blog/llm-cost-calculator-2026/) # When Local Models Beat APIs: A Break-Even Calculator for EU Companies TL;DR Local LLMs beat APIs only when the full equation wins, not when GPU hourly price looks cheap. API cost is cost per successful task; self-host cost is GPU hours plus redundancy, storage, networking, observability, engineering ops, eval upkeep and idle capacity divided by successful tasks. Utilization research shows the same H100 can range from very cheap to wildly expensive per million output tokens depending on concurrency and offered load, so utilization is the first variable. Hosted APIs win for low, bursty or fast-changing workloads; local wins for high steady volume, strict EU data residency, or expensive frontier-API replacement when an open-weight model passes your eval. The calculator asks for token volume, tokens per task, concurrency, utilization, latency SLO, engineer cost, eval pass rate and API alternative. Do not self-host before caching, batching, routing and model right-sizing are measured. Figures are a July 2026 snapshot; re-check live provider prices. Related service: [AI Enablement](/services/ai-enablement/) Local LLMs beat APIs only when the workload, governance, and operating model line up. A cheap GPU hour is not a business case. The business case is cost per successful task after utilization, concurrency, redundancy, engineer time, eval upkeep, and EU data residency are counted. If the open-weight model does not pass your eval, the calculator stops. If it passes but the GPU is idle most of the day, the calculator probably stops too. This article is the self-hosting companion to our [LLM cost calculator 2026](/blog/llm-cost-calculator-2026/). That post compares API bills by task. This one compares API tasks against local inference. The hard lesson from 2026 utilization research is simple: the same H100 can look extremely cheap or painfully expensive per million output tokens depending on offered load and concurrency. A recent arXiv paper measured a spread from $0.21 to $15.25 per million output tokens on identical H100 hardware when utilization changed, and argues that utilization-naive calculators understate cost by 1 / U. Treat that as the center of the model, not a footnote. This calculator owns server and private-cloud economics. If the workload can run on the customer's device, our [Transformers.js browser AI production guide](/blog/transformers-js-browser-ai-guide/) covers client-side privacy, model delivery, WebGPU and WASM fallbacks, and the cost transferred to product engineering and user hardware. ## The short answer Use an API when traffic is low, bursty, fast-changing, or dependent on frontier reasoning. Use a local model when you have high and steady volume, a model that passes your eval, a team that can operate the stack, and either strict EU data-residency requirements or an API alternative expensive enough to leave real margin. Hosted EU-region APIs and managed open-weight endpoints sit in the middle: often the best answer before full self-hosting. | Situation | Usually wins | Why | | --- | --- | --- | | Early internal assistant, a few thousand tasks per month | API | GPU idle time and ops work swamp token savings. | | Nightly extraction over millions of documents | Local or batch API | Steady load and async latency let you fill capacity or buy batch discounts. | | Regulated EU workload with sensitive data | EU API, private deployment, or local | Governance can override pure cost, but still compare managed EU endpoints first. | | Customer-facing SaaS chatbot with spiky traffic | API or hybrid | Elasticity, safety updates, and burst handling matter more than GPU sticker price. | | High-volume classification, routing, enrichment, or summarization | Local candidate | Small open-weight models can pass evals and saturate cheaper hardware. | ## The calculator Start with the API baseline. The right comparison is not provider invoice versus GPU invoice. It is API cost per successful task versus local cost per successful task. | Line | Formula | Notes | | --- | --- | --- | | API monthly cost | tasks_per_month * api_cost_per_successful_task | Use the task calculator, not a single call estimate. | | API task cost | sum(input + cached_input + output + tools + retries + failure_rework) | Apply prompt caching and batch discounts only where they are actually eligible. | | Self-host monthly cost | gpu_hours + redundancy + storage + networking + observability + engineering_ops + eval_upkeep | Do not hide people cost outside the spreadsheet. | | Self-host task cost | self_host_monthly / successful_tasks_per_month + variable_task_cost | Divide by passed tasks, not requests. | | Break-even tasks | self_host_fixed_monthly / (api_task_cost - self_host_variable_task_cost) | If the denominator is small or negative, APIs win. | The local inference line needs one extra formula: Effective local cost per 1M tokens = ((gpu_hourly_rate + infra_hourly + ops_hourly) / (tokens_per_second * 3600 * measured_utilization)) * 1,000,000. Measured utilization is the trapdoor. If your spreadsheet assumes 80% utilization but production gives you 12%, your cost is not a little wrong. It is wrong by a factor of roughly 6.7 before redundancy, on-call, and eval upkeep enter the room. Before pricing a local candidate, confirm that it fits the target machine at the intended context and quantization. Our [llmfit hardware-fit guide](/blog/llmfit-local-llm-hardware-guide/) shows how to create that shortlist and where its speed estimate still needs a real benchmark. If the candidate is DeepSeek V4 Flash 0731, our [single AI PC deployment review](/blog/deepseek-v4-flash-0731-local-ai-pc/) shows which 128 GB and 192 GB assumptions belong in that calculation. ## Inputs to collect before deciding Do not start with model weights or GPU quotes. Start with traces from the task you want to replace. | Input | What to measure | Why it changes the decision | | --- | --- | --- | | Task volume | Successful business tasks per day and per month | Low volume makes fixed local cost painful. | | Tokens per task | Uncached input, cached input, output, tool calls, verifier calls | Output-heavy work can make APIs expensive; stable input can make caching powerful. | | Concurrency | Requests per second, p95 latency target, in-flight requests | Concurrency drives GPU saturation and queueing behavior. | | Batchable share | Percentage that can wait minutes or hours | Batch APIs can cut provider cost before self-hosting is worth it. | | Model pass rate | Eval pass rate of the local candidate versus the API baseline | A cheaper failing model is just a support cost with better marketing. | | Ops capacity | Engineer hours for serving, updates, security, monitoring, incident response | Local systems have a salary line even when the GPU is cheap. | | Data constraints | GDPR, customer contracts, sector rules, data residency, audit needs | Governance can force local or private deployment even when API cost is lower. | ## Why utilization beats GPU price GPU quotes are seductive because they are simple. Utilization is annoying because it is an emergent property of your traffic. A single interactive assistant may have long idle periods and sudden bursts. A nightly extraction job can run at a controlled queue depth for hours. The second workload can make a local model look rational; the first often cannot. The concurrency-aware view uses Little's Law: in-flight requests are driven by request rate and latency. That matters because LLM serving is not a static token factory. Prefill, decode, KV-cache memory, batching, queueing, and output length all shape throughput. The 2026 cost-estimation paper is useful because it points at the hidden assumption in most calculators: users type a utilization value into the sheet, but the workload determines whether that value is plausible. Serving stack choices matter too. The 2025 vLLM versus HuggingFace TGI study found vLLM up to 24x higher throughput in high-concurrency workloads, while TGI showed lower tail latency in interactive moderate-concurrency scenarios. That does not mean vLLM always wins. It means your calculator needs a benchmark row for the exact workload, model, quantization, context length, and latency SLO. ## Do not compare against the wrong API A local model does not need to beat the most expensive frontier model in the provider catalogue unless that is the model your task truly needs. It needs to beat the cheapest acceptable managed path after caching, batching, routing, and model right-sizing. - OpenAI pricing separates input, cached input, and output prices, so stable prefixes can materially change the baseline before self-hosting enters the picture. - Gemini pricing includes context caching and batch pricing on paid tiers, and the page explicitly separates whether prompts are used to improve products by tier. - Amazon Bedrock says select foundation models are available for batch inference at 50% lower price than on-demand inference. - Azure OpenAI adds another comparison point for EU companies because data zones, provisioned throughput, reservations, and enterprise procurement can matter as much as token price. Only after those levers are priced should self-hosting enter the model. For the optimization ladder, see [how to reduce LLM token costs in 2026](/blog/reduce-llm-token-costs-2026/). For model selection, see our [open-weight LLM comparison](/blog/open-weight-llm-comparison-2026/). ## A worked break-even example Imagine an EU SaaS company running a document-enrichment pipeline. The current API path costs 0.018 dollars per successful document after caching, batch discounts, retries, and verifier calls. The local candidate passes evals at the same quality bar. The self-hosting stack costs 9,800 dollars per month all-in: GPU lease, hot spare capacity, storage, logging, monitoring, networking, engineering time, and eval maintenance. Variable local cost is 0.003 dollars per document. | Metric | Value | Interpretation | | --- | --- | --- | | API task cost | $0.018 | Measured cost per successful document. | | Local variable task cost | $0.003 | Per-document overhead after the fixed stack exists. | | Monthly local fixed cost | $9,800 | Infrastructure plus people and eval upkeep. | | Savings per task | $0.015 | API task cost minus local variable task cost. | | Break-even | 653,334 successful documents per month | 9,800 / 0.015, before risk buffer. | Add a risk buffer. If the eval pass rate drops, if utilization is lower than the benchmark, if failover requires a second hot GPU, or if demand is seasonal, the break-even moves right. If the workload is async and can saturate the GPU overnight, it moves left. If the API baseline is a cheap open-weight hosted endpoint rather than a frontier model, it may move so far right that self-hosting is not worth the distraction. ## EU-specific factors For EU companies, the local-versus-API question is rarely only about price. GDPR, data processing agreements, customer procurement, sector rules, and auditability can decide the shape of the architecture. But governance still has multiple options. | Need | API-friendly path | Local path | | --- | --- | --- | | EU data residency | Use EU-region or EU data-zone provider options where contractually adequate. | Run inference on EU cloud or your own infrastructure with controlled logs and storage. | | No training on prompts | Use enterprise/API terms and verify provider policy by product tier. | Keep prompts, outputs, logs, and embeddings inside your environment. | | Customer audit | Document subprocessors, retention, access controls, and model provider terms. | Document GPU provider, image provenance, model license, serving logs, and access controls. | | Sector-sensitive data | Prefer private endpoints, redaction, or a gateway that enforces policy. | Prefer local if raw data cannot leave the tenant boundary. | We go deeper on the governance side in [EU data residency for AI apps](/blog/eu-data-residency-ai-apps-2026/) and the broader economics in [what self-hosting LLMs really costs in the EU](/blog/self-hosting-llms-eu-cost/). If the selected model exceeds every single host, our [Mesh LLM review explains distributed inference across multiple computers](/blog/mesh-llm-distributed-inference-multiple-computers/), including the network and reliability cost of pooling memory. ## The local stack you are actually pricing The stack is not "model plus GPU". A production local deployment usually includes a serving runtime such as [vLLM](https://docs.vllm.ai/en/latest/), model artifact management, quantization decisions, autoscaling or queue control, telemetry, prompt and output logging policy, access control, eval jobs, rollout gates, incident response, and a fallback route when the local model fails or the GPU pool is saturated. For the storage-backed edge case, our [no-hype Colibri and GLM-5.2 review](/blog/colibri-glm-5-2-consumer-hardware/) shows why “runs on 25 GB RAM” can still mean one token every 10 to 20 seconds. Retrieval adds its own line to that bill: our guide to [cutting RAG vector memory 16x](/blog/rag-vector-memory-quantization/) covers the embedding-index footprint and the recall trade-off. That fallback route matters. The most pragmatic architecture is often hybrid: local handles cheap, stable, high-volume work; an API handles hard cases, overflow, image/audio features, tool-heavy reasoning, or the tasks where frontier quality still wins. At that point the calculator becomes a routing calculator: local default, API fallback, measured escalation rate, and evals that prove the cheap path is still acceptable. ## Decision checklist 1. **Can the local model pass the eval?** If no, stop. 2. **Have you priced the optimized API baseline?** Include prompt caching, batch, routing, smaller models, and provider-specific EU options. 3. **Can the workload keep hardware busy?** Use measured concurrency and latency, not hoped-for utilization. 4. **Can your team operate it?** Add serving, security, monitoring, upgrades, rollback, and on-call time. 5. **Does governance require it?** If data cannot leave your controlled environment, cost is secondary but still measured. 6. **Is there a hybrid path?** Local for steady cheap work, API for overflow and hard tasks is often the actual optimum. ## Sources and live-price caveat Provider prices and model names move quickly. The formulas above are stable; example price mechanics are a July 2026 snapshot. Re-check [OpenAI pricing](https://developers.openai.com/api/docs/pricing), [Gemini API pricing](https://ai.google.dev/gemini-api/docs/pricing), [Amazon Bedrock pricing](https://aws.amazon.com/bedrock/pricing/), and [Azure OpenAI pricing](https://azure.microsoft.com/en-us/pricing/details/azure-openai/) before budgeting. For utilization and serving-system assumptions, read [Beyond Per-Token Pricing](https://arxiv.org/abs/2606.11690) and [the vLLM versus TGI performance study](https://arxiv.org/abs/2511.17593). Running that comparison against your own workload, and then building whichever side wins, is our [AI enablement service](/services/ai-enablement/). Where EU data residency is the reason self-hosting is on the table at all, [AI software development in Austria](/ai-software-development-austria/) covers mapping the data flow before committing to hardware. [Hyperstate AI](/case-studies/hyperstate-ai/) is the published case where the GPU economics actually drove the architecture. ## Final thoughts Local models beat APIs when the model passes your eval, the workload keeps hardware busy, the fixed operating cost is lower than API savings, and EU governance benefits are real. They lose when traffic is low or spiky, when the API baseline has not been optimized with caching and batch, or when the team prices the GPU but forgets the people. The practical answer for many EU companies is hybrid: API first while measuring cost per task, then local for high-volume stable work, with API fallback for overflow and hard cases. Break-even is not a slogan. It is a row in your production telemetry. ## You may also like.. [**LLM Cost Calculator 2026** The companion calculator for API cost per successful task: caching, batch, routing, retries, and eval quality.](/blog/llm-cost-calculator-2026/) [**AI Enablement vs a generic AI consultancy** One hands you a strategy deck. The other ships a working setup on your infrastructure that your team owns.](/compare/ai-enablement-vs-generic-ai-consultancy/) Models and infrastructure ## Continue through this cluster Model selection, inference economics, local deployment, compression and serving architecture. [Start with the cornerstone**Self-Hosting LLMs in the EU: When Open Weights Actually Pay Off**](/blog/self-hosting-llms-eu-cost/) - [Netflix's vLLM and Triton Stack: 7 Production Lessons](/blog/netflix-vllm-triton-inference-stack/) - [Transformers.js Browser AI: When Local Inference Belongs in Your Product](/blog/transformers-js-browser-ai-guide/) - [How to Self-Host LiteLLM in Production: 2026 Guide](/blog/self-host-litellm-production-2026/) - [AI-Ready Company Wiki: Architecture and Build Guide](/blog/ai-ready-company-wiki/) - [Does Claude Watermark Text? The 2026 API Answer](/blog/claude-text-watermark-api-2026/) Inbox, without the noise ## Follow the work that matters to you Get a short email when we publish something new. Follow the whole blog or only the problems you care about. [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 13 min read · 08 Jul 2026 [**Next**](/blog/llm-cost-calculator-2026/) New posts by email × × Get new posts by email A short email when we publish. Free, no tracking. ## Structured Data ```json { "@context": "https://schema.org", "@graph": [ { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ], "employee": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "founder": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "legalRepresentative": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "name": "Wavect GmbH", "subjectOf": { "@id": "https://wavect.io/verified-claims.json#dataset", "@type": "Dataset", "creator": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "description": "A machine-readable registry of quantitative and qualitative claims published by Wavect, with review dates, localized page appearances and public third-party citations where available.", "inLanguage": "en", "isAccessibleForFree": true, "license": "https://creativecommons.org/licenses/by/4.0/", "name": "Wavect verified publication claims", "url": "https://wavect.io/verified-claims.json" }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "sameAs": [ "https://www.wikidata.org/wiki/Q139796367", "https://www.linkedin.com/in/jocr77/", "https://github.com/jo-chris" ], "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/#website", "@type": "WebSite", "inLanguage": [ "en", "de", "es", "zh" ], "name": "Wavect", "potentialAction": { "@type": "SearchAction", "query-input": "required name=search_term_string", "target": { "@type": "EntryPoint", "urlTemplate": "https://wavect.io/search/?q={search_term_string}" } }, "publisher": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/blog/local-models-vs-apis-break-even-eu-2026/#webpage", "@type": "WebPage", "dateModified": "2026-07-08", "inLanguage": "en", "isPartOf": { "@id": "https://wavect.io/#website", "@type": "WebSite" }, "lastReviewed": "2026-07-08", "url": "https://wavect.io/blog/local-models-vs-apis-break-even-eu-2026/" } ] } ``` ```json { "@context": "https://schema.org", "@type": "BlogPosting", "abstract": "Local LLMs beat APIs only when the full equation wins, not when GPU hourly price looks cheap. API cost is cost per successful task; self-host cost is GPU hours plus redundancy, storage, networking, observability, engineering ops, eval upkeep and idle capacity divided by successful tasks. Utilization research shows the same H100 can range from very cheap to wildly expensive per million output tokens depending on concurrency and offered load, so utilization is the first variable. Hosted APIs win for low, bursty or fast-changing workloads; local wins for high steady volume, strict EU data residency, or expensive frontier-API replacement when an open-weight model passes your eval. The calculator asks for token volume, tokens per task, concurrency, utilization, latency SLO, engineer cost, eval pass rate and API alternative. Do not self-host before caching, batching, routing and model right-sizing are measured. Figures are a July 2026 snapshot; re-check live provider prices.", "articleBody": " Blog overview/AI and agents/Models and infrastructure When Local Models Beat APIs: A Break-Even Calculator for EU Companies TL;DR Local LLMs beat APIs only when the full equation wins, not when GPU hourly price looks cheap. API cost is cost per successful task; self-host cost is GPU hours plus redundancy, storage, networking, observability, engineering ops, eval upkeep and idle capacity divided by successful tasks. Utilization research shows the same H100 can range from very cheap to wildly expensive per million output tokens depending on concurrency and offered load, so utilization is the first variable. Hosted APIs win for low, bursty or fast-changing workloads; local wins for high steady volume, strict EU data residency, or expensive frontier-API replacement when an open-weight model passes your eval. The calculator asks for token volume, tokens per task, concurrency, utilization, latency SLO, engineer cost, eval pass rate and API alternative. Do not self-host before caching, batching, routing and model right-sizing are measured. Figures are a July 2026 snapshot; re-check live provider prices. Related service: AI Enablement Local LLMs beat APIs only when the workload, governance, and operating model line up. A cheap GPU hour is not a business case. The business case is cost per successful task after utilization, concurrency, redundancy, engineer time, eval upkeep, and EU data residency are counted. If the open-weight model does not pass your eval, the calculator stops. If it passes but the GPU is idle most of the day, the calculator probably stops too. This article is the self-hosting companion to our LLM cost calculator 2026. That post compares API bills by task. This one compares API tasks against local inference. The hard lesson from 2026 utilization research is simple: the same H100 can look extremely cheap or painfully expensive per million output tokens depending on offered load and concurrency. A recent arXiv paper measured a spread from $0.21 to $15.25 per million output tokens on identical H100 hardware when utilization changed, and argues that utilization-naive calculators understate cost by 1 / U. Treat that as the center of the model, not a footnote. This calculator owns server and private-cloud economics. If the workload can run on the customer's device, our Transformers.js browser AI production guide covers client-side privacy, model delivery, WebGPU and WASM fallbacks, and the cost transferred to product engineering and user hardware. The short answer Use an API when traffic is low, bursty, fast-changing, or dependent on frontier reasoning. Use a local model when you have high and steady volume, a model that passes your eval, a team that can operate the stack, and either strict EU data-residency requirements or an API alternative expensive enough to leave real margin. Hosted EU-region APIs and managed open-weight endpoints sit in the middle: often the best answer before full self-hosting. SituationUsually winsWhy Early internal assistant, a few thousand tasks per monthAPIGPU idle time and ops work swamp token savings. Nightly extraction over millions of documentsLocal or batch APISteady load and async latency let you fill capacity or buy batch discounts. Regulated EU workload with sensitive dataEU API, private deployment, or localGovernance can override pure cost, but still compare managed EU endpoints first. Customer-facing SaaS chatbot with spiky trafficAPI or hybridElasticity, safety updates, and burst handling matter more than GPU sticker price. High-volume classification, routing, enrichment, or summarizationLocal candidateSmall open-weight models can pass evals and saturate cheaper hardware. The calculator Start with the API baseline. The right comparison is not provider invoice versus GPU invoice. It is API cost per successful task versus local cost per successful task. LineFormulaNotes API monthly costtasks_per_month * api_cost_per_successful_taskUse the task calculator, not a single call estimate. API task costsum(input + cached_input + output + tools + retries + failure_rework)Apply prompt caching and batch discounts only where they are actually eligible. Self-host monthly costgpu_hours + redundancy + storage + networking + observability + engineering_ops + eval_upkeepDo not hide people cost outside the spreadsheet. Self-host task costself_host_monthly / successful_tasks_per_month + variable_task_costDivide by passed tasks, not requests. Break-even tasksself_host_fixed_monthly / (api_task_cost - self_host_variable_task_cost)If the denominator is small or negative, APIs win. The local inference line needs one extra formula: Effective local cost per 1M tokens = ((gpu_hourly_rate + infra_hourly + ops_hourly) / (tokens_per_second * 3600 * measured_utilization)) * 1,000,000. Measured utilization is the trapdoor. If your spreadsheet assumes 80% utilization but production gives you 12%, your cost is not a little wrong. It is wrong by a factor of roughly 6.7 before redundancy, on-call,", "articleSection": "Engineering", "author": { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/" }, "dateModified": "2026-07-08", "datePublished": "2026-07-08", "description": "Local LLMs beat APIs only when the full equation wins, not when GPU hourly price looks cheap. API cost is cost per successful task; self-host cost is GPU hours plus redundancy, storage, networking, observability, engineering ops, eval upkeep and idle capacity divided by successful tasks. Utilization research shows the same H100 can range from very cheap to wildly expensive per million output tokens depending on concurrency and offered load, so utilization is the first variable. Hosted APIs win for low, bursty or fast-changing workloads; local wins for high steady volume, strict EU data residency, or expensive frontier-API replacement when an open-weight model passes your eval. The calculator asks for token volume, tokens per task, concurrency, utilization, latency SLO, engineer cost, eval pass rate and API alternative. Do not self-host before caching, batching, routing and model right-sizing are measured. 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