# Low-Traffic Fine-Tuned Azure OpenAI Deployments Accruing Hosting Fees Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/low-traffic-fine-tuned-azure-openai-deployments-accruing-hosting-fees A fine-tuned Azure OpenAI model deployed as Standard or Global Standard carries an hourly hosting fee on top of per-token inference, and Microsoft... By: PointFive Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) The short version A fine-tuned Azure OpenAI model deployed as Standard or Global Standard carries an hourly hosting fee on top of per-token inference, and Microsoft states that the fee applies whether or not any chat completions or Responses API calls are made. PointFive Research Cloud cost research at PointFive Azure service [Azure Cognitive Services](https://www.pointfive.co/efficiency-hub/cloud-services/azure-cognitive-services) Category [AI](https://www.pointfive.co/efficiency-hub/service-category/ai) Reference CER-0561 Type Idle or Unused Resource ## Explanation Why the waste happens and who it affects. Teams commonly deploy several candidate fine-tunes to compare them, keep last quarter's version deployed alongside the new one, or leave a fine-tune serving an internal tool that is used a few times a week. Each of these deployments bills around the clock. Azure deletes a fine-tuned deployment automatically after 15 consecutive days with no chat completions or Responses calls, which caps the cost of a completely abandoned deployment at roughly two weeks of hosting. The larger and more persistent waste is deployments that receive occasional calls, such as a weekly job, a health check or a rarely used feature, because any call resets the inactivity clock and the deployment is never removed. For those, the hosting fee can dwarf the token spend. Stored fine-tuned models cost nothing, so a deployment can be deleted and recreated when needed. ## Billing model The pricing dimensions that drive this cost. Fine-tuned models are charged for training, hosting and inference. Hosting fee Hourly charge per deployed fine-tuned model on Standard and Global Standard ($1.70 per hour in East US, USD, on the pricing page), applied even if the model is unused Inference Per-token charge at the same rate as the base model's deployment type Stored model A fine-tuned model that is not deployed is stored at no cost and can be redeployed at any time Developer deployment Per-token only with no hourly hosting fee and no SLA; deleted automatically after 24 hours ## How to detect 5 checks to find it in your estate. - List deployments of fine-tuned models (model names containing .ft-) on each Azure OpenAI or Foundry resource with az cognitiveservices account deployment list, including their sku.name - For each, sum the Azure OpenAI Requests (AzureOpenAIRequests) metric split by ModelDeploymentName over the last 30 days; flag deployments with only a handful of requests per day or week - Compare each deployment's monthly hosting charge in Cost analysis (group by meter and filter to the fine-tuned hosting meters) with its token charges; hosting that exceeds inference by a wide margin indicates a low-traffic deployment - Look for several fine-tuned deployments of the same base model and use case, which usually indicates leftover evaluation candidates or superseded versions - Check non-production resources for Standard or Global Standard fine-tuned deployments that are used only during testing ## How to fix 5 ways to remove the waste. - Delete fine-tuned deployments that are not serving production traffic; the underlying model stays stored at no cost and can be redeployed from the portal, REST API or CLI when needed - Evaluate candidate fine-tunes on Developer deployments, which have no hourly hosting fee, accepting that they have no SLA and are removed after 24 hours - Consolidate traffic onto one production fine-tune per use case and retire superseded versions promptly after a cutover - For infrequent batch-style use, deploy the fine-tune just before the job and delete it afterward through automation, rather than keeping it hosted continuously - Weigh the hosting fee against the token savings the fine-tune provides; for low-volume use cases a base model with prompt engineering may cost less overall ## Documentation Vendor references for pricing and configuration. - [Deploy a fine-tuned model - Microsoft Foundry learn.microsoft.com](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/fine-tuning-deploy) - [Fine-tuning cost management - Microsoft Foundry learn.microsoft.com](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/fine-tuning-cost-management) - [Plan and Manage Costs - Microsoft Foundry learn.microsoft.com](https://learn.microsoft.com/en-us/azure/foundry/concepts/manage-costs) - [Monitoring data reference for Azure OpenAI learn.microsoft.com](https://learn.microsoft.com/en-us/azure/foundry/openai/monitor-openai-reference) - [Azure OpenAI pricing azure.microsoft.com](https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - Azure Cognitive Services CER-0238 ### [Always-On PTUs for Seasonal or Cyclical Azure OpenAI Workloads](https://www.pointfive.co/efficiency-hub/inefficiencies/always-on-ptus-for-seasonal-or-cyclical-azure-openai-workloads-ccb97) Many Azure OpenAI workloads - such as reporting pipelines, marketing workflows, batch inference jobs, or time-bound customer interactions-only run during specific periods. When PTUs remain fully provisioned 24/7, organizations incur... AI - Azure Cognitive Services CER-0248 ### [Non-Production Azure OpenAI Deployments Using PTUs Instead of PAYG](https://www.pointfive.co/efficiency-hub/inefficiencies/non-production-azure-openai-deployments-using-ptus-instead-of-payg-9fe81) Development, testing, QA, and sandbox environments rarely have the steady, predictable traffic patterns needed to justify PTU deployments. These workloads often run intermittently, with lower throughput and shorter usage windows. When PTUs... AI - Azure Cognitive Services CER-0266 ### [Underutilized PTU Quota for Azure OpenAI Deployments](https://www.pointfive.co/efficiency-hub/inefficiencies/underutilized-ptu-quota-for-azure-openai-deployments-da831) When organizations size PTU capacity based on peak expectations or early traffic projections, they often end up with more throughput than regularly required. If real-world usage plateaus below provisioned levels, a portion of the PTU... AI --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.