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Foundry Model Ledger: Which Model, at What Price, Until When, and Which of Mine?

Build a useful Foundry model ledger by tracking exact versions, deployment and billing types, lifecycle dates, replacements, and the deployments in your own Azure subscription.
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A useful Microsoft Foundry model ledger needs more than a model name. Track the exact version, deployment and billing type, region, lifecycle status, retirement date, and any suggested replacement. Then check your Azure subscription separately: Microsoft’s public catalog and retirement schedule do not show which models you have deployed.

What to record for each Foundry deployment

Keep one record per deployment, not just one per model family. The same model version can have different deployment types, prices, regions, and lifecycle implications. Microsoft describes a catalog with more than 10,000 models and approximately 50 new models published each month; the overview does not state a year for those figures, and catalog contents change over time. Microsoft Foundry Models overview

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  • Identity: provider, catalog model name, and exact model version.
  • Lifecycle: current stage, retirement date, and any replacement named for that specific version.
  • Your deployment: deployment name, Azure subscription, region or data zone, and deployment type.
  • Cost evidence: billing basis, observed price and currency, applicable region and SKU, date checked, and usage period.
  • Migration ownership: responsible owner, candidate replacement, test status, and links to the official catalog, pricing, and retirement information you used.

Keep observed prices tied to the deployment conditions and check date. A model name by itself is not a portable price quote.

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At what price? Start with the deployment type

Foundry has multiple model categories, including models sold by Azure and partner or community models. Provider and deployment path affect the terms and price. Microsoft describes two broad deployment options: managed compute and serverless API deployments. Microsoft Foundry Models overview

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Deployment path How it is billed What to record
Managed compute Virtual-machine core hours used by deployments. Deployment type, VM or SKU details, region, observed price and currency, and date checked.
Serverless deployment Inputs and outputs, typically measured in tokens, for an API to a Microsoft-hosted model. Pricing is shown before deployment. Displayed input and output rates, currency, model/version, region or data zone where applicable, and date checked.
Partner or community model Provider-defined license terms and prices set through Azure Marketplace. Provider, Marketplace terms and displayed price, deployment path, and date checked.

For partner and community models, inspect the deployment’s displayed terms and price rather than assigning one universal price to the model name. Foundry Models from partners and community

Until when? Track lifecycle stage and version

Microsoft assigns each catalog model to one lifecycle stage. The stage tells you what the status means; it is not a substitute for the retirement date of the exact version you deployed. Foundry Models lifecycle and support policy

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Generally Available (GA) Described as production-ready, with fixed weights and APIs.
Legacy A newer model exists, so plan to assess migration. This stage may be skipped.
Deprecated Existing customers may continue managing deployments, but new customers cannot access or deploy the model. For a specific model version, Microsoft’s policy defines an existing customer at the Azure subscription level.
Retired The model is removed from service; inference requests return 410 Gone.

Record the stage, retirement date, and replacement against the exact model version. Microsoft’s policy describes a standard 18-month lifecycle for GA model versions and a 12-month lifecycle for certain providers, including Anthropic, DeepSeek, Fireworks, and Mistral AI. Those periods should not be used to infer an individual model’s deadline: check its schedule entry and the policy. Availability also varies by region and cloud environment, and not every model/version combination is available in every region. Foundry Models lifecycle and support policy

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How to read a retirement date and replacement

The schedule is version-specific, and its entries can change. For example, the schedule accessed on October 4, 2026, listed gpt-4o version 2024-05-13 as Deprecated, with retirement on December 9, 2026, and gpt-5.6-sol as its replacement. Treat that as a dated example of how to read a schedule entry, not a standing recommendation; check the live schedule before making a decision. Model retirement schedule

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A listed replacement is a candidate, not proof that it will behave the same in your application. Plan backward from the retirement date, allowing time for evaluation, rollout, and recovery. The practical migration deadline is earlier than retirement by the time your validation and rollout require.

Which of mine? Check your subscription’s deployments

The public catalog and retirement schedule describe models and lifecycle information; they do not establish which deployments belong to your Azure subscription. Add a subscription-level inventory to the ledger and reconcile it with the deployment details available to your team. Record the subscription, deployment name, model and version, region, and deployment type so that each schedule entry can be matched to a resource you actually operate.

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Do not treat a model’s public availability or a schedule listing as evidence that you deployed it. Likewise, a public price does not establish what a particular deployment costs without its deployment path and applicable pricing details.

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How to assess a replacement before migration

Microsoft recommends evaluating models with your own application and data, comparing quality, latency, and cost. Regional availability and compliance constraints can also narrow the choices. Model migration guidance

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  • Test representative workload quality, including output shape and tool use where your application relies on them.
  • Measure latency and cost under your workload rather than assuming the replacement’s behavior or economics from its name.
  • Confirm the candidate is available in the required region or data zone and satisfies your compliance constraints.
  • Review retirement behavior for your deployment type and upgrade setting. Standard, Global Standard, and Data Zone Standard pay-as-you-go deployments may be auto-upgraded on a rolling, region-by-region schedule according to versionUpgradeOption. With NoAutoUpgrade, the deployment stops working at retirement.
  • For provisioned deployments, plan a customer-led migration; these deployments are not auto-upgraded.

Record test outcomes and the migration owner alongside the candidate replacement so the decision is actionable, not just a name copied from the schedule. Model migration guidance

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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