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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsA useful Microsoft Foundry model ledger tracks the exact model version, deployment, billing basis, lifecycle stage, retirement date, and any proposed replacement. To find out which models are yours, check the resources in your Azure subscription: the public catalog and retirement schedule do not show your personal deployments.
What belongs in a Foundry model ledger?
Record deployment details alongside the model identity. A family name alone is not enough: different versions can have different lifecycle states and retirement dates, while deployment type and region affect availability and billing.
| Field | What to record |
|---|---|
| Model identity | Provider, catalog model name, and exact model version |
| Deployment | Deployment name, Azure subscription, region or data zone, and deployment type |
| Billing | Billing basis, observed price and currency, applicable SKU, usage period, and date checked |
| Lifecycle | Current lifecycle stage, version-specific retirement date, and suggested replacement, if listed |
| Ownership and migration | Migration owner, test status, and source URL for the recorded details |
Keep a price tied to the deployment type, region, SKU, and date on which you observed it. It is not a universal price for a model. Catalog contents, prices, regional availability, lifecycle states, retirement dates, and replacements can change; verify the current details before budgeting or scheduling a migration.
Which models and providers are in the catalog?
Foundry includes Azure-sold models as well as partner and community models. Microsoft’s overview describes a catalog of over 10,000 models and approximately 50 new models published each month; the overview does not state the year for that figure. See the Microsoft Foundry Models overview for the catalog and deployment options.
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Provider and deployment path matter. For partner and community models, providers define license terms and set prices through Azure Marketplace. Check the terms and price shown for the specific deployment rather than copying a price from another provider or deployment path. Microsoft explains this in its guidance on Foundry Models from partners and community.
At what price is a model billed?
Foundry’s two broad deployment options use different billing bases, so a ledger should record the deployment’s billing method rather than treat price as a portable model attribute.
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| Deployment option | Billing basis described by Microsoft | What to capture |
|---|---|---|
| Managed compute | Virtual-machine core hours used by deployments | Relevant VM or SKU, region, observed price and currency, and date checked |
| Serverless deployment | API inputs and outputs, typically measured in tokens; pricing is shown before deployment | Displayed pricing, applicable input/output measures, region, and date checked |
These are billing descriptions, not price quotes. Confirm the displayed price for the deployment you intend to use. The Foundry overview describes the two deployment options and their billing bases.
What does a lifecycle stage tell you?
Microsoft assigns each catalog model to one of five stages. The stage affects whether a model is experimental, suitable for production, or available for new deployments. Read it together with the exact version and its schedule entry. The lifecycle and support policy defines the stages:
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- Preview: Experimental. Weights, runtime, and API schema may change, and general availability is not guaranteed.
- Generally Available (GA): Described as production-ready, with fixed weights and APIs.
- Legacy: A newer model exists, so migration planning is appropriate. This stage may be skipped.
- Deprecated: Existing customers may continue managing deployments, but new customers cannot access or deploy the model. The policy defines an existing customer at the Azure subscription level for the specific model version.
- Retired: The model is removed from service; inference requests return
410 Gone.
The policy describes an 18-month standard lifecycle for GA model versions and a 12-month lifecycle for certain providers, including Anthropic, DeepSeek, Fireworks, and Mistral AI. Check the individual model schedule and policy rather than assuming either period applies to every version. Availability also varies by region and cloud environment; not every model/version combination is available everywhere.
Until when can you use a model version?
Use the retirement date for the exact version, not a date associated with its model family or a different version. The Microsoft model retirement schedule lists lifecycle information and suggested replacements by version, and says details are subject to change.
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For example, the schedule accessed on October 4, 2026, showed gpt-4o version 2024-05-13 as Deprecated, with retirement on 2026-12-09 and gpt-5.6-sol as its replacement. This is a dated schedule entry, not a standing recommendation: verify the live schedule before acting.
How do you find which models are yours?
The public catalog describes available models, and the retirement schedule describes version lifecycle information. Neither identifies which deployments exist in your Azure subscription. Build the “which of mine?” part of the ledger from a subscription-level check of your own deployed resources, recording the subscription and deployment name alongside each model version. The lifecycle policy’s subscription-level definition of an existing customer also makes the subscription relevant when assessing access to a deprecated version.
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Keep that inventory separate from public catalog availability. A model listed publicly is not evidence that you deployed it, and a public schedule entry does not establish your deployment’s configuration, region, billing basis, or upgrade behavior.
How should you plan a migration?
A replacement named in the schedule is a candidate to evaluate, not proof of identical behavior in your application. Microsoft recommends testing models with your own application and data, comparing quality, latency, and cost; regional and compliance constraints also affect the shortlist. See the model migration guidance.
Check what happens to your deployment at retirement
Retirement behavior depends on deployment type and configuration. 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 the deployment’s versionUpgradeOption. If it is set to NoAutoUpgrade, the deployment stops working at retirement. Provisioned deployments are not auto-upgraded and require a customer-led migration. Check the migration guidance for the applicable behavior rather than assuming every deployment upgrades automatically.
Set a practical migration deadline
Plan backward from the version’s retirement date: allow time to validate the replacement and roll it out before retirement, rather than treating the retirement day as the start of migration. Test against the dimensions that matter to your workload:
- Output quality and shape
- Latency and tool use
- Cost under expected usage
- Region and data-zone availability
- Compliance requirements
Record the test status and migration owner in the ledger so the schedule date is connected to an actionable plan.
Quick Recap
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.
