Microsoft Foundry Models
$200 for new accounts; it does not recur.
https://{resource}.services.ai.azure.com/api/Models mentioned
The offer covers a dynamic catalog, provider-selected route, or model class without stable model IDs in this snapshot.
Equivalent paid value
How this was valued: The $200 general Azure signup credit is spread across its 30-day validity. Eligible Foundry inference competes with all other Azure trial spend.
Limits and terms
- General Cloud Credit USD
- 200
- Duration Days
- 30
- Recurrence
- No
What happens to your prompts?
Base inference is stateless and no-training, but abuse review and stateful features can retain content for human access.
- Plan Scope
- Microsoft Foundry Azure Direct Models, including Azure OpenAI and partner models; stateful APIs and modified abuse-monitoring customers differ.
- Prompt Retention
- Base inference is stateless, but prompts and completions selected for potential abuse can be stored for authorized human review. Assistants, Responses history, Stored Completions, Batch, and customer data sources intentionally persist content.
- Response Retention
- Same abuse-monitoring and feature-specific rules as prompts.
- Ordinary Logging
- Prompts and outputs are evaluated synchronously by safety systems. Usage, resource, security, and billing telemetry is retained; automated review alone does not store content.
- Model Training
- Microsoft says prompts, completions, embeddings, and fine-tuning data are not used to train, retrain, or improve base models without customer permission or instruction.
- Product Improvement
- Customer content is excluded from general model improvement absent permission; safety classification and service telemetry remain part of delivery.
- Human Or Operator Access
- Only content flagged for potential recurring or severe abuse enters the separated review store and can be accessed by authorized Microsoft employees through controlled, request-specific access. Approved modified-monitoring customers avoid this storage and human review.
- Subprocessors And Routing
- Content stays within the Azure service boundary and selected deployment geography subject to Global or DataZone routing; partner-model licenses and Microsoft subprocessors apply.
- Deletion Controls
- Stateless model processing stores no model state. Customers manage Assistants, Responses, files, batches, Stored Completions, fine-tuning assets, and data sources; qualified managed customers can request modified abuse monitoring.
- Caveat
- “Not used for training” does not mean “never retained.” Free or unmanaged access should assume standard abuse monitoring and should avoid sensitive prompts unless the deployment's controls are confirmed.
Governing documents
- Customer Agreement
- https://www.microsoft.com/licensing/docs/customeragreement
- Privacy Statement
- https://privacy.microsoft.com/privacystatement
Eligibility
- New Customer Only
- Yes
- Phone Required
- Yes
- Non Prepaid Payment Card Required
- Yes
- Automatic Charging
- No
Primary sources
Before you build with Microsoft Foundry Models
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
No stable model ID is recorded. The offer may use a dynamic catalog, a provider-selected route, or a model class, so resolve the current machine-readable ID before writing a fixed production configuration. The recorded base endpoint is https://{resource}.services.ai.azure.com/api/, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
The public evidence does not settle whether an account is required. Confirm the signed-in flow before treating anonymous or credential-free access as available. A payment method is required. A zero-cost allowance can still be useful, but protect the account with provider-side budgets or alerts before sending production traffic. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Partially private”: Base inference is stateless and no-training, but abuse review and stateful features can retain content for human access. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 11 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.