# Azure OpenAI in Foundry Models vs Gemini Developer API (slim)

> Azure OpenAI in Foundry Models scores 80 (A) to Gemini Developer's 62.2 (B) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.md (~7,150 tokens) · this version ~3,430 tokens · JSON https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.json · canonical https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-07
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Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Gemini Developer API's 62.2 (B), and leads in 6 of 7 scored categories. Gemini Developer API leads on payments & pricing. Both do llm inference.

- Azure OpenAI in Foundry Models: A 80, rank #14 of 1486 · https://www.anchorterminal.com/tools/azure-openai.min.md
- Gemini Developer API: B 62.2, rank #566 of 1486 · https://www.anchorterminal.com/tools/gemini-api.min.md
- Azure OpenAI in Foundry Models, good for Teams on Azure that need OpenAI models with Entra ID, private networking, EU or US data zones or FedRAMP scope, and one resource that also serves DeepSeek, Llama, Mistral and Grok models. Ahead on Reliability, 80 against 45; Agent ergonomics, 98 against 93; Security & auth, 95 against 50; Transparency & trust, 83 against 72. Also Agent-ready, a grade of BB or better; No incidents deducted, where Gemini Developer API loses 3 points for them.
- Gemini Developer API, good for Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier. Ahead on Payments & pricing, 40 against 20. Also Free to start without a card.

| Category | Azure OpenAI in Foundry Models | Gemini Developer API |
| --- | --- | --- |
| Reliability (16%) | 80 | 45 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 87 | 85 |
| Agent ergonomics (13%) | 98 | 93 |
| Security & auth (14%) | 95 | 50 |
| Payments & pricing (10%) | 20 | 40 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 86 | 83 |
| Transparency & trust (7%) | 83 | 72 |

| Fact (where they differ) | Azure OpenAI in Foundry Models | Gemini Developer API |
| --- | --- | --- |
| Vendor | Microsoft | Google |
| Hosted endpoint | `https://<resource>.openai.azure.com/openai/v1` | `https://generativelanguage.googleapis.com/v1beta` |
| Auth | OAuth or key | API key |
| Pricing | Pay per use | Freemium |
| Licence | Proprietary service under the Microsoft Product Terms. The v1 OpenAPI spec is MIT, and the OpenAI SDKs the docs use are Apache-2.0 | Apache-2.0 (SDKs) |
| llms.txt | no | yes |
| Last release | 2026-10-26 | 2026-10-30 |
| Terms last updated | no date given | 2026-04-28 |
| Privacy policy last updated | 2026-09-01 | 2026-10-01 |
| Popularity | 454k npm/wk | 4k stars |
| Agent reviews | none | 3.5/5 (2) |

Why the edge:
- Security & auth, Azure OpenAI in Foundry Models +45: Azure OpenAI in Foundry Models 95 (Model reading. Microsoft Entra ID tokens with Azure roles (the Cognitive Services OpenAI User role for calls), managed identities, and key authentication that can be disabled per resource, or two regenerable resource keys in the `api-key` header. No documented route puts the key in a URL query string (30). Prompts, completions, embeddings and training data aren't used to train foundation models without the customer's permission or instruction, per the data privacy page updated 4 August 2026 (20). Flagged prompts and completions may be stored in the customer's geography for human abuse review, with no retention period stated, and approved customers can turn that storage off through modified abuse monitoring. Responses API data is kept 30 days by default unless `store` is false (12 of 15). Azure Monitor resource logs and the subscription activity log, as read in the Foundry fine-tuning dossier (15). MSRC coordinated disclosure policy and Microsoft's bounty programme, both named in microsoft.com's security.txt, and Azure OpenAI is listed in FedRAMP High and DoD IL2 scope in Azure public regions. The security.txt passed its Expires date on 2026-09-23 and was still expired on 6 November, which the Bedrock and S3 dossiers mark down by 2, applied here too (18 of 20, down from 20 on 6 October for consistency).); Gemini Developer API 50 (Model reading. Keys live in a Cloud project. Since 28 May 2026 new keys are authorisation keys bound to a service account and restricted to the Gemini API, keys can be limited by IP or origin, unrestricted standard keys are rejected, and the docs give a leak checklist, per the API key page re-read on 1 November. No permission scopes, so 25 of 30, less 10 because the API reads a token from the URL. The Discovery document (revision 20261101) lists `access_token`, `oauth_token` and `key` as query parameters, and on 1 November a generateContent request with no credential answered 403 'Method doesn't allow unregistered callers' while one with a made-up `?access_token=` answered 401 'Request had invalid authentication credentials' (ACCESS_TOKEN_TYPE_UNSUPPORTED), the deduction Google APIs took on 17 October and 1 November and Gemini Embedding and Gemini Live carry (15 of 30). Terms (effective 23 March 2026, re-read 1 November) say free-tier prompts and responses are used to improve Google products and human reviewers may read them, paid tier isn't used (10 of 20). Abuse monitoring on paid services is kept 'a limited period' with no number, grounding data 30 days, and since 26 October new paid projects store Generate Content requests by default unless `store=false`. No zero-retention route on this API was found (5 of 15). Request logs in AI Studio for billed projects (15). security.txt on google.com valid to 2030-04-01 with a vulnerability reward programme, re-read on 1 November. No certification naming the Developer API found, the reading Gemini Embedding takes (5 of 20).)
- Reliability, Azure OpenAI in Foundry Models +35: Azure OpenAI in Foundry Models 80 (Azure status page with post-incident reviews, listing Azure OpenAI Service, Foundry Models and Foundry Agent Service as components (20). One incident in the 90 days to 6 November 2026 names Azure OpenAI, and no review after it does, intermittent request failures, higher latency and HTTP 5xx errors in Sweden Central from 10:03 to 15:58 UTC on 29 September 2026 (tracking 7QL5-Z50), counted as one major outage as in the Foundry fine-tuning dossier. The 23 July West US review and the 30 September gateway incident don't name it (10). Quota tiers from Free to Tier 6 with TPM and RPM per model and deployment type, 1,000 RPM and 1,000,000 TPM for each GPT-6 model on Global Standard at Tier 1 (15). 429 responses carry `retry-after-ms`, every response carries `x-ratelimit-*` headers, and the quota guide gives exponential backoff with jitter and SDK retry settings (15). The models page says models sold by Azure are covered by Azure service-level agreements and links the Online Services SLA. The consolidated SLA document wouldn't download on 6 October and wasn't retried on 6 November, so the clause rests on Microsoft's docs, read the same way as the 5 October Azure Speech check (10). The v1 API is GA and the GPT-6 models carry no preview label (10).); Gemini Developer API 45 (Status page at aistudio.google.com/status (robots.txt allows it), but on 1 November it still returned a script shell titled Google AI Studio with no components or history, so 10 of 20, and 5 of 30 for the incident line with no readable record, as the Gemini Live and Gemini Embedding listings read the same page. Google Cloud's status dashboard covers Vertex AI products, not this endpoint. Per-model RPM and TPM live in AI Studio. The public rate-limits page, re-read on 1 November, publishes tier rules, spend-based limits of $10, $50 and $200 per 10 minutes for Tiers 1 to 3, and batch queue limits (5 of 15). The troubleshooting page, re-read on 1 November, gives exponential backoff with jitter for 429 RESOURCE_EXHAUSTED and 503 UNAVAILABLE, says to retry 429, 408 and 5xx and not 400, 402 or 403, and says the Python SDK retries transient errors up to four times (15). No SLA found for the Developer API (0). The flagship `gemini-4-argon` went GA on 13 October 2026 and the default 3.8 Flash is GA, and the Interactions API the guides lead with has been Generally Available since June 2026. The listing's example and the SDKs default to the `v1beta` path, which Google labels beta, but the API versions page, read on 1 November, says the stable `v1` path carries the Interactions API, generateContent, function calling and structured output and supports every model, so the surface is available as GA, the reading Gemini Embedding takes on the same endpoint. Gemini Live keeps a deduction because the Live API runs on `v1beta` only (10). Before 13 October the only Pro model was `gemini-3.1-pro-preview` (5 of 10).)
- Payments & pricing, Gemini Developer API +20: Azure OpenAI in Foundry Models 20 (No x402, MPP or L402 (0). Per-1M-token prices are public in the Azure Retail Prices API without a login, while the pricing page fills its table with JavaScript and showed GPT-6 prices as pending on 6 October (20). The quota tiers include a Free tier, but an Azure subscription needs a payment method (0). A person signs up for Azure in a browser and creates a resource and a deployment before the first call (0).); Gemini Developer API 40 (No machine payment protocol (0). Per-token prices published without a login, re-read on 1 November (20). Free tier on 3.8 Flash, 3.6 Flash, 3.5 Flash-Lite and 3.1 Flash-Lite, not on `gemini-4-argon` or 3.1 Pro preview, and the rate-limits page needs only an active project for the free tier and a linked billing account from Tier 1, so the free tier needs no card (20). A Google account and AI Studio are needed for a key (0).)
- Transparency & trust, Azure OpenAI in Foundry Models +11: Azure OpenAI in Foundry Models 83 (Closed service under the Microsoft Product Terms, with the v1 spec under MIT and the OpenAI SDKs under Apache-2.0 (15). The data privacy page agrees with the product terms on no training and states where data is processed and stored by deployment type, but gives no retention period for the human-review store and sits beside a 30-day default for Responses data (24 of 30). A published lifecycle policy and a retirement schedule with dates and replacements, but on 6 November the schedule listed `gpt-realtime-mini` 2025-10-06 and 2025-12-15 twice each with different retirement dates, and no GPT-6 model yet (18 of 20, down from 20). Data locations stated for Standard, Data Zone and Global deployments, and a public sub-processor list per the 5 October Azure Speech check, not re-read (20).); Gemini Developer API 72 (Closed service with clear terms, SDKs Apache-2.0 (15). Terms, pricing page and logs policy agree that free-tier data improves Google products and paid data doesn't, but abuse retention on paid services has no number, and the 26 October changelog entry turns on request storage by default for new paid projects while existing projects keep their setting (20 of 30). Deprecations table with earliest shutdown dates, exact dates promised later (15 of 20). Terms say data may be cached in any country where Google has facilities. No subprocessor list checked (5 of 20).)
- Agent ergonomics, Azure OpenAI in Foundry Models +5: Azure OpenAI in Foundry Models 98 (Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use with parallel calls and a forced call by naming the function in `tool_choice`, 18 of 20 because `gpt-6.1-sol` calls tools only through the Responses API (18). Strict structured outputs (15). Prompt caching from 1,024 tokens, cached input at 0.1x on GPT-6 ($1 per million on Astra) and cache writes at 1.25x from GPT-5.6 on, with explicit breakpoints (15). 1,050,000-token context on GPT-6 and GPT-6.1, input capped at 922,000 (15). Batch at 50% off Global Standard with a 24-hour target (10). The docs use OpenAI's own SDKs for Python, JavaScript, C#, Go and Java against the v1 endpoint (10). 429 and quota errors documented with retry headers (15). Calls name a deployment rather than a model, so each model must be deployed in the resource first, which the model checklist doesn't score.); Gemini Developer API 93 (Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling documented, and `FunctionCallingConfig` mode `ANY` with `allowedFunctionNames` forces a call to a named tool, with a `VALIDATED` mode that checks calls by constrained decoding (20 of 20). The structured output page, re-read on 1 November, says output adheres to the supplied JSON Schema and is syntactically correct JSON, and names the keywords constrained decoding doesn't enforce, while asking callers to validate values (13 of 15; the 12 October note's 10 rested on the page promising no constrained decoding). Context caching at 0.1x input, $0.075 per million on 3.8 Flash until 31 December, and implicit caching on `gemini-4-argon`, which doesn't support explicit caches (15). 1,048,576-token context on the default and flagship models (15). Batch half price (10). Official SDKs for Python and JavaScript (10). Documented error codes with backoff guidance (10 of 15, because 400, 402 and 403 are flagged as not retryable but recovery advice per code sits on a separate page we only skimmed).)

| Task | Azure OpenAI in Foundry Models | Gemini Developer API |
| --- | --- | --- |
| Research a product and write a sourced report | $110 | $1.93 |
| Build a small four-page website | $25.07 | $0.84 |
| Find and fix a bug in a large codebase | $5.41 | $0.18 |