# Azure OpenAI in Foundry Models vs BlockRun.AI (slim)

> Azure OpenAI in Foundry Models scores 80 (A) to BlockRun.AI's 78.9 (A) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.

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- 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 BlockRun.AI's 78.9 (A), and leads in 5 of 7 scored categories. BlockRun.AI leads on payments & pricing. Both do llm inference. BlockRun.AI accepts x402. Azure OpenAI in Foundry Models doesn't.

- Azure OpenAI in Foundry Models: A 80, rank #14 of 1486 · https://www.anchorterminal.com/tools/azure-openai.min.md
- BlockRun.AI: A 78.9, rank #20 of 1486 · https://www.anchorterminal.com/tools/blockrun-ai.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 65; Agent ergonomics, 98 against 88; Security & auth, 95 against 61; Transparency & trust, 83 against 73. Also A hosted endpoint, with nothing to install.
- BlockRun.AI, good for Agents that must start and pay with no human sign-up, and builders testing x402 against a real inference gateway. Ahead on Payments & pricing, 100 against 20. Also An agent can pay per call over x402, with no account; Runs on your own machine.

| Category | Azure OpenAI in Foundry Models | BlockRun.AI |
| --- | --- | --- |
| Reliability (16%) | 80 | 65 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 87 | 90 |
| Agent ergonomics (13%) | 98 | 88 |
| Security & auth (14%) | 95 | 61 |
| Payments & pricing (10%) | 20 | 100 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 86 | 85 |
| Transparency & trust (7%) | 83 | 73 |

| Fact (where they differ) | Azure OpenAI in Foundry Models | BlockRun.AI |
| --- | --- | --- |
| Kind | Model API | MCP server |
| Vendor | Microsoft | BlockRun, Inc. |
| Hosted endpoint | `https://<resource>.openai.azure.com/openai/v1` | no (local only) |
| Transports | HTTP | stdio |
| x402 | no | yes |
| 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 | MIT |
| Tools exposed | none | 19 |
| Read-only variant documented | no | yes |
| llms.txt | no | yes |
| MCP registry | not listed | `io.github.BlockRunAI/blockrun-mcp` |
| Last release | 2026-10-26 | 2026-10-28 |
| Terms last updated | no date given | 2026-09-02 |
| Privacy policy last updated | 2026-09-01 | 2026-09-02 |
| Customer content may train models | yes | not found in the text |
| Terms restrict automated access | yes | not found in the text |
| Terms restrict benchmarking | yes | not found in the text |
| Popularity | 454k npm/wk | 5 stars, 679 PyPI/wk |
| Agent reviews | none | 3.5/5 (2) |

Why the edge:
- Payments & pricing, BlockRun.AI +80: 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).); BlockRun.AI 100 (x402 v2 on its own gateway, USDC on Base and Solana, and since MCP 0.55.0 (28 October) on Circle's Arc through arc.blockrun.ai (40). Per-token prices per model published without a login, plus a flat $0.001 fee per wallet call on Base (20). Seven free models per the status page's menu, no card or sign-up, and a wallet costs nothing to create (20). An agent can create its own wallet and pay per request with no human sign-up (20). 40 + 20 + 20 + 20 = 100.)
- Security & auth, Azure OpenAI in Foundry Models +34: 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).); BlockRun.AI 61 (MCP and API reading. The credential is a wallet private key, auto-created at `~/.blockrun/.session` or passed in an environment variable and optionally mirrored to the OS keychain, and it controls the whole balance. `blockrun_wallet` can delegate a capped budget to a child agent, and an API-key route backed by account credit exists (20 of 30, as on 1 October). `blockrun_polymarket_read` gives read-only positions and previews apart from the trading tool, trades need `confirm:true` with a $25 default order cap, `--profile` drops whole groups of tools, and `BLOCKRUN_CONFIRM_SPEND=on` pauses every paid call for approval, though it's off by default (16 of 20). Search, Exa, market and RPC tools return third-party content, and no prompt-injection guidance was found in the README or docs read (5, as knock and suprsend). Every payment settles on-chain and the wallet tool reports balances and spending (10 of 15). security.txt is valid on 1 November, with an email contact, a policy link and an Expires date of 1 November 2027, and SECURITY.md takes private GitHub reports with a 72-hour acknowledgement and a 7-day plan for fund-affecting issues. No bounty or certification found (10). 20 + 16 + 5 + 10 + 10 = 61.)
- Reliability, Azure OpenAI in Foundry Models +15: 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).); BlockRun.AI 65 (Read with the local-software lines, as the listing's kind is `mcp` and the owner runs the server over stdio, though every paid call lands on BlockRun's gateway (the reading assymetrix-mcp and copilot-studio-mcp get). The 1 October check read it as a router. Installs from the official npm package `@blockrun/mcp` with Node 20.19 or later stated in `engines` (20). The CI workflow runs typecheck, build and tests on Node 20.19 and 22 on every push and pull request, with a required gate job. Run results weren't readable, as GitHub's pages answered 403 (20 of 25). Open issues couldn't be read. The changelog names numbered reports fixed in releases, such as #176 and #177 in 0.54.3 and 0.54.4 (10 of 25). Semver numbering with breaking changes marked BREAKING in CHANGELOG.md and shipped as minor bumps, as its own entries say (15). Version 0.55.0, pre-1.0 (0). 20 + 20 + 10 + 15 + 0 = 65. For the gateway behind it, blockrun.ai/status still shows live checks of the Base and Solana gateways and says "None recorded on this page yet" under incident history, the terms still say "We do not commit to any particular uptime", and free models are limited to 30 requests a minute and 300 an hour per IP with 429s carrying Retry-After.)
- Agent ergonomics, Azure OpenAI in Foundry Models +10: 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.); BlockRun.AI 88 (MCP reading. 19 tools, which the README measures at 14,416 tokens of definitions in the default profile (15 for 11 to 30), and `--profile` loads fixed subsets of 3 to 8 tools (chat 2,155 tokens, research 2,800, media 7,212, trading 5,471) (8 back, 23 of 25). Search takes `max_results`, chat responses carry `truncated_output` and `finish_reason`, and market tools pass filters through to the data source, but per-tool paging and size controls weren't read in detail (12 of 20). The gateway's error reference gives a code per status, whether it's charged and what to do next, and the server redacts secrets on both thrown and `isError` paths (18 of 20). Every tool carries a readOnlyHint or destructiveHint assigned by effect since 0.33.0, with a test that fails on an unannotated tool. Payment settles only after a successful upstream response, and since 0.54.2 a withdrawal that may have moved money is never signed twice (20). Free tools need no setup, and official SDKs cover Python, TypeScript and Go (15). 23 + 12 + 18 + 20 + 15 = 88.)
- Transparency & trust, Azure OpenAI in Foundry Models +10: 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).); BlockRun.AI 73 (The editorial half. The MCP server and SDKs are MIT and the gateway is closed. The listing is graded as the MCP server, so 30 as assymetrix-mcp got for an MIT server in front of a closed API (was 15 under the router reading). The terms and privacy policy, both last updated 21 October 2026, now name the same entity, BlockRun, Inc., as contracting party and data controller. The privacy policy now says prompts, completions and tool calls to blockrun.ai, arc.blockrun.ai, nano.blockrun.ai and api.blockrun.ai are stored with the paying wallet or account and kept "until we delete it; it has no fixed expiry", usage records and connection metadata for 13 months, while the Solana gateway keeps no request content. The 1 October policy said prompts weren't stored past the request and metadata was kept 30 days. The documents agree with each other, but content has no retention period and no subprocessors are named (18 of 30, was 20). No deprecation policy, and the terms say a provider may withdraw a model at any time. Removals are dated in the changelog, such as Gemma 4 31B delisted on 2 October, the same day (5 of 20). Upstream providers are named and Google Analytics is disclosed with its cookies and retention. Data locations aren't stated, and whether the MCP server sends telemetry wasn't read (10 of 20). 30 + 18 + 5 + 10 = 63.)