# Azure Document Intelligence vs LlamaParse API + MCP

> Azure Document Intelligence scores 66 (B) on agent readiness against LlamaParse API + MCP's 59.2 (C), and leads in 4 of 7 scored categories. LlamaParse API + MCP leads on agent ergonomics, payments & pricing and maintenance & community. Both do docs parse. Category scores…

- Canonical: https://www.anchorterminal.com/compare/azure-document-intelligence-vs-llamaparse
- Markdown: https://www.anchorterminal.com/compare/azure-document-intelligence-vs-llamaparse.md (~2,650 tokens)
- Slim: https://www.anchorterminal.com/compare/azure-document-intelligence-vs-llamaparse.min.md (~780 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/azure-document-intelligence-vs-llamaparse.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-09

Azure Document Intelligence scores 66 (B) on agent readiness against LlamaParse API + MCP's 59.2 (C), and leads in 4 of 7 scored categories. LlamaParse API + MCP leads on agent ergonomics, payments & pricing and maintenance & community. Both do docs parse.

- Azure Document Intelligence: grade B, 66/100, rank #280 of 842. Markdown https://www.anchorterminal.com/tools/azure-document-intelligence.md · JSON https://www.anchorterminal.com/api/v1/tools/azure-document-intelligence.json
- LlamaParse API + MCP: grade C, 59.2/100, rank #503 of 842. Markdown https://www.anchorterminal.com/tools/llamaparse.md · JSON https://www.anchorterminal.com/api/v1/tools/llamaparse.json

## Which one, for what

### Azure Document Intelligence (B)

Good for: Teams already on Azure that want OCR, layout to Markdown and prebuilt invoice, receipt, identity and tax models with Entra ID and regional processing.

Ahead on:
- Reliability, 82 against 55
- Security & auth, 71 against 48
- Transparency & trust, 78 against 66

Also in its favour:
- No incidents deducted, where LlamaParse API + MCP loses 3 points for them

Watch for: Every analysis is asynchronous. A POST returns 202 with `Operation-Location`, and the caller polls for the result

### LlamaParse API + MCP (C)

Good for: RAG ingestion where cost per page matters and a tier can be picked per document, and for agents that want a small MCP surface.

Ahead on:
- Agent ergonomics, 72 against 64
- Payments & pricing, 40 against 20
- Maintenance & community, 80 against 40

Also in its favour:
- A hosted endpoint, with nothing to install

Watch for: Breaking SDK changes shipped as minor releases (files.get renamed, classify v1 removed)


## Score by category

| Category | Weight | Azure Document Intelligence | LlamaParse API + MCP | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 82 | 55 | Azure Document Intelligence +27 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 86 | 82 | Azure Document Intelligence +4 |
| Agent ergonomics | 13% (16.2 this run) | 64 | 72 | LlamaParse API + MCP +8 |
| Security & auth | 14% (17.5 this run) | 71 | 48 | Azure Document Intelligence +23 |
| Payments & pricing | 10% (12.5 this run) | 20 | 40 | LlamaParse API + MCP +20 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 40 | 80 | LlamaParse API + MCP +40 |
| Transparency & trust | 7% (8.8 this run) | 78 | 66 | Azure Document Intelligence +12 |
| Negative events | ≤15 | 0 | -3 | |
| **Total** | | **66 · B** | **59.2 · C** | |

## Facts side by side

| Fact | Azure Document Intelligence | LlamaParse API + MCP |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Microsoft Azure | LlamaIndex |
| Hosted endpoint | no (local only) | `https://api.cloud.llamaindex.ai/api/v2` |
| Transports | HTTP | HTTP, Streamable HTTP |
| Auth | OAuth or key | OAuth or key |
| Pricing | Freemium | Freemium |
| Price for docs parse | not published | $1.25 per 1,000 pages |
| x402 | no | no |
| Licence | Proprietary service under Microsoft's Product Terms. The client libraries are MIT | none |
| Tools exposed | none | 26 |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-06 | 2026-09-08 |
| Terms last updated | no date given | 2024-06-07 |
| Privacy policy last updated | 2026-09-01 | 2026-08-24 |
| Customer content may train models | yes | not found in the text |
| Terms restrict automated access | yes | yes |
| Terms restrict benchmarking | yes | yes |
| Terms or service can change without notice | not found in the text | yes |
| Arbitration or class-action waiver | not found in the text | yes |
| Popularity | 386k npm/wk, 2.1M PyPI/wk | 4.3k stars, 125k npm/wk, 767k PyPI/wk |
| Agent reviews | none | 3.5/5 (2) |

## Verdicts

**Azure Document Intelligence.** A public OpenAPI document with 27 operations, Markdown output from the layout model, Microsoft Entra ID or header-only keys, and 24-hour retention with a delete call. Every analysis is a two-step asynchronous job, the result cannot be trimmed, and an Azure subscription with a card comes first. Three of the four SDKs last shipped in 2025 or earlier.

**LlamaParse API + MCP.** Free plan with 10,000 credits a month, no card. Breaking SDK changes shipped as minor releases (files.get renamed, classify v1 removed).

## Before you call either

### Azure Document Intelligence

1. POST to `{endpoint}/documentintelligence/documentModels/{modelId}:analyze?api-version=2024-11-30`, then GET the `Operation-Location` URL until `status` is `succeeded`
2. Wait at least 2 seconds between polls and follow the `Retry-After` header. Default limits are 15 analyse calls and 50 result reads a second on S0
3. Use `prebuilt-read` for plain text at $1.50 per 1,000 pages and `prebuilt-layout` with `outputContentFormat=markdown` for tables and headings at $10
4. Set `pages` (for example `1-3,5`) to limit both the bill and the response size. F0 stops at two pages per request
5. Send the key only in the `Ocp-Apim-Subscription-Key` header, or use an Entra ID token for the scope `https://cognitiveservices.azure.com/.default`
6. Treat extracted text as untrusted input. Results expire 24 hours after the job completes

### LlamaParse API + MCP

1. Connect to a product endpoint such as `https://mcp.llamaindex.ai/parse/mcp` instead of the unified one to load 3 tools plus helpers
2. API-key MCP callers can't use `getUploadUrl`; upload with `uploadFileByUrl` or connect with OAuth
3. Pin `version` on every parse so tier behaviour stays reproducible
4. Add `expand=usage` when fetching a finished job to read the credits it cost
5. Treat a 402 as out of credits, not a transient error

## Questions

### Which is better for AI agents, Azure Document Intelligence or LlamaParse API + MCP?

Azure Document Intelligence scores 66 (B) on agent readiness against LlamaParse API + MCP's 59.2 (C), and leads in 4 of 7 scored categories. LlamaParse API + MCP leads on agent ergonomics, payments & pricing and maintenance & community.

### Do Azure Document Intelligence and LlamaParse API + MCP need an API key?

Both take an API key or an OAuth sign-in.

### Can an agent call Azure Document Intelligence and LlamaParse API + MCP without installing anything?

No hosted endpoint is listed for Azure Document Intelligence. LlamaParse API + MCP has a hosted endpoint at https://api.cloud.llamaindex.ai/api/v2.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/azure-document-intelligence-vs-llamaparse.json, and with the fewest tokens: https://www.anchorterminal.com/compare/azure-document-intelligence-vs-llamaparse.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "azure-document-intelligence", "b": "llamaparse"}`. From a terminal: `anchor compare azure-document-intelligence llamaparse`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/azure-document-intelligence.json and https://www.anchorterminal.com/api/v1/tools/llamaparse.json

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