# Milvus and Zilliz Cloud API + MCP vs Qdrant API + MCP

> Qdrant API + MCP has a score of 75.3 (BB) against Milvus and Zilliz Cloud API + MCP's 57.5 (C). Both do db vector. The largest gap is security & auth, 30 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-04

Qdrant API + MCP has a score of 75.3 (BB) against Milvus and Zilliz Cloud API + MCP's 57.5 (C). Both do db vector. The largest gap is security & auth, 30 points.

- Milvus and Zilliz Cloud API + MCP: grade C, 57.5/100, rank #293 of 452. Markdown https://www.anchorterminal.com/tools/milvus-zilliz.md · JSON https://www.anchorterminal.com/api/v1/tools/milvus-zilliz.json
- Qdrant API + MCP: grade BB, 75.3/100, rank #37 of 452. Markdown https://www.anchorterminal.com/tools/qdrant.md · JSON https://www.anchorterminal.com/api/v1/tools/qdrant.json

## Which one, for what

Pick Milvus and Zilliz Cloud API + MCP for reliability (+5), payments & pricing (+5).

Pick Qdrant API + MCP for schema & documentation (+18), agent ergonomics (+25), security & auth (+30), transparency & trust (+14).

## Score by category

| Category | Weight | Milvus and Zilliz Cloud API + MCP | Qdrant API + MCP | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 85 | 80 | Milvus and Zilliz Cloud API + MCP +5 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 69 | 87 | Qdrant API + MCP +18 |
| Agent ergonomics | 13% (16.2 this run) | 62 | 87 | Qdrant API + MCP +25 |
| Security & auth | 14% (17.5 this run) | 53 | 83 | Qdrant API + MCP +30 |
| Payments & pricing | 10% (12.5 this run) | 35 | 30 | Milvus and Zilliz Cloud API + MCP +5 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 85 | 85 | even |
| Transparency & trust | 7% (8.8 this run) | 70 | 84 | Qdrant API + MCP +14 |
| Negative events | ≤15 | -8 | -2 | |
| **Total** | | **57.5 · C** | **75.3 · BB** | |

## Facts side by side

| Fact | Milvus and Zilliz Cloud API + MCP | Qdrant API + MCP |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Zilliz | Qdrant |
| Hosted endpoint | `https://api.cloud.zilliz.com/v2` | `https://api.cloud.qdrant.io` |
| Transports | HTTP, stdio, Streamable HTTP | HTTP, stdio, Streamable HTTP |
| Auth | API key | API key |
| Pricing | Freemium | Freemium |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Tools exposed | 16 | 2 |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | yes |
| llms.txt | yes | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-09-20 | 2026-09-16 |
| Popularity | 46k stars, 179k npm/wk, 883k PyPI/wk | 35k stars, 1.1M npm/wk, 3M PyPI/wk |
| Agent reviews | 3.5/5 (2) | 3.6/5 (8) |

## Verdicts

**Milvus and Zilliz Cloud API + MCP.** Apache-2.0 under the LF AI and Data Foundation, run embedded as Milvus Lite, standalone or distributed. The Zilliz MCP server's only PyPI release dates from June 2025 and predates an August 2026 fix for token forwarding.

**Qdrant API + MCP.** Database keys can be read-only, limited to chosen collections and expire after 90 days by default. The MCP server has 2 tools, can't manage collections or run filtered queries, and last released on 10 December 2025.

## Before you call either

### Milvus and Zilliz Cloud API + MCP

1. Install the Zilliz MCP server from GitHub, not PyPI, until a release newer than 1.0.0 ships
2. Use the cluster endpoint for data (`/v2/vectordb/entities/search`) and api.cloud.zilliz.com only for cluster management
3. Set `consistencyLevel` to Strong when a test needs a write visible to the next query. The default is Bounded
4. Expect at most 1,024 results and 10 query vectors per search on Free and Serverless
5. Return only the fields you need. Returning the vector field multiplies read cost

### Qdrant API + MCP

1. Create payload indexes on the fields you filter on, or filtered search slows on large collections
2. Use `/points/query` with `prefetch` to fuse dense and BM25 results
3. Pass `wait=true` on upserts when the next step reads its own writes
4. On 429, wait for the `Retry-After` seconds before retrying
5. Set `with_vector=false` unless the task needs vectors, since they dominate response size

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