Vector databases · October 2026 Edition
Milvus vs MongoDB Atlas Vector Search
Zero of fourteen models named Milvus first on the direct prompt; zero named MongoDB Atlas Vector Search. Milvus was named by fourteen of the fourteen models and MongoDB Atlas Vector Search by eight and Milvus carries 41 labels and MongoDB Atlas Vector Search 12, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
First-choice share3%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate20%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#6#7A position in a field of 10; printed, not drawn.
Labels4112A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Milvus reading right to left. Rank and label count are printed, not drawn.Weaviate was named alongside these two in eleven of the fourteen direct answers. pgvector vs Milvus · pgvector vs MongoDB Atlas Vector Search · Pinecone vs Milvus
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the vector databases page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
MilvusFirst choices, of fourteen modelsMongoDB Atlas Vector Search
Direct001 against Milvus
Paraphrase01
Comparative101 against Milvus
Budget-constrained203 against Milvus
Scale-constrained00
Negative003 against Milvus · 1 against MongoDB Atlas Vector Search
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Milvus and MongoDB Atlas Vector Search stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | |||
| GPT-5.4 mini | | | ||||
| Gemini 3.5 Flash | | | | |||
| Perplexity Sonar | | | ||||
| Grok 4.1 Fast | | | | |||
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | | | ||
| Llama 4 Maverick | | |||||
| Qwen 3.7 Flash | | | | |||
| Kimi K2 | | |||||
| GLM 4.7 FlashX | | |||||
| MiniMax M2.5 | | | ||||
| GPT-6 Luna | | | ||||
| Muse Glimmer 30B | |
Milvus MongoDB Atlas Vector Search first choice named as an alternative argued againstblank: not namedEach cell is one answer, Milvus on the left and MongoDB Atlas Vector Search on the right.The direct prompt
The plain question, one answer per model, grouped by where Milvus and MongoDB Atlas Vector Search stood in it.
Neither was the first choice, one was named
2 of 14 modelsThe answer put something else first and named one of the two as an alternative.Qwen 3.7 FlashPinecone alternatives: MongoDB Atlas Vector Search, Weaviate
MiniMax M2.5Pinecone alternatives: Elasticsearch, Milvus, Weaviate
Neither was named
12 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Pinecone, Weaviate
GPT-5.4 miniPinecone alternatives: Qdrant, Weaviate
Gemini 3.5 Flashpgvector alternatives: Pinecone, Qdrant, Weaviate
Perplexity SonarQdrant, pgvector alternatives: Pinecone, Weaviate
Grok 4.1 Fastpgvector alternatives: Pinecone, Qdrant, Weaviate
Mistral Smallpgvector alternatives: pgvectorscale
DeepSeek V4 FlashQdrant, pgvector alternatives: Pinecone, Weaviate
Llama 4 Maverickpgvector
Kimi K2pgvector alternatives: Pinecone, Qdrant, Weaviate
GLM 4.7 FlashXQdrant, Weaviate alternatives: pgvector
GPT-6 LunaQdrant alternatives: Pinecone, pgvector
Muse Glimmer 30BPinecone, pgvector alternatives: Qdrant, Weaviate
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Level: the same share of first choices.
Milvus0%#7 of 7MongoDB Atlas Vector Search0%#– of 7Mid-marketThe figures above
Milvus leads by two points.
Milvus3%#6 of 10MongoDB Atlas Vector Search2%#7 of 10Enterprise
Milvus leads by nine points.
Milvus9%#3 of 10MongoDB Atlas Vector Search0%#– of 10What the models said about Milvus
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“heavy self-hosted systems like Milvus (requires Kubernetes know-how) ... neither is friendly to a limited budget” DeepSeek V4 Flash · budget prompt · hard negative
“Caution: If you run Milvus self-hosted, you must be on 2.4.24+, 2.5.21+ or 2.6.5+ and strip `sourceID` at the gateway” Muse Glimmer 30B · negative prompt · soft negative
“handles billions of vectors at lower cost; requires engineering resources... more resource-intensive to operate” Claude Haiku 4.5 · comparative prompt · soft negative
“likely to be an open-source option such as Milvus or Chroma, as they offer free or low-cost options” Llama 4 Maverick · budget prompt · first choice
“I'd recommend starting with either self-hosted Milvus/Qdrant (if you have DevOps capacity)” Claude Haiku 4.5 · budget prompt · first choice
“Massive Scale / Billions of vectors | Milvus” Qwen 3.7 Flash · comparative prompt · first choice
What the models said about MongoDB Atlas Vector Search
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“MongoDB Atlas gets pricing gripes in broader DB contexts” Grok 4.1 Fast · negative prompt · soft negative
“the sweet spot today is MongoDB Atlas Vector Search (if your data already lives in MongoDB)” GLM 4.7 FlashX · paraphrase prompt · first choice
“Stick with MongoDB Atlas if your existing data pipeline is already built around MongoDB documents.” Qwen 3.7 Flash · direct prompt · alternative
“Use pgvector or Atlas Vector Search” Kimi K2 · scale prompt · alternative
Also compared
pgvector vs Milvuspgvector vs MongoDB Atlas Vector SearchPinecone vs MilvusPinecone vs MongoDB Atlas Vector SearchQdrant vs MilvusQdrant vs MongoDB Atlas Vector SearchChroma vs MilvusChroma vs MongoDB Atlas Vector SearchWeaviate vs MilvusWeaviate vs MongoDB Atlas Vector SearchAll twenty-one head-to-head pages and the full vector databases standing
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.