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Vector databases · October 2026 Edition

Qdrant vs MongoDB Atlas Vector Search

Four of fourteen models named Qdrant first on the direct prompt; zero named MongoDB Atlas Vector Search. Qdrant was named by fourteen of the fourteen models and MongoDB Atlas Vector Search by eight and Qdrant carries 70 labels and MongoDB Atlas Vector Search 12, so the shares are not directly comparable.

Qdrant

accepted challenger

Named in two categories this edition.

MongoDB Atlas Vector Search

accepted challenger

Named in one category this edition.

First-choice share22%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.

Negative rate4%8%Negative labels as a share of the product's labels, 0 to 100.

Rank in category#3#7A position in a field of 10; printed, not drawn.

Labels7012A count; the two differ.

The two percentage rows are drawn on one 0 to 100 track, Qdrant 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 Qdrant · pgvector vs MongoDB Atlas Vector Search · Pinecone vs Qdrant

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.

QdrantFirst choices, of fourteen modelsMongoDB Atlas Vector Search

Direct40

Paraphrase31

Comparative20

Budget-constrained60

Scale-constrained10

Negative203 against Qdrant · 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.

Across every category in the October 2026 Edition, Qdrant and MongoDB Atlas Vector Search were named in the same answer nineteen times, of the 213 answers naming Qdrant and the 20 naming MongoDB Atlas Vector Search. In those answers MongoDB Atlas Vector Search took the first choice one time and Qdrant four.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Qdrant 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

Qdrant

MongoDB Atlas Vector Search

first choice

named as an alternative

argued againstblank: not namedEach cell is one answer, Qdrant on the left and MongoDB Atlas Vector Search on the right.

The direct prompt

The plain question, one answer per model, grouped by where Qdrant and MongoDB Atlas Vector Search stood in it.

Qdrant first, MongoDB Atlas Vector Search not the choice

4 of 14 modelsMongoDB Atlas Vector Search was named in the answer but not as the choice, or not at all.

Perplexity SonarQdrant, pgvector alternatives: Pinecone, Weaviate

DeepSeek V4 FlashQdrant, pgvector alternatives: Pinecone, Weaviate

GLM 4.7 FlashXQdrant, Weaviate alternatives: pgvector

GPT-6 LunaQdrant alternatives: Pinecone, pgvector

Neither was the first choice, one was named

6 of 14 modelsThe answer put something else first and named one of the two as an alternative.

GPT-5.4 miniPinecone alternatives: Qdrant, Weaviate

Gemini 3.5 Flashpgvector alternatives: Pinecone, Qdrant, Weaviate

Grok 4.1 Fastpgvector alternatives: Pinecone, Qdrant, Weaviate

Qwen 3.7 FlashPinecone alternatives: MongoDB Atlas Vector Search, Weaviate

Kimi K2pgvector alternatives: Pinecone, Qdrant, Weaviate

Muse Glimmer 30BPinecone, pgvector alternatives: Qdrant, Weaviate

Neither was named

4 of 14 modelsThe answer made no first choice from these two in this category.

Claude Haiku 4.5Pinecone, Weaviate

Mistral Smallpgvector alternatives: pgvectorscale

Llama 4 Maverickpgvector

MiniMax M2.5Pinecone alternatives: Elasticsearch, Milvus, 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

Qdrant leads by seventeen points.

Qdrant#3 of 7

MongoDB Atlas Vector Search#– of 7

The full small business standing →

Mid-marketThe figures above

Qdrant leads by twenty points.

Qdrant#3 of 10

MongoDB Atlas Vector Search#7 of 10

The full mid-market standing →

Enterprise

Qdrant leads by nine points.

Qdrant#2 of 10

MongoDB Atlas Vector Search#– of 10

The full enterprise standing →

What the models said about Qdrant

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.

“The open source engine is strong technically, but self-hosting requires hardening defaults and active patching.” Muse Glimmer 30B · negative prompt · soft negative

“Qdrant \u2013 Known production issues ... Performance issues, GLIBC issues, steep learning curve” GLM 4.7 FlashX · negative prompt · soft negative

“Qdrant had a reported arbitrary-file-write issue involving its logger endpoint.” GPT-6 Luna · negative prompt · soft negative

“For most mid-market companies, pgvector or Qdrant represent the best balance of cost, capability, and practical feasibility” DeepSeek V4 Flash · direct prompt · first choice

“I would usually recommend Qdrant if you want a strong balance of production readiness, flexibility, and cost control” Perplexity Sonar · paraphrase prompt · first choice

“I'd recommend Qdrant for most mid-market B2B use cases because it appears most consistently favored for this segment” Perplexity Sonar · direct 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 Qdrant pgvector vs MongoDB Atlas Vector Search Pinecone vs Qdrant Pinecone vs MongoDB Atlas Vector Search Qdrant vs Chroma Qdrant vs Weaviate Qdrant vs Milvus Chroma vs MongoDB Atlas Vector Search Weaviate vs MongoDB Atlas Vector Search Milvus vs MongoDB Atlas Vector Search All 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.