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Vector databases · October 2026 Edition

Weaviate vs MongoDB Atlas Vector Search

Two of fourteen models named Weaviate first on the direct prompt; zero named MongoDB Atlas Vector Search. Weaviate was named by fourteen of the fourteen models and MongoDB Atlas Vector Search by eight and Weaviate carries 67 labels and MongoDB Atlas Vector Search 12, so the shares are not directly comparable.

Weaviate

accepted challenger

Named in one category this edition.

MongoDB Atlas Vector Search

accepted challenger

Named in one category this edition.

First-choice share5%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.

Negative rate10%8%Negative labels as a share of the product's labels, 0 to 100.

Rank in category#5#7A position in a field of 10; printed, not drawn.

Labels6712A count; the two differ.

The two percentage rows are drawn on one 0 to 100 track, Weaviate reading right to left. Rank and label count are printed, not drawn.Pinecone was named alongside these two in eleven of the fourteen direct answers. pgvector vs Weaviate · pgvector vs MongoDB Atlas Vector Search · Pinecone vs Weaviate

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.

WeaviateFirst choices, of fourteen modelsMongoDB Atlas Vector Search

Direct20

Paraphrase11

Comparative10

Budget-constrained002 against Weaviate

Scale-constrained001 against Weaviate

Negative004 against Weaviate · 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, Weaviate and MongoDB Atlas Vector Search were named in the same answer nineteen times, of the 203 answers naming Weaviate and the 20 naming MongoDB Atlas Vector Search. In those answers MongoDB Atlas Vector Search took the first choice one time and Weaviate zero.

Every model, every framing

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

Weaviate

MongoDB Atlas Vector Search

first choice

named as an alternative

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

The direct prompt

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

Weaviate first, MongoDB Atlas Vector Search not the choice

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

Claude Haiku 4.5Pinecone, Weaviate

GLM 4.7 FlashXQdrant, Weaviate alternatives: pgvector

Neither was the first choice, one was named

9 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

Perplexity SonarQdrant, pgvector alternatives: Pinecone, Weaviate

Grok 4.1 Fastpgvector alternatives: Pinecone, Qdrant, Weaviate

DeepSeek V4 FlashQdrant, pgvector alternatives: Pinecone, Weaviate

Qwen 3.7 FlashPinecone alternatives: MongoDB Atlas Vector Search, Weaviate

Kimi K2pgvector alternatives: Pinecone, Qdrant, Weaviate

MiniMax M2.5Pinecone alternatives: Elasticsearch, Milvus, Weaviate

Muse Glimmer 30BPinecone, pgvector alternatives: Qdrant, Weaviate

Neither was named

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

Mistral Smallpgvector alternatives: pgvectorscale

Llama 4 Maverickpgvector

GPT-6 LunaQdrant alternatives: Pinecone, pgvector

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

Weaviate leads by five points.

Weaviate#4 of 7

MongoDB Atlas Vector Search#– of 7

The full small business standing →

Mid-marketThe figures above

Weaviate leads by three points.

Weaviate#5 of 10

MongoDB Atlas Vector Search#7 of 10

The full mid-market standing →

Enterprise

Weaviate leads by seven points.

Weaviate#4 of 10

MongoDB Atlas Vector Search#– of 10

The full enterprise standing →

What the models said about Weaviate

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

“Weaviate's own security checklist says self-managed deployments allow anonymous queries by default unless you configure auth. That's fine for local dev, but risky for production.” GPT-5.4 mini · negative prompt · soft negative

“Weaviate Cloud if your costs need to be especially simple and predictable: Pricing can depend on vector dimensions...” GPT-6 Luna · negative prompt · soft negative

“*Proprietary (e.g., Pinecone, Weaviate Cloud):* Easier to start, harder to leave later (vendor lock-in).” Qwen 3.7 Flash · scale prompt · soft negative

“I'd recommend either Pinecone ... or Weaviate (if you want flexibility and cost control)” Claude Haiku 4.5 · direct prompt · first choice

“I'd suggest beginning with Weaviate or pgvector if you're already invested in PostgreSQL” Claude Haiku 4.5 · paraphrase prompt · first choice

“Weaviate (open‑source + managed cloud, built‑in multimodal & GraphQL APIs)” GLM 4.7 FlashX · 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 Weaviate pgvector vs MongoDB Atlas Vector Search Pinecone vs Weaviate Pinecone vs MongoDB Atlas Vector Search Qdrant vs Weaviate Qdrant vs MongoDB Atlas Vector Search Chroma vs Weaviate Chroma vs MongoDB Atlas Vector Search Weaviate vs Milvus 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.