Vector databases · October 2026 Edition
pgvector vs MongoDB Atlas Vector Search
Eight of fourteen models named pgvector first on the direct prompt; zero named MongoDB Atlas Vector Search. pgvector was named by fourteen of the fourteen models and MongoDB Atlas Vector Search by eight and pgvector carries 66 labels and MongoDB Atlas Vector Search 12, so the shares are not directly comparable.
pgvector
endorsed leaderNamed in one category this edition.
Named in one category this edition.
First-choice share35%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate6%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#7A position in a field of 10; printed, not drawn.
Labels6612A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, pgvector 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 Pinecone · pgvector vs Qdrant · pgvector vs Chroma
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.
pgvectorFirst choices, of fourteen modelsMongoDB Atlas Vector Search
Direct80
Paraphrase31
Comparative10
Budget-constrained80
Scale-constrained40
Negative704 against pgvector · 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 pgvector and MongoDB Atlas Vector Search stood in it.
| Model | DirectPG | ParaphrasePG | ComparativePG | Budget-constrainedPG | Scale-constrainedPG | NegativePG |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | PG | PG | PG | |||
| GPT-5.4 mini | PG | PG | ||||
| Gemini 3.5 Flash | PG | PG | PG | PG | PG | PG |
| Perplexity Sonar | PG | PG | PG | PG | ||
| Grok 4.1 Fast | PG | PG | PG | PG | PG | PG |
| Mistral Small | PG | PG | PG | |||
| DeepSeek V4 Flash | PG | PG | PG | PG | PG | PG |
| Llama 4 Maverick | PG | PG | PG | |||
| Qwen 3.7 Flash | | PG | PG | PG | ||
| Kimi K2 | PG | PG | PG | PG | PG | |
| GLM 4.7 FlashX | PG | PG | PG | PG | PG | |
| MiniMax M2.5 | PG | PG | PG | PG | ||
| GPT-6 Luna | PG | PG | PG | PG | PG | PG |
| Muse Glimmer 30B | PG | PG | PG |
PG pgvector MongoDB Atlas Vector SearchPG first choicePG named as an alternativePG argued againstblank: not namedEach cell is one answer, pgvector on the left and MongoDB Atlas Vector Search on the right.
MongoDB Atlas Vector SearchPG first choicePG named as an alternativePG argued againstblank: not namedEach cell is one answer, pgvector on the left and MongoDB Atlas Vector Search on the right.The direct prompt
The plain question, one answer per model, grouped by where pgvector and MongoDB Atlas Vector Search stood in it.
pgvector first, MongoDB Atlas Vector Search not the choice
8 of 14 modelsMongoDB Atlas Vector Search was named in the answer but not as the choice, or not at all.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
Muse Glimmer 30BPinecone, pgvector alternatives: Qdrant, Weaviate
Neither was the first choice, one was named
3 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
GLM 4.7 FlashXQdrant, Weaviate alternatives: pgvector
GPT-6 LunaQdrant alternatives: Pinecone, pgvector
Neither was named
3 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
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
pgvector leads by forty-nine points.
pgvector49%#1 of 7
MongoDB Atlas Vector Search0%#– of 7Mid-marketThe figures above
pgvector leads by thirty-four points.
pgvector35%#1 of 10
MongoDB Atlas Vector Search2%#7 of 10Enterprise
pgvector leads by seven points.
pgvector7%#5 of 10
MongoDB Atlas Vector Search0%#– of 10What the models said about pgvector
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“| Poor filtered search | pgvector, basic setups | ... | Reindexing locks | pgvector | Large HNSW rebuilds block reads/writes” Grok 4.1 Fast · negative prompt · soft negative
“pgvector when you expect very high vector-search throughput or huge scale without testing” GPT-6 Luna · negative prompt · soft negative
“Be cautious at massive scale ... you must be cautious of its ceilings” Gemini 3.5 Flash · negative prompt · soft negative
“Unless you have 2-3 people who can own this infrastructure, lean managed or existing DB extension first.... Already run Postgres heavily, <10M vectors | pgvector” Kimi K2 · scale prompt · first choice
“The best vector database for a mid-market B2B company is pgvector, which is a PostgreSQL extension. It is the strongest default for mid-market enterprises.” Llama 4 Maverick · direct prompt · first choice
“evaluating pgvector might save you the overhead of learning a new technology, as it handles vector search well within a relational environment” Qwen 3.7 Flash · scale 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 Pineconepgvector vs Qdrantpgvector vs Chromapgvector vs Weaviatepgvector vs MilvusPinecone vs MongoDB Atlas Vector SearchQdrant vs MongoDB Atlas Vector SearchChroma vs MongoDB Atlas Vector SearchWeaviate vs MongoDB Atlas Vector SearchMilvus 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.