Vector databases · October 2026 Edition
pgvector vs Qdrant
Eight of fourteen models named pgvector first on the direct prompt; four named Qdrant. Both were named by all fourteen models and pgvector carries 66 labels and Qdrant 70, so the shares are not directly comparable.
pgvector
endorsed leaderNamed in one category this edition.
Named in two categories this edition.
First-choice share35%22%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate6%4%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#3A position in a field of 10; printed, not drawn.
Labels6670A 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 Chroma · pgvector 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.
pgvectorFirst choices, of fourteen modelsQdrant
Direct84
Paraphrase33
Comparative12
Budget-constrained86
Scale-constrained41
Negative724 against pgvector · 3 against Qdrant
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, pgvector and Qdrant were named in the same answer 160 times, of the 179 answers naming pgvector and the 213 naming Qdrant. In those answers Qdrant took the first choice twenty-six times and pgvector sixty-three.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where pgvector and Qdrant 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 QdrantPG first choicePG named as an alternativePG argued againstblank: not namedEach cell is one answer, pgvector on the left and Qdrant on the right.
QdrantPG first choicePG named as an alternativePG argued againstblank: not namedEach cell is one answer, pgvector on the left and Qdrant on the right.The direct prompt
The plain question, one answer per model, grouped by where pgvector and Qdrant stood in it.
Both were the first choice
2 of 14 modelsThe answer named them together, and the judge labeled each a first choice.Perplexity SonarQdrant, pgvector alternatives: Pinecone, Weaviate
DeepSeek V4 FlashQdrant, pgvector alternatives: Pinecone, Weaviate
pgvector first, Qdrant an alternative
6 of 14 modelsQdrant was named in the answer but not as the choice, or not at all.Gemini 3.5 Flashpgvector alternatives: Pinecone, Qdrant, Weaviate
Grok 4.1 Fastpgvector alternatives: Pinecone, Qdrant, Weaviate
Mistral Smallpgvector alternatives: pgvectorscale
Llama 4 Maverickpgvector
Kimi K2pgvector alternatives: Pinecone, Qdrant, Weaviate
Muse Glimmer 30BPinecone, pgvector alternatives: Qdrant, Weaviate
Qdrant first, pgvector an alternative
2 of 14 modelspgvector was named in the answer but not as the choice, or not at all.GLM 4.7 FlashXQdrant, Weaviate alternatives: pgvector
GPT-6 LunaQdrant alternatives: Pinecone, pgvector
Neither was the first choice, one was named
1 of 14 modelsThe answer put something else first and named one of the two as an alternative.GPT-5.4 miniPinecone alternatives: Qdrant, Weaviate
Neither was named
3 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Pinecone, Weaviate
Qwen 3.7 FlashPinecone alternatives: MongoDB Atlas Vector Search, 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 thirty-two points.
pgvector49%#1 of 7
Qdrant17%#3 of 7Mid-marketThe figures above
pgvector leads by fourteen points.
pgvector35%#1 of 10
Qdrant22%#3 of 10Enterprise
The order flips: Qdrant leads at enterprise.
Qdrant9%#2 of 10pgvector7%#5 of 10
The full enterprise standing →What 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 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
Also compared
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.