Index › IT › Data platform › Vector databases › Qdrant vs pgvector
Vector databases · November 2026 Edition

Qdrant vs pgvector

Six of fourteen models named Qdrant first on the direct prompt; five named pgvector. Both were named by all fourteen models and both carry 73 labels, so the shares below are directly comparable.

Qdrant

endorsed leader

Named in one category this edition.

pgvector

endorsed leader

Named in one category this edition.

First-choice share34%34%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate1%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#2A position in a field of 11; printed, not drawn.
Labels7373Equal, which is what makes the shares comparable.
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.Pinecone was named alongside these two in fourteen of the fourteen direct answers. Qdrant vs Pinecone · Qdrant vs Weaviate · Qdrant 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.
QdrantFirst choices, of fourteen modelspgvector
Direct65
Paraphrase42
Comparative30
Budget-constrained611
Scale-constrained31
Negative321 against Qdrant · 6 against pgvector
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 November 2026 Edition, Qdrant and pgvector were named in the same answer 172 times, of the 215 answers naming Qdrant and the 192 naming pgvector. In those answers pgvector took the first choice fifty times and Qdrant forty-nine.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Qdrant and pgvector stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.20
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 pgvector first choice named as an alternative argued againstblank: not namedEach cell is one answer, Qdrant on the left and pgvector on the right.

The direct prompt

The plain question, one answer per model, grouped by where Qdrant and pgvector stood in it.

Qdrant first, pgvector an alternative

6 of 14 modelspgvector was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Qdrant alternatives: MongoDB Atlas Vector Search, Pinecone, Weaviate, pgvector
Gemini 3.5 FlashPinecone, Qdrant alternatives: Weaviate, pgvector
Grok 4.20Qdrant alternatives: Pinecone, Weaviate, pgvector
Llama 4 MaverickQdrant alternatives: Pinecone, Weaviate, pgvector
MiniMax M2.5Qdrant, Weaviate alternatives: Milvus, Pinecone, pgvector
GPT-6 LunaQdrant alternatives: Pinecone, pgvector

pgvector first, Qdrant an alternative

5 of 14 modelsQdrant was named in the answer but not as the choice, or not at all.
Perplexity Sonarpgvector alternatives: Pinecone, Qdrant
DeepSeek V4 Flashpgvector alternatives: Pinecone, Qdrant
Qwen 3.7 Flashpgvector alternatives: Pinecone, Qdrant
Kimi K2pgvector alternatives: Pinecone, Qdrant
Muse Glimmer 30BPinecone, pgvector alternatives: Milvus, 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.
GPT-5.4 miniPinecone alternatives: Qdrant, Weaviate, pgvector
Mistral SmallPinecone alternatives: Qdrant, Weaviate, pgvector
GLM 4.7 FlashXPinecone alternatives: Milvus, 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
pgvector leads by six points.
pgvector35%#1 of 9
Qdrant29%#2 of 9
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
Qdrant34%#1 of 11
pgvector34%#2 of 11
The full mid-market standing →
Enterprise
The order flips: Qdrant leads at enterprise.
Qdrant20%#2 of 13
pgvector7%#4 of 13
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. Four of five in this category shown.

“reported with a serious flaw in the same round up” Muse Glimmer 30B · negative prompt · soft negative
“Filtering is where Qdrant stands out — the database supports rich JSON-based filters that integrate with vector search efficiently. This makes it ideal for B2B use cases requiring complex filtering.” Claude Haiku 4.5 · direct prompt · first choice
“Qdrant consistently receives the most favorable reviews across testing ... If you need a dedicated vector database, it's often the safer choice.” DeepSeek V4 Flash · negative prompt · first choice
“Most teams should evaluate Qdrant (for performance and flexibility) or Weaviate (for AI-native features) as their primary options” GLM 4.7 FlashX · comparative prompt · first choice

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.

“pgvector does not natively support GPU acceleration or disk-optimized indexes, and at billion-scale workloads typically require more advanced sharding strategies” Claude Haiku 4.5 · negative prompt · soft negative
“Using vectors beside your application data in Postgres via pgvector is often the safest default until measured retrieval requirements force a dedicated engine.” Muse Glimmer 30B · negative prompt · soft negative
“pgvector for large, high-throughput workloads with selective filters or many tenants... approximate-index filtering can return too few results” GPT-6 Luna · negative prompt · soft negative
“the recommendation is to start with pgvector unless your scale, query patterns, or operational constraints provide specific evidence otherwise” Claude Haiku 4.5 · scale prompt · first choice
“Start simple: Use pgvector (in Postgres) or SQLite with vector extensions for most apps—it's battle-tested, avoids new tech debt” Grok 4.20 · negative prompt · first choice
“If your company already uses PostgreSQL for standard application data, pgvector is almost always the most cost-effective choice.” Gemini 3.5 Flash · budget 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.