pgvector vs Elasticsearch
Five of fourteen models named pgvector first on the direct prompt; zero named Elasticsearch. pgvector was named by fourteen of the fourteen models and Elasticsearch by eight and pgvector carries 73 labels and Elasticsearch 11, so the shares are not directly comparable.
Named in one category this edition.
Elasticsearch
accepted challengerNamed in eight categories this edition.
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
pgvectorFirst choices, of fourteen modelsElasticsearchAcross every category in the November 2026 Edition, pgvector and Elasticsearch were named in the same answer twenty-five times, of the 192 answers naming pgvector and the 86 naming Elasticsearch. In those answers Elasticsearch took the first choice one time and pgvector six.
Every model, every framing
| Model | DirectEL | ParaphraseEL | ComparativeEL | Budget-constrainedEL | Scale-constrainedEL | NegativeEL |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | ||
| GPT-5.4 mini | | | | | ||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | EL | EL | | | |
| Grok 4.20 | | | EL | | | |
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | | | EL | |
| Llama 4 Maverick | | EL | | | ||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | |||
| MiniMax M2.5 | | | | |||
| GPT-6 Luna | | | | | EL | |
| Muse Glimmer 30B | | | | | | |
pgvectorEL Elasticsearch first choice named as an alternative argued againstblank: not namedEach cell is one answer, pgvector on the left and Elasticsearch on the right.The direct prompt
pgvector first, Elasticsearch not the choice
5 of 14 modelsElasticsearch was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
1 of 14 modelsThe answer made no first choice from these two in this category.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
pgvector35%#1 of 9pgvector34%#2 of 11pgvector7%#4 of 13What 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
What the models said about Elasticsearch
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“1. Elasticsearch: Best for combining vector, full-text, and log search.” Llama 4 Maverick · paraphrase prompt · first choice
“Your existing search platform—for example, Elasticsearch—if you already use it and need lexical search, filters, and vector retrieval in one system.” GPT-6 Luna · scale prompt · alternative
“Elasticsearch / OpenSearch | Best if you already run it for search; good hybrid search capabilities” DeepSeek V4 Flash · scale prompt · alternative
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.