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Index › IT › Data platform › Vector databases › Weaviate vs Elasticsearch
Vector databases · November 2026 Edition

Weaviate vs Elasticsearch

One of fourteen models named Weaviate first on the direct prompt; zero named Elasticsearch. Weaviate was named by fourteen of the fourteen models and Elasticsearch by eight and Weaviate carries 60 labels and Elasticsearch 11, so the shares are not directly comparable.

Weaviate

accepted challenger

Named in one category this edition.

Elasticsearch

accepted challenger

Named in eight categories this edition.

First-choice share5%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate12%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#6A position in a field of 11; printed, not drawn.
Labels6011A 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.Qdrant was named alongside these two in fourteen of the fourteen direct answers. Qdrant vs Weaviate · Qdrant vs Elasticsearch · 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.
WeaviateFirst choices, of fourteen modelsElasticsearch
Direct10
Paraphrase21
Comparative10
Budget-constrained004 against Weaviate
Scale-constrained00
Negative003 against Weaviate
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, Weaviate and Elasticsearch were named in the same answer twenty-eight times, of the 183 answers naming Weaviate and the 86 naming Elasticsearch. In those answers Elasticsearch took the first choice zero times and Weaviate one.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Weaviate and Elasticsearch stood in it.
ModelDirectELParaphraseELComparativeELBudget-constrainedELScale-constrainedELNegativeEL
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity SonarELEL
Grok 4.20EL
Mistral Small
DeepSeek V4 FlashEL
Llama 4 MaverickEL
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 LunaEL
Muse Glimmer 30B
WeaviateEL Elasticsearch first choice named as an alternative argued againstblank: not namedEach cell is one answer, Weaviate on the left and Elasticsearch on the right.

The direct prompt

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

Weaviate first, Elasticsearch not the choice

1 of 14 modelsElasticsearch was named in the answer but not as the choice, or not at all.
MiniMax M2.5Qdrant, Weaviate alternatives: Milvus, Pinecone, pgvector

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.
Claude Haiku 4.5Qdrant alternatives: MongoDB Atlas Vector Search, Pinecone, Weaviate, pgvector
GPT-5.4 miniPinecone alternatives: Qdrant, Weaviate, pgvector
Gemini 3.5 FlashPinecone, Qdrant alternatives: Weaviate, pgvector
Grok 4.20Qdrant alternatives: Pinecone, Weaviate, pgvector
Mistral SmallPinecone alternatives: Qdrant, Weaviate, pgvector
Llama 4 MaverickQdrant alternatives: Pinecone, Weaviate, pgvector
GLM 4.7 FlashXPinecone alternatives: Milvus, Qdrant, Weaviate
Muse Glimmer 30BPinecone, pgvector alternatives: Milvus, Qdrant, Weaviate

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
Perplexity Sonarpgvector alternatives: Pinecone, Qdrant
DeepSeek V4 Flashpgvector alternatives: Pinecone, Qdrant
Qwen 3.7 Flashpgvector alternatives: Pinecone, Qdrant
Kimi K2pgvector alternatives: Pinecone, Qdrant
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 four points.
Weaviate4%#4 of 9
Elasticsearch0%#– of 9
The full small business standing →
Mid-marketThe figures above
Weaviate leads by four points.
Weaviate5%#4 of 11
Elasticsearch2%#6 of 11
The full mid-market standing →
Enterprise
Weaviate leads by thirteen points.
Weaviate13%#3 of 13
Elasticsearch0%#10 of 13
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.

“What to Avoid on a Budget ... Weaviate — More complex to set up and resource-intensive” MiniMax M2.5 · budget prompt · hard negative
“Managed Weaviate — $25–45/month minimums plus hidden infrastructure costs” DeepSeek V4 Flash · budget prompt · hard negative
“Running a dedicated service (like Weaviate, Pinecone, or Qdrant) alongside your primary database increases infrastructure costs” Qwen 3.7 Flash · negative prompt · soft negative
“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
“For most mid-market B2B companies, Weaviate or Qdrant offer the best balance of cost, performance, and flexibility” MiniMax M2.5 · direct prompt · first choice
“we often recommend starting with a specialized open-source solution like Weaviate (using their cloud tier)” Qwen 3.7 Flash · paraphrase 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
Also compared
Qdrant vs WeaviateQdrant vs Elasticsearchpgvector vs Weaviatepgvector vs ElasticsearchPinecone vs WeaviatePinecone vs ElasticsearchWeaviate vs MilvusMilvus vs ElasticsearchAll fifteen 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.