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

Pinecone vs Elasticsearch

Five of fourteen models named Pinecone first on the direct prompt; zero named Elasticsearch. Pinecone was named by fourteen of the fourteen models and Elasticsearch by eight and Pinecone carries 65 labels and Elasticsearch 11, so the shares are not directly comparable.

Pinecone

accepted challenger

Named in one category this edition.

Elasticsearch

accepted challenger

Named in eight categories this edition.

First-choice share21%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate23%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#6A position in a field of 11; printed, not drawn.
Labels6511A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Pinecone 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 Pinecone · Qdrant vs Elasticsearch · pgvector vs Pinecone

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.
PineconeFirst choices, of fourteen modelsElasticsearch
Direct50
Paraphrase611 against Pinecone
Comparative50
Budget-constrained006 against Pinecone
Scale-constrained101 against Pinecone
Negative007 against Pinecone
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, Pinecone and Elasticsearch were named in the same answer thirty-four times, of the 219 answers naming Pinecone and the 86 naming Elasticsearch. In those answers Elasticsearch took the first choice one time and Pinecone thirteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Pinecone 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
PineconeEL Elasticsearch first choice named as an alternative argued againstblank: not namedEach cell is one answer, Pinecone on the left and Elasticsearch on the right.

The direct prompt

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

Pinecone first, Elasticsearch not the choice

5 of 14 modelsElasticsearch was named in the answer but not as the choice, or not at all.
GPT-5.4 miniPinecone alternatives: Qdrant, Weaviate, pgvector
Gemini 3.5 FlashPinecone, Qdrant alternatives: Weaviate, pgvector
Mistral SmallPinecone alternatives: Qdrant, Weaviate, pgvector
GLM 4.7 FlashXPinecone alternatives: Milvus, Qdrant, Weaviate
Muse Glimmer 30BPinecone, pgvector alternatives: Milvus, Qdrant, Weaviate

Neither was the first choice, one was named

9 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
Perplexity Sonarpgvector alternatives: Pinecone, Qdrant
Grok 4.20Qdrant alternatives: Pinecone, Weaviate, pgvector
DeepSeek V4 Flashpgvector alternatives: Pinecone, Qdrant
Llama 4 MaverickQdrant alternatives: Pinecone, Weaviate, pgvector
Qwen 3.7 Flashpgvector alternatives: Pinecone, Qdrant
Kimi K2pgvector alternatives: Pinecone, Qdrant
MiniMax M2.5Qdrant, Weaviate alternatives: Milvus, Pinecone, pgvector
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
Pinecone leads by nineteen points.
Pinecone19%#3 of 9
Elasticsearch0%#– of 9
The full small business standing →
Mid-marketThe figures above
Pinecone leads by twenty points.
Pinecone21%#3 of 11
Elasticsearch2%#6 of 11
The full mid-market standing →
Enterprise
Pinecone leads by forty-five points.
Pinecone45%#1 of 13
Elasticsearch0%#10 of 13
The full enterprise standing →

What the models said about Pinecone

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.

“Avoid for a limited budget: Pinecone — managed services with $50 minimums and costs that blow past initial estimates” DeepSeek V4 Flash · budget prompt · hard negative
“What to Avoid on a Tight Budget - Managed services like Pinecone (starts at ~$70/month for basics” Grok 4.20 · budget prompt · hard negative
“What to Avoid on a Budget - Pinecone — Excellent product but starts at ~$70/month” MiniMax M2.5 · budget prompt · hard negative
“The most widely adopted vector database in the mid-market, with a 57% adoption rate... If you want the most popular and easiest managed solution, choose Pinecone.” Mistral Small · direct prompt · first choice
“### 1. Pinecone (Best for Simplicity) ... Choose Pinecone if you prioritize operational simplicity and have modest data volumes” Claude Haiku 4.5 · paraphrase prompt · first choice
“Fully managed, zero ops overhead, serverless architecture | Rapid prototyping to production for teams wanting no infra management.” Gemini 3.5 Flash · scale 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 PineconeQdrant vs Elasticsearchpgvector vs Pineconepgvector vs ElasticsearchPinecone vs WeaviatePinecone vs MilvusWeaviate vs ElasticsearchMilvus 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.