--- title: Vector Database Comparison 2026: Pinecone vs Weaviate vs... description: Most teams evaluating vector databases should not stand up a second store if they already run OpenSearch or Elasticsearch, since k-NN there is just a mapping... canonical: https://daily.dev/posts/vector-database-comparison-2026-pinecone-vs-weaviate-vs-qdrant-vs-opensearch-ufform1ua twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Vector Database Comparison 2026: Pinecone vs Weaviate vs Qdrant vs OpenSearch | daily.dev og:description: Most teams evaluating vector databases should not stand up a second store if they already run OpenSearch or Elasticsearch, since k-NN there is just a mapping... og:url: https://daily.dev/posts/vector-database-comparison-2026-pinecone-vs-weaviate-vs-qdrant-vs-opensearch-ufform1ua og:image: https://api.daily.dev/og/posts/UffORM1Ua.png og:image:alt: Vector Database Comparison 2026: Pinecone vs Weaviate vs Qdrant vs OpenSearch og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Vector Database Comparison 2026: Pinecone vs Weaviate vs Qdrant vs OpenSearch **[BigData Boutique blog](https://daily.dev/sources/bigdataboutique)** · 13 min read · 17 upvotes · 4 comments ## Summary Most teams evaluating vector databases should not stand up a second store if they already run OpenSearch or Elasticsearch, since k-NN there is just a mapping change and a search pipeline. The post compares Pinecone, Weaviate, Qdrant, and OpenSearch on indexing, hybrid search, quantization, and filtering, plus turbopuffer, Amazon S3 Vectors, and WarpSearch as object-storage engines. Qdrant reports 0.98 recall@100 with 4x oversampling on OpenAI ada-002 embeddings. Pinecone's Standard plan charges $16-18 per million read units, and turbopuffer bills $1 per PB queried with a 1.28 GB minimum per query. Binary quantization can shrink a million 768-dim vectors from 3.07 GB down by up to 32x. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Do I need a separate vector database if I already run Elasticsearch or OpenSearch for search? Usually not. If OpenSearch or Elasticsearch is already running for logs or application search, adding k-NN is just a mapping change plus a search pipeline, reusing existing security, dashboards, and backups. A second dedicated vector store becomes a sync job that eventually disagrees with the first about which documents exist. A dedicated engine like Pinecone, Qdrant, or Weaviate makes sense mainly for pure-vector workloads with a small team and no search-engine features on the roadmap. _Weigh converged search versus a dedicated vector store the way you'd compare any architecture tradeoff, informed through daily.dev._ ### How much does Pinecone cost compared to Qdrant and turbopuffer for vector search? Pinecone's Standard plan charges $16-18 per million read units, $4-4.50 per million write units, and $0.33/GB-month storage, with a $50 monthly minimum. Qdrant Cloud bills hourly for compute, memory, and storage with no per-query metering and a free tier, so cost tracks capacity rather than traffic. turbopuffer bills $1 per PB queried, with each query counting the full namespace size (1.28 GB minimum), plus $0.33/GB-month stored, and a $16 monthly minimum. _Compare these pricing models side by side before committing budget to a vector database on daily.dev._ ### What is the difference between pre-filtering, post-filtering, and filter-aware traversal in vector search? Pre-filtering runs the predicate first and searches only the survivors, preserving recall but degrading on selective filters. Post-filtering searches first and drops non-matches, which silently loses recall. Filter-aware traversal evaluates the predicate inside the graph walk and is now standard: Pinecone has used single-stage filtering for years, Weaviate made ACORN the default in version 1.34, and OpenSearch applies efficient filtering across Lucene, Faiss, and JVector. _Track filtering strategy details like these on daily.dev when tuning recall for selective vector queries._ ## Community discussion Top comments from developers on daily.dev. **@pdfopsdev** · 7 upvotes > ran opensearch k-NN in prod for a while and the filtering story is the part that bites you, not indexing. pre-filter on a high-cardinality field and your recall tanks because the HNSW graph traversal doesn't know about your filter until after. qdrant's filterable HNSW actually handles this better out of the box, that's the real reason to stand up a second store sometimes, not raw recall numbers. **@swarnasharmin** · 1 upvotes > People rush to deploy standalone vector DBs when extending OpenSearch's existing k-NN pipeline handles 90% of production workloads fine. **@kartiknvj** · 0 upvotes > I've never seen filtered recall on a tenant_id that matches 0.1% of the corpus on any leaderboard either, and that's the query shape my multi-tenant RAG runs all day. For the migration replay, do you have a top-k overlap number you'd treat as safe to cut over, or does it get judged per workload? **@andychiare** · 0 upvotes > Among other things, this tool compares vector DBs: [https://agenticstack.sh/compare?category=vectordb](https://agenticstack.sh/compare?category=vectordb) ## Similar posts on daily.dev - [pgvector vs Pinecone vs Turbopuffer vs Qdrant \(2026\)](https://daily.dev/posts/pgvector-vs-pinecone-vs-turbopuffer-vs-qdrant-2026--m1dot7ras) · Alex CloudStar · 0 upvotes · 0 comments - [Weaviate vs. OpenSearch vs. pgvector](https://daily.dev/posts/weaviate-vs-opensearch-vs-pgvector-ant1kjxte) · DigitalOcean Community · 1 upvotes · 0 comments - [Pinecone Introduces Dedicated Read Nodes in Public Preview for Predictable Vector Workloads](https://daily.dev/posts/pinecone-introduces-dedicated-read-nodes-in-public-preview-for-predictable-vector-workloads-u5t4hn0b5) · InfoQ · 1 upvotes · 0 comments - [Top vector database alternatives for RAG pipelines](https://daily.dev/posts/top-vector-database-alternatives-for-rag-pipelines-kohnuwlhw) · Redis · 1 upvotes · 0 comments - [Top 5 Vector Databases for High-Performance LLM Applications](https://daily.dev/posts/top-5-vector-databases-for-high-performance-llm-applications-rv1svpxdf) · Machine Learning Mastery · 8 upvotes · 0 comments --- Tags: [#vector-search](https://daily.dev/tags/vector-search), [#opensearch](https://daily.dev/tags/opensearch), [#weaviate](https://daily.dev/tags/weaviate), [#pinecone](https://daily.dev/tags/pinecone), [#qdrant](https://daily.dev/tags/qdrant) [View this post on daily.dev](https://daily.dev/posts/vector-database-comparison-2026-pinecone-vs-weaviate-vs-qdrant-vs-opensearch-ufform1ua) ```json 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