--- title: Postgres extensions can replace specialized databases... description: Postgres extensions can replace specialized databases like Elasticsearch, Pinecone, Redis, MongoDB, and InfluxDB using the same core algorithms (BM25, HNSW,... canonical: https://daily.dev/posts/postgres-extensions-can-replace-specialized-databases-like-elasticsearch-pinecone-redis-mongodb--8hexcfhfr twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Postgres extensions can replace specialized databases like Elasticsearch, Pinecone, Redis, MongoDB, and InfluxDB using the same core algorithms (BM25, HNSW, DiskANN). Managing one database instead of seven reduces operational complexity, eliminates data sync issues, simplifies AI agent testing, and cuts infrastructure overhead. Extensions like pg_textsearch, pgvector, pgvectorscale, TimescaleDB, and PostGIS provide full-text search, vector search, time-series, caching, document storage, and geospatial capabilities within Postgres. For 99% of companies, Postgres handles all database needs without the cognitive load, monitoring burden, and failure modes of database sprawl. | daily.dev og:description: Postgres extensions can replace specialized databases like Elasticsearch, Pinecone, Redis, MongoDB, and InfluxDB using the same core algorithms (BM25, HNSW,... og:url: https://daily.dev/posts/postgres-extensions-can-replace-specialized-databases-like-elasticsearch-pinecone-redis-mongodb--8hexcfhfr og:image: https://api.daily.dev/og/posts/8HEXcfhFR.png og:image:alt: Postgres extensions can replace specialized databases like Elasticsearch, Pinecone, Redis, MongoDB, and InfluxDB using the same core algorithms (BM25, HNSW, DiskANN). Managing one database instead of seven reduces operational complexity, eliminates data sync issues, simplifies AI agent testing, and cuts infrastructure overhead. Extensions like pg_textsearch, pgvector, pgvectorscale, TimescaleDB, and PostGIS provide full-text search, vector search, time-series, caching, document storage, and geospatial capabilities within Postgres. For 99% of companies, Postgres handles all database needs without the cognitive load, monitoring burden, and failure modes of database sprawl. 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. # Postgres extensions can replace specialized databases like Elasticsearch, Pinecone, Redis, MongoDB, and InfluxDB using the same core algorithms (BM25, HNSW, DiskANN). Managing one database instead of seven reduces operational complexity, eliminates data sync issues, simplifies AI agent testing, and cuts infrastructure overhead. Extensions like pg_textsearch, pgvector, pgvectorscale, TimescaleDB, and PostGIS provide full-text search, vector search, time-series, caching, document storage, and geospatial capabilities within Postgres. For 99% of companies, Postgres handles all database needs without the cognitive load, monitoring burden, and failure modes of database sprawl. **[Tech explorations](https://daily.dev/sources/techexplorations)** · [@danielpetrica](https://daily.dev/danielpetrica) · 1 upvotes · 0 comments ## Summary Postgres extensions can replace specialized databases like Elasticsearch, Pinecone, Redis, MongoDB, and InfluxDB using the same core algorithms (BM25, HNSW, DiskANN). Managing one database instead of seven reduces operational complexity, eliminates data sync issues, simplifies AI agent testing, and cuts infrastructure overhead. Extensions like pg_textsearch, pgvector, pgvectorscale, TimescaleDB, and PostGIS provide full-text search, vector search, time-series, caching, document storage, and geospatial capabilities within Postgres. For 99% of companies, Postgres handles all database needs without the cognitive load, monitoring burden, and failure modes of database sprawl. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Don’t just attend KubeCon \+ CloudNativeCon, Merge Forward your experience\!](https://daily.dev/posts/don-t-just-attend-kubecon-cloudnativecon-merge-forward-your-experience--l0rpp73x8) · CNCF · 0 upvotes · 0 comments - [Announcing H2 2026 KCDs](https://daily.dev/posts/announcing-h2-2026-kcds-m96goajm1) · CNCF · 1 upvotes · 0 comments - [Two months of Open Community Groups](https://daily.dev/posts/two-months-of-open-community-groups-asf52zhbs) · CNCF · 0 upvotes · 0 comments - [CNCF Unveils Schedule for KubeCon \+ CloudNativeCon Europe 2026](https://daily.dev/posts/cncf-unveils-schedule-for-kubecon-cloudnativecon-europe-2026-ikhcoa5cb) · CNCF · 2 upvotes · 0 comments - [CNCF Debuts KubeCon \+ CloudNativeCon Japan 2026 Schedule](https://daily.dev/posts/cncf-debuts-kubecon-cloudnativecon-japan-2026-schedule-xp5pyudub) · CNCF · 1 upvotes · 0 comments --- Tags: [#database](https://daily.dev/tags/database), [#postgresql](https://daily.dev/tags/postgresql), [#elk](https://daily.dev/tags/elk), [#vector-search](https://daily.dev/tags/vector-search) [View this post on daily.dev](https://daily.dev/posts/postgres-extensions-can-replace-specialized-databases-like-elasticsearch-pinecone-redis-mongodb--8hexcfhfr) ```json 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databases like Elasticsearch, Pinecone, Redis, MongoDB, and InfluxDB using the same core algorithms (BM25, HNSW, DiskANN).\n\nManaging one database instead of seven reduces operational complexity, eliminates data sync issues, simplifies AI agent testing, and cuts infrastructure overhead. \nExtensions like pg_textsearch, pgvector, pgvectorscale, TimescaleDB, and\n PostGIS provide full-text search, vector search, time-series, caching, document storage, and geospatial capabilities within Postgres.\n\nFor 99% of companies, Postgres handles all database needs without the cognitive load, monitoring burden, and failure modes of database sprawl.","text":"Shared: It’s 2026, Just Use Postgres","url":"https://daily.dev/posts/postgres-extensions-can-replace-specialized-databases-like-elasticsearch-pinecone-redis-mongodb--8hexcfhfr","datePublished":"2026-02-14T13:34:23.284Z","dateModified":"2026-02-14T13:34:23.287Z","author":{"@type":"Person","name":"Daniel, Petrica 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