# WhichDB

> Find the right database for what you're building. Smart matching, transparent rankings and honest profiles of every database — vector, relational, NoSQL, time-series, graph and more.

WhichDB is an independent, vendor-neutral guide to 800+ databases. Every database has a profile with its data model, features, scaling, consistency, deployment options, licensing, pricing, strengths, trade-offs and alternatives, each with cited sources. The WhichDB Score (0–100) ranks popularity from public adoption signals (package downloads, Hacker News, Wikipedia pageviews, job posts, GitHub stars and growth, Docker pulls, Stack Overflow); it measures adoption, not quality. Smart match ranks databases by fit for a plain-English project description. Nobody pays for placement.

## Key pages

- [Smart match](https://whichdb.com/find): describe a project in plain English and get databases ranked by fit, with reasons
- [Rankings](https://whichdb.com/rankings): the WhichDB Score leaderboard, filterable by data model, license and status
- [All databases](https://whichdb.com/databases): browse and filter the full catalog
- [Compare](https://whichdb.com/compare): side-by-side comparison of up to four databases
- [Methodology](https://whichdb.com/methodology): exactly how the score and smart match work, and where the data comes from

## Most popular databases

- [PostgreSQL](https://whichdb.com/postgresql): The extensible, standards-first open-source relational database. (Relational · open source (permissive) · #1 · score 93.2)
- [Redis](https://whichdb.com/redis): The in-memory data structure server that became the default cache, now with search, JSON and vectors built in. (Key-Value · open source (copyleft) · #2 · score 84.3)
- [MySQL](https://whichdb.com/mysql): The web's default open-source SQL database, now on calendar versioning with 9.7 as the LTS line. (Relational · open source (copyleft) · #3 · score 82.3)
- [MongoDB](https://whichdb.com/mongodb): The default document database: flexible BSON documents, rich queries and built-in sharding. (Document · source-available · #4 · score 80.5)
- [SQLite](https://whichdb.com/sqlite): The in-process SQL engine that ships in every phone, browser and OS: one file, zero servers. (Relational · open source (permissive) · #5 · score 75.5)
- [Elasticsearch](https://whichdb.com/elasticsearch): Lucene-based distributed search and analytics engine for text, vectors, logs and metrics. (Search Engine · open source (copyleft) · #6 · score 75.1)
- [Snowflake](https://whichdb.com/snowflake): Fully managed cloud data warehouse with separate storage and compute, now spanning AI and apps. (Columnar / OLAP · proprietary · #7 · score 74.4)
- [Microsoft SQL Server](https://whichdb.com/sql-server): Microsoft's flagship RDBMS: T-SQL, columnstore, Always On, and now native vectors and JSON. (Relational · proprietary · #8 · score 71.0)
- [Prometheus](https://whichdb.com/prometheus): CNCF-graduated pull-based monitoring system with a local TSDB, PromQL and alerting rules. (Time-Series · open source (permissive) · #9 · score 70.8)
- [ClickHouse](https://whichdb.com/clickhouse): Open-source columnar OLAP database known for very fast aggregations over billions of rows. (Columnar / OLAP · open source (permissive) · #10 · score 69.8)
- [Firebase](https://whichdb.com/firebase): Google's app platform: Firestore, Realtime Database and Data Connect, plus auth, functions and hosting. (Document · proprietary · #11 · score 65.4)
- [Google BigQuery](https://whichdb.com/google-bigquery): Google's serverless, pay-per-query data warehouse built on Dremel and Colossus. (Columnar / OLAP · proprietary · #12 · score 65.3)
- [DuckDB](https://whichdb.com/duckdb): In-process columnar SQL database: the SQLite of analytics, running inside your app or notebook. (Columnar / OLAP · open source (permissive) · #13 · score 63.7)
- [Amazon DynamoDB](https://whichdb.com/amazon-dynamodb): AWS's serverless key-value and document store with single-digit-millisecond reads at any scale. (Key-Value · proprietary · #14 · score 63.3)
- [Neo4j](https://whichdb.com/neo4j): The default property-graph database, with Cypher, a big ecosystem and a managed cloud. (Graph · open source (copyleft) · #15 · score 61.2)
- [Valkey](https://whichdb.com/valkey): The Linux Foundation's BSD-licensed fork of Redis, now the default engine on AWS and Google Cloud. (Key-Value · open source (permissive) · #16 · score 58.0)
- [Oracle Database](https://whichdb.com/oracle-database): The enterprise heavyweight: RAC, Data Guard and a converged engine for SQL, JSON, graph and vectors. (Relational · proprietary · #17 · score 56.9)
- [OpenSearch](https://whichdb.com/opensearch): Apache-licensed, foundation-governed fork of Elasticsearch for search, vectors and observability. (Search Engine · open source (permissive) · #18 · score 56.6)
- [MariaDB](https://whichdb.com/mariadb): Community-led MySQL fork with extra storage engines, built-in Galera and a faster feature cadence. (Relational · open source (copyleft) · #19 · score 56.2)
- [Apache Cassandra](https://whichdb.com/apache-cassandra): Masterless wide-column database built for always-on writes across data centers. (Wide-Column · open source (permissive) · #20 · score 55.9)
- [Amazon Redshift](https://whichdb.com/amazon-redshift): AWS's MPP columnar data warehouse, available as provisioned RA3 clusters or Serverless. (Columnar / OLAP · proprietary · #21 · score 55.8)
- [Algolia](https://whichdb.com/algolia): Hosted search-as-a-service with fast typo-tolerant search, merchandising and AI ranking. (Search Engine · proprietary · #22 · score 54.3)
- [Supabase](https://whichdb.com/supabase): Open-source Postgres backend platform: database, auth, storage, realtime and edge functions in one project. (Relational · open source (permissive) · #23 · score 54.3)
- [InfluxDB](https://whichdb.com/influxdb): Purpose-built time-series database, rebuilt in Rust on Arrow, DataFusion and Parquet for version 3. (Time-Series · open source (permissive) · #24 · score 51.0)
- [Memcached](https://whichdb.com/memcached): The original multi-threaded, dead-simple distributed memory cache for key-value blobs. (Key-Value · open source (permissive) · #25 · score 50.6)
- [Apache Spark SQL](https://whichdb.com/apache-spark-sql): Spark's SQL and DataFrame engine for large-scale batch, streaming and lakehouse processing. (Relational · open source (permissive) · #26 · score 50.3)
- [Trino](https://whichdb.com/trino): Fast distributed SQL query engine for federated analytics across lakes and databases. (Relational · open source (permissive) · #27 · score 49.9)
- [Google Cloud Firestore](https://whichdb.com/google-cloud-firestore): Serverless document database with realtime listeners, offline SDKs and a new Enterprise edition. (Document · proprietary · #28 · score 49.1)
- [Chroma](https://whichdb.com/chroma): Open-source retrieval database for AI apps, from embedded Python to serverless Chroma Cloud. (Vector · open source (permissive) · #29 · score 49.0)
- [RocksDB](https://whichdb.com/rocksdb): Meta's embeddable LSM-tree key-value engine, the storage layer under dozens of databases. (Key-Value · open source (permissive) · #30 · score 48.2)
- [Splunk](https://whichdb.com/splunk): Machine-data indexing and search platform with schema-on-read SPL queries, now owned by Cisco. (Search Engine · proprietary · #31 · score 48.1)
- [PostGIS](https://whichdb.com/postgis): The standard spatial extension for PostgreSQL: geometry, geography, rasters and topology in SQL. (Spatial · open source (copyleft) · #32 · score 47.5)
- [CockroachDB](https://whichdb.com/cockroachdb): Postgres-wire distributed SQL built to survive node, zone and region failures with serializable transactions. (Relational · source-available · #33 · score 47.1)
- [TimescaleDB](https://whichdb.com/timescaledb): PostgreSQL extension adding hypertables, columnar compression and continuous aggregates for time series. (Time-Series · source-available · #34 · score 47.1)
- [Milvus](https://whichdb.com/milvus): Cloud-native, distributed open-source vector database built for billions of vectors. (Vector · open source (permissive) · #35 · score 45.9)
- [ScyllaDB](https://whichdb.com/scylladb): C++ rewrite of Cassandra with shard-per-core design; also speaks the DynamoDB API. (Wide-Column · source-available · #36 · score 45.9)
- [Microsoft Access](https://whichdb.com/microsoft-access): Desktop database and forms builder bundled with Microsoft 365; still everywhere in back offices. (Relational · proprietary · #37 · score 45.8)
- [IBM Db2](https://whichdb.com/ibm-db2): IBM's enterprise RDBMS for Linux, UNIX, Windows and z/OS, with BLU columnar and pureScale clustering. (Relational · proprietary · #38 · score 45.5)
- [Azure Cosmos DB](https://whichdb.com/azure-cosmos-db): Microsoft's globally distributed, multi-API NoSQL and vector database with SLA-backed latency. (Document · proprietary · #39 · score 45.3)
- [Faiss](https://whichdb.com/faiss): Meta's C++/Python library for fast dense-vector similarity search and clustering, CPU and GPU. (Vector · open source (permissive) · #40 · score 44.7)
- [Apache CouchDB](https://whichdb.com/apache-couchdb): HTTP/JSON document database built around multi-master replication and offline-first sync. (Document · open source (permissive) · #41 · score 43.6)
- [Aerospike](https://whichdb.com/aerospike): Flash-optimized distributed key-value and document database for predictable sub-millisecond latency at scale. (Key-Value · open source (copyleft) · #42 · score 40.4)
- [ArangoDB](https://whichdb.com/arangodb): Native multi-model database: documents, graphs, search and vectors behind one query language. (Graph · source-available · #43 · score 40.0)
- [pgvector](https://whichdb.com/pgvector): The standard Postgres extension for vector similarity search with HNSW and IVFFlat indexes. (Vector · open source (permissive) · #44 · score 40.0)
- [Apache ZooKeeper](https://whichdb.com/apache-zookeeper): The original coordination service: hierarchical znodes, watches and ephemeral nodes for clusters. (Hierarchical · open source (permissive) · #45 · score 39.9)
- [Apache Solr](https://whichdb.com/apache-solr): The original Lucene search server: battle-tested, Apache-governed, now with GPU-accelerated vectors. (Search Engine · open source (permissive) · #46 · score 39.8)
- [LMDB](https://whichdb.com/lmdb): Tiny memory-mapped B+tree with ACID transactions and lock-free readers; reached 1.0 in 2026. (Key-Value · open source (permissive) · #47 · score 39.7)
- [SAP HANA](https://whichdb.com/sap-hana): SAP's in-memory columnar HTAP database, the mandatory engine under S/4HANA. (Relational · proprietary · #48 · score 39.7)
- [Consul KV](https://whichdb.com/hashicorp-consul-kv): The key-value store built into HashiCorp Consul for service config, locks and discovery. (Key-Value · source-available · #49 · score 39.0)
- [Apache Lucene](https://whichdb.com/apache-lucene): The Java search library at the heart of Elasticsearch, OpenSearch and Solr, now with HNSW vectors. (Search Engine · open source (permissive) · #50 · score 38.6)
- [Teradata Vantage](https://whichdb.com/teradata-vantage): The original MPP data warehouse, now VantageCloud with open table formats and in-database analytics. (Relational · proprietary · #51 · score 38.5)
- [MotherDuck](https://whichdb.com/motherduck): Serverless cloud data warehouse built on DuckDB, with hybrid local-plus-cloud execution. (Columnar / OLAP · proprietary · #52 · score 37.7)
- [Presto](https://whichdb.com/presto): Distributed SQL query engine from Meta for interactive queries over data lakes and many sources. (Relational · open source (permissive) · #53 · score 37.7)
- [FoundationDB](https://whichdb.com/foundationdb): Apple's ordered, strictly serializable distributed key-value store, built to have layers on top. (Key-Value · open source (permissive) · #54 · score 36.8)
- [Qdrant](https://whichdb.com/qdrant): Rust vector search engine with rich payload filtering, quantization and a managed cloud. (Vector · open source (permissive) · #55 · score 35.9)
- [TiDB](https://whichdb.com/tidb): MySQL-compatible distributed SQL with a row store for OLTP and a columnar replica for real-time analytics. (Relational · open source (permissive) · #56 · score 35.9)
- [PGlite](https://whichdb.com/pglite): Real PostgreSQL compiled to WebAssembly: an embeddable Postgres for browsers, Node, Bun and Deno. (Relational · open source (permissive) · #57 · score 35.1)
- [Apache Druid](https://whichdb.com/apache-druid): Real-time analytics database for sub-second slice-and-dice over streaming event data. (Columnar / OLAP · open source (permissive) · #58 · score 35.0)
- [Google Cloud Spanner](https://whichdb.com/google-cloud-spanner): Google's globally consistent, horizontally scaling SQL service with graph, vector and full-text built in. (Relational · proprietary · #59 · score 34.5)
- [QuestDB](https://whichdb.com/questdb): Columnar time-series SQL database built for market data and very high ingest rates. (Time-Series · open source (permissive) · #60 · score 34.1)

## Databases by data model

- [Relational databases](https://whichdb.com/category/relational): Tables, rows and joins with SQL. The default choice for transactional business data where integrity matters.
- [Document databases](https://whichdb.com/category/document): Self-describing JSON/BSON documents with flexible schemas. Great when your data is naturally hierarchical or evolves fast.
- [Key-Value databases](https://whichdb.com/category/key-value): The simplest model: values looked up by key. Powers caches, session stores and the storage engines inside other databases.
- [Wide-Column databases](https://whichdb.com/category/wide-column): Rows partitioned by key with flexible columns, built for massive write throughput across many nodes.
- [Graph databases](https://whichdb.com/category/graph): Nodes and relationships as first-class citizens. Ideal for traversals: fraud rings, recommendations, network analysis.
- [RDF / Triple Store databases](https://whichdb.com/category/rdf): Subject–predicate–object triples queried with SPARQL. The backbone of knowledge graphs and linked data.
- [Time-Series databases](https://whichdb.com/category/time-series): Optimised for append-heavy timestamped data with downsampling, retention and time-window queries.
- [Vector databases](https://whichdb.com/category/vector): Stores embeddings and answers nearest-neighbour queries. Core infrastructure for semantic search and RAG.
- [Search Engine databases](https://whichdb.com/category/search): Inverted indexes, relevance ranking, facets and typo tolerance for search experiences and log analytics.
- [Columnar / OLAP databases](https://whichdb.com/category/columnar): Column-oriented storage and vectorised execution for scanning and aggregating billions of rows quickly.
- [Object databases](https://whichdb.com/category/object): Persists programming-language objects directly, avoiding an object-relational mapping layer.
- [Spatial databases](https://whichdb.com/category/spatial): Geometry types, spatial indexes and GIS functions for location-aware applications.
- [Event Store databases](https://whichdb.com/category/event-store): Immutable streams of events as the source of truth, built for event sourcing and CQRS.
- [Ledger databases](https://whichdb.com/category/ledger): Immutable, verifiable histories and double-entry accounting with strict guarantees.
- [Array / Scientific databases](https://whichdb.com/category/array): Multi-dimensional arrays for scientific, imaging, genomics and geospatial raster data.
- [XML databases](https://whichdb.com/category/xml): Native XML documents queried with XPath/XQuery, common in publishing and regulated industries.
- [Hierarchical databases](https://whichdb.com/category/hierarchical): Parent–child record trees, the original mainframe database model still running critical systems.
- [Multivalue (Pick) databases](https://whichdb.com/category/multivalue): Pick-style records with multi-valued fields, a long-lived niche in ERP and vertical software.
- [Network / Navigational databases](https://whichdb.com/category/navigational): CODASYL network model with record sets and pointers, found on mainframes.

## Databases by use case

- [Databases for Transactional apps (OLTP)](https://whichdb.com/use-case/oltp): Many small reads and writes with transactions: the backbone of most applications.
- [Databases for Analytics (OLAP)](https://whichdb.com/use-case/olap): Large scans and aggregations over historical data for BI and reporting.
- [Databases for Hybrid (HTAP)](https://whichdb.com/use-case/htap): Transactions and analytics on the same fresh data without ETL.
- [Databases for Real-time analytics](https://whichdb.com/use-case/real-time-analytics): Sub-second queries over streaming, constantly-arriving data for user-facing dashboards.
- [Databases for Data warehouse](https://whichdb.com/use-case/data-warehouse): Central store for analytics across the business, usually SQL and columnar.
- [Databases for Lakehouse](https://whichdb.com/use-case/lakehouse): Warehouse-style SQL directly over open table formats (Iceberg, Delta) on object storage.
- [Databases for Stream processing](https://whichdb.com/use-case/stream-processing): Continuous queries and incrementally maintained views over event streams.
- [Databases for Caching](https://whichdb.com/use-case/caching): Keep hot data in memory in front of a slower system of record.
- [Databases for Session store](https://whichdb.com/use-case/session-store): Fast, expiring per-user state such as sessions, carts and rate limits.
- [Databases for Queues & jobs](https://whichdb.com/use-case/queue): Durable work queues and background job processing.
- [Databases for Pub/Sub messaging](https://whichdb.com/use-case/pub-sub): Broadcast messages to subscribers in real time.
- [Databases for Full-text search](https://whichdb.com/use-case/full-text-search): Keyword search with ranking, stemming, facets and typo tolerance.
- [Databases for Vector search](https://whichdb.com/use-case/vector-search): Similarity search over embeddings for semantic search and recommendations.
- [Databases for RAG & AI memory](https://whichdb.com/use-case/rag): Retrieval for LLM apps: embeddings, hybrid search, filtering and agent memory.
- [Databases for Time-series data](https://whichdb.com/use-case/time-series): Metrics, sensor readings and events indexed by time.
- [Databases for Observability](https://whichdb.com/use-case/observability): Metrics, logs and traces at high ingest rates with retention policies.
- [Databases for IoT & telemetry](https://whichdb.com/use-case/iot): Huge fleets of devices emitting telemetry, often with edge ingestion.
- [Databases for Graph traversal](https://whichdb.com/use-case/graph-traversal): Multi-hop relationship queries in milliseconds (friends-of-friends, paths).
- [Databases for Graph analytics](https://whichdb.com/use-case/graph-analytics): Whole-graph algorithms: PageRank, community detection, centrality.
- [Databases for Knowledge graph](https://whichdb.com/use-case/knowledge-graph): Linked, semantically rich entities, ontologies and reasoning.
- [Databases for Geospatial](https://whichdb.com/use-case/geospatial): Location queries, geofencing, routing and GIS.
- [Databases for Embedded / in-process](https://whichdb.com/use-case/embedded): Runs inside your application process with no server to operate.
- [Databases for Edge & serverless](https://whichdb.com/use-case/edge): Data close to users, accessible from edge functions with low latency.
- [Databases for Mobile & offline](https://whichdb.com/use-case/mobile-offline): On-device storage that keeps working offline and syncs later.
- [Databases for Local-first sync](https://whichdb.com/use-case/local-first): Client-side database as the source of truth with real-time multi-device sync.
- [Databases for Event sourcing](https://whichdb.com/use-case/event-sourcing): Immutable event logs as the system of record, with projections.
- [Databases for Ledger & accounting](https://whichdb.com/use-case/ledger): Financial transactions with strict, auditable correctness.
- [Databases for ML feature store](https://whichdb.com/use-case/ml-feature-store): Low-latency feature serving and offline feature computation for ML.
- [Databases for Content & catalogs](https://whichdb.com/use-case/content-management): Product catalogs, CMS content and user-generated data.
- [Databases for Metadata & config](https://whichdb.com/use-case/metadata-store): System metadata, service discovery and small critical state.
- [Databases for Coordination](https://whichdb.com/use-case/configuration): Distributed locks, leader election and consistent configuration.
- [Databases for Scientific data](https://whichdb.com/use-case/scientific): Arrays, genomics, imaging and simulation outputs.
- [Databases for Financial tick data](https://whichdb.com/use-case/financial-tick-data): High-frequency market data with time-series analytics.

## Machine-readable data

- [Catalog index (JSON)](https://whichdb.com/data/index.json): every database with model, license, deployment, features, score and rank
- Full profile (JSON): https://whichdb.com/data/db/{slug}.json, for example https://whichdb.com/data/db/postgresql.json

## Optional

- [All 844 profiles, condensed](https://whichdb.com/llms-full.txt)