10 Best Geospatial Databases of 2026

1. 1

   PostGIS

   An open-source extension that adds spatial storage, indexing and analysis to the PostgreSQL relational database.

   9.6overall

   An open-source extension that adds spatial storage, indexing and analysis to the PostgreSQL relational database.

   Best forGIS teams that want a free, standards-based spatial SQL database

   Adoption data

   57.9%PostGIS runs inside PostgreSQL, which 57.9% of respondents to the database question in the 2026 Stack Overflow Developer Survey, or 6,984 developers, said they had done extensive work with. The survey does not ask about PostGIS separately.

   Stack Overflow, 2026 Developer Survey: Databases (2026): “PostgreSQL 6,984 57.9%”

   PostGIS is the spatial extension for PostgreSQL, and it is the default choice for most GIS and mapping teams that need a database rather than a warehouse. The project describes support for points, lines, polygons and multi-geometries in 2D and 3D, spatial indexing, a wide range of spatial functions for distances, areas, intersections and buffers, raster storage for elevation and weather data, and geocoding and reverse geocoding. Its getting-started guide notes that cloud PostgreSQL services usually ship it already, so enabling it takes one CREATE EXTENSION statement. Desktop and server GIS tools such as QGIS, GeoServer, MapServer and ArcGIS connect to it directly. PostGIS is licensed under the GNU GPLv2, and the project's FAQ says ordinary use such as loading data and running queries does not trigger the GPL's sharing clauses. The tradeoff is operations: you manage PostgreSQL yourself or pay a hosting provider. We compared documented features and published prices.

   Strengths

   • +No license fee: PostGIS is published under the GNU GPLv2
   • +Enabled with one command, CREATE EXTENSION postgis, on most managed PostgreSQL services
   • +Sits on PostgreSQL, the most used database in the 2026 Stack Overflow survey at 57.9%

   Limitations

   • −You run and tune PostgreSQL yourself or pay a cloud provider for it
   • −Very large analytical scans are better suited to warehouses such as BigQuery or Snowflake
   • −No vendor support contract from the PostGIS project itself

   SummaryPostGIS is the best choice for GIS and mapping teams because it adds 2D and 3D geometry, raster data and spatial indexes to PostgreSQL at no license cost. Teams scanning billions of rows for analytics may prefer BigQuery or Snowflake.

2. 2

   Google BigQuery

   Google Cloud's serverless data warehouse, with a GEOGRAPHY data type and GoogleSQL geography functions for location analytics.

   8.7overall

   Google Cloud's serverless data warehouse, with a GEOGRAPHY data type and GoogleSQL geography functions for location analytics.

   Best forAnalytics teams querying very large location datasets on Google Cloud

   Adoption data

   6.7%In the 2026 Stack Overflow Developer Survey, 6.7% of respondents to the database question, or 809 developers, said they had done extensive work with BigQuery.

   Stack Overflow, 2026 Developer Survey: Databases (2026): “BigQuery 809 6.7%”

   BigQuery is the strongest pick here for teams whose location data already lives in a data warehouse or arrives in very large volumes, such as vehicle pings or transaction logs. Google's documentation describes a GEOGRAPHY data type and GoogleSQL geography functions for tasks such as tracking delivery vehicles and mapping customer transactions, plus guides for raster data, grid systems and loading Google Earth Engine data. The service is serverless, so there are no clusters to manage. Pricing is clear: on-demand queries are free for the first 1 TiB each month, then $6.25 per TiB scanned. There are limits worth knowing. Geography functions are available only in GoogleSQL, and only the Python client library supports the GEOGRAPHY type, so other clients must convert values with ST_ASTEXT or ST_ASGEOJSON. It is also an analytics engine, not a transactional store for map editing. We compared documented features and published prices.

   Strengths

   • +The first 1 TiB of queries each month is free
   • +Scales to warehouse-size tables without server management
   • +Google documents raster and Earth Engine workflows next to vector analysis

   Limitations

   • −Geography functions work only in GoogleSQL
   • −Only the Python client library supports the GEOGRAPHY type; other clients must convert to text
   • −On-demand billing by data scanned makes costs depend on query design

   SummaryGoogle BigQuery is the best choice for analysts running spatial SQL on very large tables because it is serverless and the first 1 TiB of queries each month is free. Teams that edit features in desktop GIS should keep a PostGIS database alongside it.

3. 3

   DuckDB (spatial extension)

   An in-process analytical database whose spatial extension reads GIS file formats and runs spatial SQL on a laptop.

   8.3overall

   An in-process analytical database whose spatial extension reads GIS file formats and runs spatial SQL on a laptop.

   Best forAnalysts working with GIS files and Parquet on a single machine

   Adoption data

   5.7%In the 2026 Stack Overflow Developer Survey, 5.7% of respondents to the database question, or 690 developers, said they had done extensive work with DuckDB.

   Stack Overflow, 2026 Developer Survey: Databases (2026): “DuckDB 690 5.7%”

   DuckDB is an in-process analytical database, and its spatial extension has become a quick way for analysts to query GIS files without standing up a server. After INSTALL spatial and LOAD spatial, the documented functions include ST_Read, which imports many geospatial file formats through the GDAL library, ST_ReadOSM for compressed OpenStreetMap .osm.pbf files, and ST_ReadSHP for Shapefiles. DuckDB itself reads and writes CSV, JSON, Parquet and Iceberg, locally or in object storage, so location data can be joined with other tables in one SQL query. DuckDB and its related projects are MIT-licensed and governed by the independent DuckDB Foundation, so there is no cost. Usage is growing: 5.7% of database respondents in the 2026 Stack Overflow survey had worked with it. It is a single-user engine, though, and it does not replace a shared database that many editors and web map servers connect to. The extension also is not autoloadable. We compared documented features and published prices.

   Strengths

   • +Free under the MIT license
   • +Runs in-process, so there is no server to set up
   • +Reads OpenStreetMap extracts and Shapefiles without a separate import tool

   Limitations

   • −The spatial extension is not autoloadable and must be loaded in each session
   • −Single-machine engine, not a shared multi-user GIS database
   • −Only 5.7% of 2026 Stack Overflow database respondents use DuckDB

   SummaryDuckDB is the best choice for analysts exploring GIS files on one machine because its free spatial extension reads GDAL formats, Shapefiles and OpenStreetMap extracts with SQL. Shared, multi-user mapping systems still need PostGIS or a cloud warehouse.

4. 4

   MongoDB Atlas

   A document database that stores locations as GeoJSON and queries them with 2dsphere and 2d geospatial indexes.

   8.3overall

   A document database that stores locations as GeoJSON and queries them with 2dsphere and 2d geospatial indexes.

   Best forApp developers adding location search to a document database

   Adoption data

   70,600MongoDB said in its second quarter fiscal 2027 results that more than 70,600 customers rely on the database.

   MongoDB, Inc., MongoDB, Inc. Announces Second Quarter Fiscal 2027 Financial Results (Exhibit 99.1) (2026): “Millions of developers and more than 70,600 customers across almost every industry—including approximately 75% of the Fortune 100—rely on MongoDB for their most important applications.”

   MongoDB is the practical pick for application teams that need location features such as store finders, delivery radius checks or geofences, rather than full GIS analysis. Its manual documents GeoJSON storage, 2dsphere indexes for spherical geometry and 2d indexes for flat coordinates, and query operators including $geoWithin, $geoIntersects, $near and $nearSphere, plus the $geoNear aggregation stage. Location data sits inside the same documents as the rest of the app record, so there is no second database to sync. MongoDB Atlas has a free cluster with 512 MB of storage, Flex clusters billed by usage between $8 and $30 a month, and Dedicated clusters from $56.94 a month. MongoDB reported more than 70,600 customers in its latest results. The limits are real for GIS work: there is no raster support, the function set is small next to PostGIS, and time series collections only support $geoNear for geospatial sorting. We compared documented features and published prices.

   Strengths

   • +Free forever Atlas cluster for learning and prototypes
   • +Flex clusters cost between $8 and $30 a month
   • +Location fields live inside the same documents as app data

   Limitations

   • −No raster support and far fewer spatial functions than PostGIS or Oracle Spatial
   • −Time series collections support only the $geoNear stage for geospatial sorting
   • −Not built for desktop GIS editing workflows

   SummaryMongoDB Atlas is the best choice for app developers adding location search because GeoJSON fields and 2dsphere indexes sit inside the documents they already store, with a free cluster to start. GIS analysts should use PostGIS instead.

5. 5

   Oracle Spatial

   Spatial features built into Oracle AI Database and Autonomous AI Database, covering vector, raster, LiDAR, network and geocoding data.

   8.3overall

   Spatial features built into Oracle AI Database and Autonomous AI Database, covering vector, raster, LiDAR, network and geocoding data.

   Best forEnterprises already running Oracle databases

   Adoption data

   9.4%In the 2026 Stack Overflow Developer Survey, 9.4% of respondents to the database question, or 1,135 developers, said they had done extensive work with Oracle. The survey does not ask about Oracle Spatial separately.

   Stack Overflow, 2026 Developer Survey: Databases (2026): “Oracle 1,135 9.4%”

   Oracle Spatial has the broadest feature set of any database in this list, and since it is part of Oracle AI Database, Oracle says it is included in all editions, database cloud services and Autonomous AI Database without additional licensing costs. The product page lists 2D geometries with hundreds of spatial functions, 3D LiDAR point clouds, raster imagery, network models, geocoding and routing, H3 hexagonal indexing and vector tiles for fast maps. Spatial Studio gives analysts a no-code way to build maps, and a natural language feature turns plain-English questions into spatial SQL. In Oracle Cloud, Autonomous AI Transaction Processing is billed from $0.336 per ECPU per hour. The catch is the platform cost: Oracle databases cost far more than open-source PostGIS, and the value is highest for organizations that already license Oracle. Oracle had 9.4% usage among database respondents in the 2026 Stack Overflow survey. We compared documented features and published prices.

   Strengths

   • +No separate spatial license on any Oracle AI Database edition
   • +Covers LiDAR, raster and network models that many databases leave out
   • +Natural language to SQL generates spatial queries from plain-language prompts

   Limitations

   • −The underlying Oracle database licenses and cloud ECPUs are costly next to free options
   • −Only 9.4% of 2026 Stack Overflow database respondents use Oracle
   • −Best features assume Oracle Cloud or Exadata infrastructure

   SummaryOracle Spatial is the best choice for enterprises already on Oracle because spatial, raster, LiDAR and routing features come with every Oracle AI Database edition at no added license cost. Teams starting fresh will spend less with PostGIS.

6. 6

   Esri ArcGIS Enterprise

   Esri's self-hosted GIS platform for mapping, analytics and spatial data management, deployed on-premises or in your own cloud.

   8.1overall

   Esri's self-hosted GIS platform for mapping, analytics and spatial data management, deployed on-premises or in your own cloud.

   Best forGovernment and enterprise GIS departments that standardize on Esri

   Adoption data

   700,000Esri says its ArcGIS software is used by over 700,000 customer organizations, including 70% of the largest global companies. The figure covers all of ArcGIS, not ArcGIS Enterprise alone.

   Esri, Company | About Esri (2026): “Users Over 700,000 customer organizations, including 70% of the largest global companies, most national governments, 30,000 cities and local governments, all 50 US states, and 12,000 universities”

   ArcGIS Enterprise is less a database than a complete self-hosted GIS platform, but for many government and utility GIS departments it is where location data is stored and managed. Esri describes mapping, analytics and data management that run on your own infrastructure, on-premises or in the cloud, with multiuser editing and data validation workflows, 2D and 3D maps, digital twins and connections to cloud data warehouses, databases and cloud storage. It can be configured for compliance standards such as HIPAA and GDPR, which matters for agencies with data sovereignty rules. Esri reports over 700,000 customer organizations across ArcGIS, including most national governments and 30,000 cities and local governments, though that figure is not specific to Enterprise. There is no public price list: licenses are sold by user type through Esri's sales team, so we score value conservatively. You also need administrators to run it, and it typically sits on top of a relational database. We compared documented features and published prices.

   Strengths

   • +Esri reports over 700,000 customer organizations using ArcGIS
   • +Full GIS stack: data management, analysis, mapping and apps in one platform
   • +Data stays on infrastructure you control

   Limitations

   • −No public price list; licensing goes through Esri sales
   • −Needs servers and administrators to run
   • −It is a GIS platform that uses an underlying database rather than a standalone database

   SummaryEsri ArcGIS Enterprise is the best choice for agencies that standardize on Esri because it bundles data management, multiuser editing and mapping on infrastructure they control. Teams that only need a spatial database will spend far less with PostGIS.

7. 7

   Snowflake

   A cloud data platform with native GEOGRAPHY and GEOMETRY data types and H3 functions for geospatial analytics.

   8.1overall

   A cloud data platform with native GEOGRAPHY and GEOMETRY data types and H3 functions for geospatial analytics.

   Best forData teams that already keep their warehouse in Snowflake

   Adoption data

   $1.49 billionSnowflake reported product revenue of $1.49 billion in the second quarter of fiscal 2027, up 37% year over year.

   Snowflake Inc., Snowflake Reports Financial Results for the Second Quarter of Fiscal 2027 (2026): “Product revenue of $1.49 billion in the second quarter, representing 37% year-over-year growth”

   Snowflake is the logical geospatial database for teams whose data already lives in it. The documentation describes two native types: GEOGRAPHY, which follows the WGS 84 standard and treats the Earth as a sphere, and GEOMETRY, which uses planar coordinates with a spatial reference ID. Snowflake accepts longitude and latitude pairs, WKT, WKB and GeoJSON, and recommends storing them in GEOGRAPHY columns for faster queries. It also provides SQL functions for H3, the hexagonal grid used for aggregating points, and the search optimization service can speed up spatial lookups. The limits matter for some GIS work: altitude is not supported, GEOMETRY is 2D only, and coordinates are rounded to 14 decimal places. Standard edition compute is $2.00 per credit, and there is no permanent free plan. Snowflake reported $1.49 billion of product revenue in its latest quarter. We compared documented features and published prices.

   Strengths

   • +Both spherical GEOGRAPHY and planar GEOMETRY types
   • +H3 functions for hexagon-based aggregation
   • +Spatial data sits next to the rest of the warehouse with no extra copy

   Limitations

   • −GEOGRAPHY does not support altitude, and GEOMETRY supports only 2D coordinates
   • −No free plan; compute bills from $2.00 per credit
   • −Coordinates are rounded to 14 decimal places

   SummarySnowflake is the best choice for data teams already on Snowflake because GEOGRAPHY, GEOMETRY and H3 functions let them query location data where it already sits. Teams that need 3D data or raster analysis should look at PostGIS or Oracle Spatial.

8. 8

   Microsoft SQL Server

   Microsoft's relational database, with geometry and geography spatial types and spatial indexes in SQL Server and Azure SQL.

   8.0overall

   Microsoft's relational database, with geometry and geography spatial types and spatial indexes in SQL Server and Azure SQL.

   Best forMicrosoft shops storing location data next to business records

   Adoption data

   27.1%In the 2026 Stack Overflow Developer Survey, 27.1% of respondents to the database question, or 3,270 developers, said they had done extensive work with Microsoft SQL Server.

   Stack Overflow, 2026 Developer Survey: Databases (2026): “Microsoft SQL Server 3,270 27.1%”

   SQL Server is the natural option for organizations that already run Microsoft data platforms and want location data next to their business records. Microsoft documents two spatial types: geometry for flat coordinate systems and geography for round-earth coordinates, both implemented as .NET CLR types, with spatial indexes and a documented nearest-neighbor query pattern. The same types work in Azure SQL Database, Azure SQL Managed Instance and Microsoft Fabric, though Fabric's SQL database cannot mirror spatial columns to OneLake. Microsoft also sponsors SQLServerSpatialTools, an open-source library of extra functions, which is a sign that the built-in function set is narrower than PostGIS. Express and Developer editions are free; Standard costs $73 per core per month on pay-as-you-go billing through Azure Arc, or $3,945 per 2-core pack. SQL Server had 27.1% usage among database respondents in the 2026 Stack Overflow survey. We compared documented features and published prices.

   Strengths

   • +Express edition is a free download
   • +Second most used database in the 2026 Stack Overflow survey after PostgreSQL and SQLite, at 27.1%
   • +Spatial types carry over to Azure SQL and Fabric

   Limitations

   • −Fewer spatial functions than PostGIS, with extras left to the SQLServerSpatialTools project
   • −Spatial data in Fabric SQL database cannot be mirrored to OneLake
   • −Enterprise licenses cost $15,123 per 2-core pack

   SummaryMicrosoft SQL Server is the best choice for Microsoft-centered teams because geometry and geography types work the same in SQL Server, Azure SQL and Fabric, and Express is free. Dedicated GIS teams will find more spatial functions in PostGIS.

9. 9

   Apache Sedona

   An Apache cluster computing framework for processing large-scale geospatial data on Spark, Flink and Snowflake.

   7.9overall

   An Apache cluster computing framework for processing large-scale geospatial data on Spark, Flink and Snowflake.

   Best forData engineers processing very large spatial datasets on Spark

   Adoption data

   2.4kThe apache/sedona repository on GitHub had 2.4k stars when checked in November 2026.

   GitHub, apache/sedona (2026): “Star 2.4k”

   Apache Sedona is not a database in the classic sense but a cluster computing framework for large-scale geospatial data, and it is the open-source choice when PostGIS runs out of room on one server. It adds spatial SQL, DataFrame APIs and raster functions to Apache Spark, with modules for Apache Flink and Snowflake and a newer SedonaDB engine. The documentation has install guides for Databricks, AWS EMR, AWS Glue, Microsoft Fabric and Azure Synapse, and readers for GeoParquet, GeoPackage, Shapefiles, GeoJSON and STAC catalogs. Recent releases added a GeoPandas-compatible API in 1.8.0 and KNN join in 1.7.0, and version 1.9.1 is the latest. Sedona is free under the Apache 2.0 license, though you pay for the compute it runs on. Its community is smaller than the databases here, at 2.4k GitHub stars, and it needs engineers comfortable with Spark. We compared documented features and published prices.

   Strengths

   • +Free under the Apache 2.0 license
   • +Runs on Spark platforms many data teams already use
   • +Handles both vector and raster data at cluster scale

   Limitations

   • −Needs a Spark or Flink cluster and engineers to run it
   • −Smaller community than the databases here, with 2.4k GitHub stars
   • −It is a processing framework, not a transactional database for map editing

   SummaryApache Sedona is the best choice for data engineers processing billions of spatial records on Spark because it adds spatial SQL, raster functions and GeoParquet support to clusters they already run, for free. Smaller teams will be better served by PostGIS or DuckDB.

10. 10

    SpatiaLite

    An open-source library that adds spatial SQL to SQLite, so a whole spatial database fits in one portable file.

    7.7overall

    An open-source library that adds spatial SQL to SQLite, so a whole spatial database fits in one portable file.

    Best forField and desktop users who need a portable single-file spatial database

    Adoption data

    42.1%SpatiaLite runs on SQLite, which 42.1% of respondents to the database question in the 2026 Stack Overflow Developer Survey, or 5,083 developers, said they had used extensively. The survey does not ask about SpatiaLite itself, so we score its adoption conservatively.

    Stack Overflow, 2026 Developer Survey: Databases (2026): “SQLite 5,083 42.1%”

    SpatiaLite extends SQLite with spatial SQL, which makes it the lightest option here: the whole spatial database is one file with no server, installation or configuration. The project describes it as mostly OGC-SFS compliant and roughly equivalent to PostgreSQL plus PostGIS, and it ships alongside spatialite-tools, a GUI and librasterlite2 for raster work. That makes it handy for field data collection, offline maps and handing a dataset to a colleague as a single file. It is free under a tri-license, letting you choose MPL 1.1, GPL v2.0 or later, or LGPL v2.1 or later. The drawbacks are maintenance and scale. The current version, 5.1.0, was released on 2023-08-04, development is credited to one maintainer, and SQLite is not built for many simultaneous writers. Adoption figures exist only for SQLite as a whole, which 42.1% of 2026 Stack Overflow database respondents use, so we scored SpatiaLite's adoption conservatively. We compared documented features and published prices.

    Strengths

    • +Free under a choice of MPL, GPL or LGPL
    • +One file can be copied between platforms
    • +The project positions it as roughly equivalent to PostgreSQL plus PostGIS

    Limitations

    • −Current version 5.1.0 was released on 2023-08-04, so updates are infrequent
    • −Maintained by a single named developer
    • −Not designed for many concurrent writers

    SummarySpatiaLite is the best choice for portable, offline spatial data because it puts spatial SQL into a single SQLite file with no server. Teams with many editors or heavy queries should use PostGIS.

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