What should I use for geospatial data API?

It depends on what you need the API to do. For geospatial data, the “best” choice usually comes down to data type, scale, and workflow.

Common good choices

1. PostGIS + REST API

• Best for: custom geospatial apps, spatial queries, filtering, analytics
• Why: very powerful SQL spatial functions, mature, widely used
• Typical stack: PostgreSQL + PostGIS + FastAPI/Django/Node

2. GeoServer

• Best for: serving GIS data through standard geospatial protocols
• Why: supports WMS, WFS, WCS, WMTS, easy to publish layers
• Good if you need interoperability with GIS tools like QGIS/ArcGIS

3. Apache Sedona / GeoMesa

• Best for: big geospatial data, distributed processing
• Why: built for Spark/Hadoop ecosystems and large-scale spatial analytics

4. Vector tile API / tile server

• Best for: map visualization at scale
• Why: faster map rendering for web maps
• Examples: Tegola, TileServer GL, Martin, pg_tileserv

5. Cloud-native geospatial services

• Best for: managed infrastructure and easy scaling
• Options:
  • Google Earth Engine for remote sensing/earth observation
  • AWS Open Data + Athena/Geospatial
  • BigQuery GIS for analytics
  • Snowflake GIS if you're already on Snowflake

Quick recommendation

• If you’re building an app with spatial search, filtering, and CRUD: PostGIS + a web API
• If you need standards-based map service: GeoServer
• If you need massive scale analytics: Sedona/GeoMesa
• If you need fast browser maps: vector tiles

If you want a safe default

For most projects, I’d start with: PostgreSQL + PostGIS + FastAPI and add vector tiles later if you need map rendering performance.

If you tell me:

1. your data type (points/lines/polygons/rasters),
2. whether it’s for web maps or analytics,
3. expected data volume, I can recommend a specific stack.