Protomaps Blog

What's new in PMTiles V3

PMTiles is a single-file archive format for map tiles, optimized for the cloud. Think about it like MBTiles, where the database can live on another computer or static storage like S3; or as a minimal alternative to Cloud Optimized GeoTIFFs for any tiled data - remote sensing readings, photographs, or vector GIS features.

Why adopt PMTiles? Companies like Felt, a collaborative mapmaking app, are using PMTiles for user-uploaded datasets - eliminating the need to run map tile servers at all.

Spec version 3

Read the specification on GitHub

In its first year of existence, PMTiles focused on being the simplest possible implementation of the HTTP Byte Range read strategy. PMTiles V3 is a revision that makes the retrieval and storage of tiles not just simple but also efficient. Minimizing archive size and the number of intermediate requests has a direct effect on the latency of tile requests and ultimately the end user experience of viewing a map on the web.

File Structure

  • 97% smaller overhead - Spec version 2 would always issue a 512 kilobyte initial request; version 3 reduces this to 16 kilobytes. What remains the same is that nearly any map tile can be retrieved in at most two additional requests.

  • Unlimited metadata - version 2 had a hard cap on the amount of JSON metadata of about 300 kilobytes; version 3 removes this limit. This is essential for tools like tippecanoe to store detailed column statistics. Essential archive information, such as tile type and compression methods, are stored in a binary header separate from application metadata.

  • Hilbert tile IDs - tiles internally are addressed by a single 64-bit Hilbert tile ID instead of Z/X/Y. See the blog post on Tile IDs for details.

  • Archive ordering - An optional clustered mode enforces that tile contents are laid out in Tile ID order.

  • Compressed directories and metadata - Directories used to fetch offsets of tile data consume about 10% the space of those in version 2. See the blog post on compressed directories for details.


  • Compression - The TypeScript pmtiles library now includes a decompressor - fflate - to allow reading compressed vector tile archives directly in the browser. This reduces the size and latency of vector tiles by as much as 70%.

  • Tile Cancellation - All JavaScript plugins now support tile cancellation, meaning quick zooming across many levels will interrupt the loading of tiles that are never shown. This has a significant effect on the perceived user experience, as tiles at the end of a animation will appear earlier.

  • ETag support - clients can detect when files change on static storage by reading the ETag HTTP header. This means that PMTiles-based map applications can update datasets in place at low frequency without running into caching problems.

Inspector app

PMTiles on GitHub now hosts an open source inspector for local or remote archives. View an archive hosted on your cloud storage (CORS required) - or drag and drop a file from your computer - no server required.


For raster tiles, there is first-class support for loading PNG or JPG image archives into Leaflet via the tiny (7 kilobytes!) PMTiles library like this:

const p = new pmtiles.PMTiles('example.pmtiles')

For vector tiles, you’ll need to use protomaps.js, the from-scratch renderer built for vector rendering and labeling using plain Canvas. It’s only about 32 kilobytes - a fraction of the size of an alternative like MapLibre GL JS - and now supports V3 archives.

MapLibre GL JS

The MapLibre protocol plugin has a new, simpler API; specifying the archive under a source url will automatically infer the archive’s minzoom and maxzoom.

"sources": {
    "example_source": {
        "type": "vector",
        "url": "pmtiles://",


pmtiles/python on GitHub

  • Python libraries are now modular and can have data sources swapped out. A PMTiles file can be read from disk, or a custom function can be provided to grab byte ranges from AWS via the boto library, Google Cloud, or any other blob data source.

  • Python command line utilities have been deprecated as the first-class tooling for creating and working with PMTiles.


go-pmtiles on GitHub

The greatest obstacle to adopting PMTiles for many users was the need to have a working Python 3 installation on your computer.

The official PMTiles tooling is now a single-file executable you can download at GitHub Releases.

Example for converting an MBTiles archive:

pmtiles convert input.mbtiles output.pmtiles

This will spit out some facts on the internals of your archive:

tippecanoe ne_10m_admin_1_states_provinces.geojsonseq -o ne_10m_admin_1_states_provinces.mbtiles -z8
pmtiles convert ne_10m_admin_1_states_provinces.mbtiles ne_10m_admin_1_states_provinces.pmtiles
# of addressed tiles:  40560
# of tile entries (after RLE):  20733
# of tile contents:  18933
Root dir bytes:  57
Leaves dir bytes:  53570
Num leaf dirs:  6
Total dir bytes:  53627
Average leaf dir bytes:  8928
Average bytes per addressed tile: 1.32
Finished in  444.930625ms

The above shows that the sample dataset - Admin 1 boundaries from Natural Earth has more than 50% redundant tiles. Although about 40,000 tiles are addresses by the archive, only 19,000 tiles are stored.

On average, only 1.3 bytes or 11 bits is needed per tile in the directory index after compression!

To upgrade your PMTiles V2 archive to V3:

pmtiles convert input_v2.pmtiles output_v3.pmtiles

Inspect a PMTiles V3 archive:

pmtiles show file://. output.pmtiles

Uploading your archive to cloud storage, once you’ve put your credentials in environment variables:

pmtiles upload LOCAL.pmtiles "s3://BUCKET_NAME?endpoint=" REMOTE.pmtiles


Free Downloads

Finally, you can download OpenStreetMap-derived, up-to-the minute basemap tilesets from, now only delivered in the V3 format. Small-area downloads are perfect for your hyper-local mapping project that will work forever, hosted on storage like GitHub Pages or S3.