A GIS and maps product,
that stays fast past a million points
A map with a thousand pins is easy. A map with a million stays fast only with real engineering: tiled layers, a search index, offline support. We built exactly that for an atlas of 1.9 million objects, and bring the same architecture to any GIS product.
Why a thousand points and a million points are different problems
A GIS and maps product is an interactive map built to stay fast and usable as the underlying dataset grows. That is a fundamentally different engineering problem at a thousand points than at a million. The difference is architectural. Tiled layers replace rendering every point directly. A real search index replaces visual-only browsing. A data pipeline ingests and updates sources without a developer touching code for every change. This is for any product, archive, logistics tool, real estate platform or research project where location is core to the data. It applies once the dataset is large enough that a basic map embed will not hold up.
What’s in the build
Tiled map layers, vector tiles generated with tools suited to large datasets, keep the map responsive whether it shows a thousand points or well over a million. They render only what is visible at the current zoom level, instead of every point at once. A real search index over the geospatial data lets a user find a specific point by name or attribute, instead of panning and zooming to spot it visually. Layer toggles for different data categories, time periods or statuses let a user filter the map to what matters to them right now.
Offline map support covers field use, since a map that only works with a live connection fails exactly the users who need it most out there. A data pipeline handles ingesting and updating sources: open data like OpenStreetMap and Wikidata, or your own proprietary dataset. It is built so updates do not require a full rebuild and redeploy. Admin tooling lets non-technical staff manage map data without filing a developer ticket for every correction.
How we build it
We design the tiling and data architecture around your actual expected scale from day one. Retrofitting tiled rendering onto a product built for rendering every point directly is a much larger rebuild than designing for scale from the start. Search indexing is built as a first-class feature, not an afterthought. A map with no real search is a map users give up on past a certain size.
Offline support gets tested under genuinely poor connectivity, not just airplane mode on a device that already cached everything moments earlier. This is close to the hardest version of this problem we have solved. An archaeological atlas with 1.9 million mapped objects, each with its own description pulled from Wikidata, OSM and national heritage registries. We kept it fast and navigable, including offline field use, with a Python data pipeline handling ingestion from multiple open-data sources.
We load-test the tiled rendering against your actual expected data volume plus a healthy margin. A map that performs well in a demo with sample data can behave very differently in full. The real dataset is often larger and messier than initial estimates. Weekly builds let your team explore the live map and try the search index throughout development, not just review static screenshots. We also test the offline mode under genuinely poor field conditions, not just airplane mode on a device that already cached everything moments before. That gap is where an offline feature either proves itself or quietly fails the people who need it most.
Timeline and price
| Tier | Price | What it covers |
|---|---|---|
| MVP | from $4,000 | Core flow, one platform or chain, ready to test with real users |
| Production | from $9,000 | Full feature set, handover docs, we keep running it with you |
| Full control (handover-ready) | from $10,000 | Same scope, built and documented for your own team to run with zero dependency on us |
Timeline runs 4 to 8 weeks for an MVP. Production builds typically run longer, depending on integrations.
What you own at the end
The application source code, the tile-generation pipeline, and the geospatial database all sit in your own infrastructure. Your underlying data, whether sourced from open data or proprietary, stays in a database you control. There is no dependency on a mapping SaaS that could change its pricing or API terms. Admin tooling for managing the data is yours to run independently.
Related
Part of our custom development work. See related builds: logistics tracking platform, fleet dispatch system, property management platform. On the technical side: interactive maps and gis layers, search engine infrastructure. Related case study. Ready to scope yours? Get in touch and we will send back a written plan with a fixed price.
FAQ
How much does a GIS or maps product cost?
An interactive map with tiled layers and search over a moderate dataset starts at $4,000. A fuller product with offline support, multiple data layers and admin tooling for non-technical updates runs $9,000 to $10,000.
How long does it take?
4 to 5 weeks for a map product on a defined, moderate-sized dataset. 6 to 8 weeks when offline support and multiple data layers with admin tooling are included.
What is the stack?
MapLibre or a similar tiling-capable map library, PMTiles or vector tiles generated with tools like tippecanoe for large datasets, and a geospatial-aware database (PostGIS). A data pipeline ingests sources like OSM, Wikidata or your own proprietary data.
Who owns the map data and the product?
You. The map data, tile generation pipeline and application source are all delivered in your repository and infrastructure, with no dependency on a proprietary mapping SaaS.
What support is included after launch?
30 days of fixes as real usage surfaces performance or data edge cases, plus a handover document on the tiling and data pipeline. Ongoing data updates and new layer additions are available as monthly work.