Cloud Firestore development
A document database that syncs to the client and keeps working offline — with security enforced in rules rather than in your app.
Document database
Chosen per project
Maintainable handover
Firestore is at its strongest where a mobile app must keep working with a poor connection and catch up when the signal returns. Delivery apps, field data collection and chat all fall into that shape, and building that sync layer by hand is a serious piece of work.
The trade-off is modelling. Queries must be designed before the data is structured, because Firestore will not join for you, and billing is per document read — so a screen that reads a thousand documents costs a thousand reads every time it opens.
What we build with Firestore
Field and delivery apps that must work without a signal
Chat, notifications and live status screens
Rapid product builds where a backend team is not yet in place
Apps where several users watch the same data change live
Where it fits — and where it does not
Good fit when
Realtime and offline-first mobile applications
Small teams needing a backend without running servers
Data that is naturally document-shaped
Consider something else when
Reporting and aggregation across large collections
Highly relational data with many joins
Read-heavy screens where per-read billing adds up
What happens when the signal drops
The behaviour that justifies choosing Firestore in the first place.
Write locally
The app records the change and moves on.
Queue
Pending writes held durably on the device.
Reconnect
Changes sent as soon as the network returns.
Resolve
Server rules decide; listeners update every device.
How we work with Firestore
Model for the screens
Collections shaped around the queries each screen makes, with controlled duplication instead of joins.
Security rules as real code
Access enforced in rules and covered by tests — the client is never trusted to filter.
Aggregate deliberately
Counters and rollups maintained by functions so a dashboard does not read the whole collection.
Cost modelled early
Reads per screen estimated during design, because that, not storage, is what the bill is made of.
Our typical Firestore setup
| Concern | What we use |
|---|---|
| Modelling | Query-first collection design, controlled denormalisation |
| Security | Firestore rules with automated tests in CI |
| Offline | Native persistence in the mobile and web SDKs |
| Aggregation | Cloud Functions maintaining counters and summaries |
| Cost | Read counts estimated per screen, indexes kept lean |
Frequently asked questions
For document-shaped, screen-driven data, often yes. For accounting, inventory ledgers and anything needing multi-table consistency and ad-hoc reporting, a relational database remains the right tool.
By designing the read pattern, not the storage. Aggregates maintained by functions, pagination, and avoiding screens that read whole collections are what keep it predictable.
Yes, provided the security rules are treated as production code and tested. The failure mode is always permissive rules, not the platform.
Topics people search for
Services built with Firestore
Mobile App Development
Apps built to be used daily — fast to open, sensible offline and maintainable once they are in the stores.
Web Application Development
Applications that run in the browser, handle real workloads and hold up when several departments depend on them at once.
Custom Software Development
Software shaped around the way your business already works, instead of a packaged product you have to reorganise around.
SaaS Development
Building a software product means building a business system around it too — tenancy, billing, onboarding and support tooling.
Tell us what you are trying to build
Describe the problem in plain language and we will tell you what it would take to solve it — the approach, the moving parts and the sensible order to build them in. No obligation either way.