Expert Guide Series

How Do I Build My App to Handle Ten Times More Users?

A pet care booking app launched with two thousand users and worked perfectly, the response times were under a second and nobody complained about slow loading times. Six months later they ran a marketing campaign that brought in twenty thousand new users over a single weekend... and by Monday morning the app was timing out on basic searches, image uploads were failing completely, and their customer support inbox was filling up with angry messages. The company had to temporarily disable new registrations whilst they scrambled to fix their infrastructure, losing potential customers and damaging their reputation in the process.

Planning for growth isn't about predicting exact numbers, it's about building systems that can adapt when success arrives faster than expected.

Most app developers focus on getting their product launched and acquiring those first users, which makes sense because you need to validate your concept before spending money on infrastructure you might not need. The problem comes when you treat scaling as something you'll deal with later rather than designing for it from the start. You don't need to build for a million users on day one, but you do need to understand which parts of your architecture will break first when growth happens, and have a plan for addressing those bottlenecks before they bring your app down. Over the past ten years I've worked with dozens of apps that faced this exact situation, and the ones that handled growth successfully all had certain things in common when it came to how they built their backend systems and planned their infrastructure.

Understanding What Scalability Really Means for Mobile Apps

When people talk about scaling an app they often think it just means handling more users, but that's only part of the picture. Scalability means your app can handle increased load without degrading performance or requiring a complete rebuild of your systems. It means when you go from ten thousand to one hundred thousand users your costs increase proportionally (or less) rather than exponentially, and your team doesn't need to work around the clock just to keep the lights on. There are two types of scaling you need to think about and both matter for different reasons. Vertical scaling means making your existing servers more powerful by adding more memory or faster processors, which is the simpler option but eventually you hit physical limits and costs start climbing fast. Horizontal scaling means adding more servers to distribute the load across multiple machines, which requires more thoughtful architecture from the start but gives you much more room to grow without hitting hard limits. The biggest misconception I see is developers thinking they can just throw more server power at performance problems when they arise. Some problems can't be solved by bigger servers, especially ones related to how your database is structured or how your app makes network calls. A poorly designed database query will still be slow even on a powerful server, and an app that makes fifty separate API calls to load a single screen will struggle regardless of your backend infrastructure. This is one of many aspects worth considering when you're setting realistic growth targets for your mobile app.

Response time staying under 2 seconds even during peak usage periods
Database queries completing in under 500 milliseconds for typical requests
Server CPU usage staying below 70% during normal operation
Memory usage that doesn't continuously grow over time
Error rates staying below 1% of total requests

Building Your Backend Infrastructure to Support Growth

Your backend architecture needs to separate concerns from the beginning, which means breaking your system into distinct services that can scale independently. A monolithic architecture where everything runs in one large application makes it impossible to scale the parts that need it most... if your image processing is slowing down you end up scaling your entire application even though the database queries are running fine. Look at microservices architecture or at minimum a service-oriented approach where you split functionality into separate components. Your authentication service, database layer, media processing, and API endpoints should all be able to scale independently based on where the load actually sits. This doesn't mean you need dozens of separate services from day one, but you should structure your code so that separating components later doesn't require rewriting everything. Understanding these architectural decisions becomes especially important when considering whether to split your app into multiple products as you scale.

Start with a modular monolith that's organised into distinct logical services within a single codebase, then extract the highest-load components into separate services only when you have real usage data showing where the bottlenecks are.

Cloud platforms like AWS, Google Cloud, or Azure give you the tools to scale horizontally without buying physical servers, and they let you use managed services for databases, caching, and file storage that handle much of the scaling complexity for you. The trade-off is you become dependent on their infrastructure and costs can climb quickly if you're not careful about monitoring usage, but for most apps the flexibility is worth it compared to managing your own servers.

Service Type	Scaling Approach	Typical Bottleneck
API Servers	Horizontal (add more instances)	CPU during complex calculations
Database	Read replicas then sharding	Write operations and joins
Media Processing	Queue-based workers	Memory and processing time
File Storage	CDN distribution	Bandwidth and latency

Load balancers distribute incoming requests across multiple server instances so no single server gets overwhelmed, and they can automatically route traffic away from servers that are having problems. Most cloud platforms offer managed load balancing services that handle this complexity for you, and they're worth using even before you think you need them because they make scaling additional servers completely transparent to your app.

Database Design Decisions That Affect Your Scaling Ability

The way you structure your database from the beginning has a bigger impact on scaling than almost any other technical decision. Relational databases like PostgreSQL or MySQL work well for most apps, but they require careful schema design and index planning to perform well at scale... poorly designed tables with missing indexes can turn simple queries into operations that take seconds instead of milliseconds. Indexes speed up reads but slow down writes because the database needs to update the index every time you insert or modify data. You need indexes on any columns you filter or sort by regularly, and on foreign keys used in joins, but adding indexes to every column wastes space and hurts performance. I've seen databases where developers added indexes to every field "just in case" and ended up with write operations taking five times longer than necessary. Database normalisation reduces data duplication and keeps your schema clean, but joining multiple tables together on every query creates performance problems when you're dealing with millions of rows. Strategic denormalisation where you store some redundant data can eliminate expensive joins and speed up common queries dramatically. A product listing that includes the category name directly rather than requiring a join might feel wrong from a pure database design perspective, but it can cut your query time in half. This becomes particularly important when you're thinking about technical constraints that could affect your app's feasibility.

Add indexes to foreign keys and any columns used in WHERE clauses
Limit the number of joins in frequently-used queries to three or fewer
Store computed values that get accessed often rather than calculating on every request
Use connection pooling to reuse database connections instead of creating new ones
Implement read replicas to distribute query load across multiple database instances
Consider partitioning large tables by date or other logical boundaries

Read replicas let you send read queries to separate database instances whilst all writes still go to the primary database, which works well because most apps read data much more frequently than they write it. The primary database replicates changes to the read replicas with a small delay (typically under a second), which means you might occasionally show slightly outdated data but the performance gains are usually worth this trade-off. Eventually even read replicas won't be enough and you'll need to consider sharding, where you split your data across multiple database instances based on some logical division like user ID ranges or geographic regions. Sharding is complex because your application needs to know which database to query for any given request, and some queries that span multiple shards become extremely difficult, so don't implement it until you've exhausted other options.

Caching Strategies That Actually Work at Scale

Caching means storing the results of expensive operations so you can return them instantly the next time they're requested instead of recalculating or re-querying every time. It's one of the most effective ways to improve performance and reduce load on your databases and servers, but only if you implement it thoughtfully with clear rules about what gets cached and when it gets invalidated.

The two hardest problems in computer science are cache invalidation, naming things, and off-by-one errors.

Redis and Memcached are the most common caching systems and both work well, with Redis offering more features like data persistence and complex data structures whilst Memcached is simpler and slightly faster for basic key-value storage. For most apps Redis is the better choice because the extra features give you more flexibility as your caching needs become more sophisticated. You can cache at multiple levels and each one has different trade-offs. Database query results are the most common thing to cache and can reduce your database load by 80% or more for read-heavy applications, but you need to think carefully about invalidation so users don't see stale data. Computed values like user statistics or feed algorithms that take time to calculate should definitely be cached and can often be updated on a schedule rather than on every request. API responses to external services should be cached aggressively because you're paying for those API calls and they're often slower than your own systems. This approach is particularly valuable when implementing performance optimisation strategies that improve user experience. Cache warming means preloading your cache with data you know will be requested soon rather than waiting for users to trigger cache misses. If you're about to send a push notification to fifty thousand users about a new product, warm your cache with that product's data before the notification goes out so the first users to open the app don't experience slow loading whilst the cache populates. This is especially relevant when implementing effective push notification strategies for your mobile app.

When to Invalidate Your Cache

The hardest part of caching is knowing when cached data is no longer valid. Time-based expiration where cache entries automatically expire after a set period is the simplest approach and works well for data that doesn't change often or where showing slightly outdated information is acceptable. User profiles might cache for five minutes whilst product prices might only cache for thirty seconds depending on your business requirements. Event-based invalidation where you actively remove or update cache entries when the underlying data changes gives you the freshest data but requires more complex code to track what cache entries are affected by each change. When a product's price changes you need to invalidate not just the product detail cache but also any category listings or search results that include that product.

Managing Media Files and Content Delivery at Volume

Storing and serving images, videos, and other media files becomes a major bottleneck when your user base grows because these files are large and users expect them to load quickly. Serving media directly from your application servers is a terrible idea at any scale because it ties up server resources that should be handling API requests and wastes bandwidth that you're probably paying for. Content Delivery Networks (CDN) like Cloudflare, AWS CloudFront, or Fastly cache your media files on servers distributed around the world so users download them from a location geographically close to them. This reduces latency and takes the load completely off your application servers. Most CDNs charge based on bandwidth used which typically works out to a few pence per gigabyte, and the performance improvement is dramatic enough that you should use a CDN even when you're small.

File Type	Recommended Approach	Typical Storage Cost
User Profile Photos	CDN with aggressive caching	£0.02 per GB stored
Product Images	CDN with automatic resizing	£0.02 per GB stored
User-Generated Videos	Streaming service + CDN	£0.05 per GB stored
Document Downloads	Cloud storage with CDN	£0.02 per GB stored

Image optimisation becomes critical when you're serving millions of images per day. Users don't need the full 4000x3000 pixel original photo when they're viewing a thumbnail on a mobile screen, but many apps serve oversized images that waste bandwidth and make pages load slowly. Implement automatic image resizing where your system generates multiple versions of each uploaded image at different sizes, then serve the appropriate size based on where the image appears in your app. Modern image formats like WebP reduce file sizes by 30-40% compared to JPEG without visible quality loss, and newer formats like AVIF can save even more. The challenge is that not all devices support these formats yet, so you need to implement format detection where you serve WebP to browsers that support it and fall back to JPEG for older devices.

Handling User Uploads

When users upload files to your app they shouldn't upload directly to your application servers. Instead implement direct uploads to cloud storage like Amazon S3 or Google Cloud Storage where your server generates a temporary signed URL that gives the mobile app permission to upload directly to storage. This keeps large file uploads from consuming your server resources and makes uploads faster for users because they're writing directly to storage infrastructure designed for high throughput. Background processing for uploaded media is necessary because tasks like video transcoding or image analysis take too long to do during the upload request. Use a queue system where uploads trigger background jobs that process the media asynchronously, and update your database once processing completes. Users can see an "in progress" state whilst their video is being processed rather than waiting for the entire operation to complete before getting any response.

Load Testing Before You Need It

Load testing means deliberately putting stress on your systems to see how they perform under heavy load and identify bottlenecks before your real users experience them. Most developers skip this step completely until they have performance problems, but by then you're fixing issues whilst users are having a poor experience and you're probably losing revenue.

Run load tests that simulate 3-5 times your current peak traffic at least quarterly, and always before major marketing campaigns or feature launches that might drive sudden traffic spikes.

Tools like Apache JMeter, Gatling, or cloud-based services like Loader.io let you simulate thousands of concurrent users making requests to your app to see where things start to break down. You're looking for the point where response times start climbing, where error rates increase, or where your servers run out of memory or CPU capacity. The specific numbers matter less than understanding which component fails first and at what load level. Realistic test scenarios matter more than just hammering your servers with random requests. Map out typical user journeys like signing up, browsing products, adding items to a cart, and checking out, then create load tests that simulate those flows with realistic delays between actions. Your test data should include edge cases like users with hundreds of saved items or accounts with years of history, because these are often what expose scaling problems. This becomes particularly important when you're testing features before rolling them out to your user base.

Start with baseline testing at your current average load to establish performance benchmarks
Gradually increase concurrent users until you see performance degradation
Note which components or endpoints start failing first
Test sustained load over longer periods to catch memory leaks or gradual degradation
Simulate sudden traffic spikes to see how quickly your auto-scaling responds
Test with geographically distributed traffic if you serve multiple regions

The point where your app starts having problems is your current capacity ceiling, and you want to stay well below that threshold during normal operation. If problems start at 500 concurrent users and your peak traffic is 300, you have very little headroom and should focus on scaling before you grow further. If problems don't appear until 2000 concurrent users and your peak is 300, you have breathing room but should still understand what changes would be needed to scale beyond that.

Monitoring and Performance Tracking as You Grow

You can't fix performance problems you don't know about, and by the time users are complaining in app store reviews it's too late... you need monitoring systems that alert you to problems before they impact your users significantly. Basic server monitoring tells you CPU, memory, and disk usage, but application-level monitoring that tracks API response times, error rates, and user flows gives you the data you need to actually fix issues. Services like New Relic, Datadog, or open-source options like Prometheus with Grafana give you detailed insights into your application performance. Response time is one of the most telling metrics because it directly affects user experience... if your typical API response time starts climbing from 200ms to 800ms you have a problem developing even if nothing is technically broken yet. Error rates show you when things are actually failing rather than just slow, and tracking them by endpoint helps you identify which parts of your app are problematic. A 2% error rate might sound small but that's two failures for every hundred requests, which means many users are experiencing problems. Breaking down errors by type (timeouts, database errors, third-party API failures) helps you understand root causes and is critical when monitoring app crash rates to maintain quality standards.

API response times by endpoint with 95th percentile tracking
Error rates broken down by error type and affected endpoints
Database query performance with slow query logging
Cache hit rates to verify your caching is working effectively
Server resource usage including CPU, memory, and disk space
External API dependency response times and failure rates

Real user monitoring (RUM) tracks actual user experiences rather than synthetic tests from your monitoring service. Users in remote areas with poor connectivity have very different experiences than users on fast connections, and RUM data shows you what real people are experiencing. Mobile-specific monitoring tools can track app crashes, screen load times, and network request performance broken down by device type and operating system version. Set up alerts that notify your team when metrics cross defined thresholds rather than constantly watching dashboards. If your error rate exceeds 1% for more than five minutes, if response times climb above 2 seconds, or if server CPU stays above 80% for ten minutes, you need to know immediately. False alarms are annoying but missing a real issue that affects thousands of users is far worse. This monitoring becomes especially important when you're ready to start investing in paid user acquisition where performance issues can waste your marketing budget.

Conclusion

Building an app that can handle ten times more users isn't about predicting the future or spending money on capacity you don't need yet, it's about understanding where your current architecture will struggle and having a plan to address those limitations before they become emergencies. The apps that scale successfully are the ones where developers thought about growth from the beginning even if they didn't build for massive scale on day one. Start with solid foundations like proper database design with appropriate indexes, implement caching for expensive operations, use a CDN for media files, and separate your concerns so different parts of your system can scale independently. Run load tests regularly to understand your current capacity and watch your monitoring systems to catch problems early. These aren't complicated concepts but they require consistent attention and a willingness to invest time in infrastructure work that doesn't add visible features. The technical choices you make in your first few months of development will either enable growth or require expensive rewrites when success arrives. I've seen too many apps struggle because they treated scaling as something to worry about later, then found themselves unable to capitalise on marketing opportunities or viral growth because their systems couldn't handle the load.

If you're building an app and want to make sure your architecture can handle growth when it comes, or if you're already experiencing performance problems as your user base expands, get in touch and we can talk through your specific situation.

Frequently Asked Questions

Run load tests that simulate 3-5 times your current peak traffic and monitor where your system starts to break down. If your app handles the load without response times exceeding 2 seconds or error rates climbing above 1%, you have good headroom for growth.

Start with a modular monolith that's organised into distinct logical services within a single codebase, then extract only the highest-load components into separate services when you have real usage data. Building full microservices from day one adds unnecessary complexity before you understand where your actual bottlenecks will be.

Implement direct uploads to cloud storage like Amazon S3 using temporary signed URLs, then serve files through a CDN with automatic image resizing for different screen sizes. This approach costs around £0.02-0.05 per GB and keeps large file handling off your application servers.

With proper architecture, your costs should scale proportionally or sub-linearly with user growth rather than exponentially. If your costs are doubling every time you add 50% more users, you likely have architectural issues that need addressing before further growth.

Monitor your database query performance from the beginning and be concerned if typical queries take longer than 500ms or if you're seeing frequent slow query alerts. Implement read replicas when your database CPU consistently exceeds 70% during peak hours.

Track API response times (aim for 95th percentile under 2 seconds), error rates (keep below 1%), and server resource usage (CPU under 70% during normal operation). Set up alerts for when these metrics cross thresholds rather than waiting for user complaints.

Use time-based expiration for data that changes infrequently (user profiles cached for 5 minutes) and event-based invalidation for critical data like prices or inventory. The slight delay in showing updated information is usually worth the performance gains from reduced database load.

Yes, implement a CDN even when you're small because the performance improvement is dramatic and costs are typically just a few pence per gigabyte. Most CDN services have minimal monthly fees and the speed improvement for users downloading images or videos justifies the cost immediately.