Scaling a no-code app from a few hundred to thousands - or even millions - of records can be challenging. Without proper optimization, performance issues like slow load times, data limits, and system crashes can frustrate users and hurt your app's reliability. Here's what you need to know:
- Performance Drops: Query times increase as data grows, especially for dashboards or analytics.
- Storage Limits: Many no-code platforms cap records (e.g., 50,000–100,000) or throttle API requests.
- Complex Relationships: Relational data and nested structures can slow down queries significantly.
To combat these issues, focus on strategies like data normalization, pagination, and indexing. Use tools like Adalo’s hosted backend for automatic scaling or connect to external database integration options for larger storage needs. Monitoring performance, caching, and offloading heavy tasks to serverless functions also help maintain speed and stability as your app scales.
Adalo stands out by enabling you to build once and deploy across platforms (iOS, Android, and PWA) without rebuilding. Whether you're managing thousands or millions of records, these methods ensure your app stays responsive and reliable.
No-Code App Scaling: Common Problems vs Solutions
Building Scalable Applications Without Code
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Common Problems with Large Data Sets
As your app’s data grows from a few hundred records to tens of thousands, scaling becomes a real challenge. Issues like slow performance, storage limits, and handling complex data relationships can quickly turn into bottlenecks. Let’s break these down.
Slow Performance
When your data volume increases, query times can skyrocket. With smaller datasets, records load almost instantly. But once you hit 10,000 rows or more, query speeds can drop by 2–5 times unless proper indexing is in place. What used to take milliseconds can stretch into seconds, frustrating users.
Things get even worse with reporting features or dashboards. Processing thousands of records for analytics can push load times past 10 seconds. Add high concurrent user access to the mix, and these delays ripple across your entire app, making the experience sluggish for everyone.
Storage and Data Limits
Platform restrictions can cap your app’s growth. Many no-code platforms have hard limits, like row counts capped at 50,000–100,000 records or storage restricted to 10–50 GB. For instance, Adalo’s free plan only allows 200 records total. Once you hit these limits, your app simply stops accepting new data.
API rate limits are another hurdle. Platforms often throttle API requests, which can choke performance during heavy usage. For example, Airtable allows just 5 API requests per second per base and limits responses to 100 records per request.
"Your Adalo app is working great until you hit the 200-record wall on the free plan." - The Adalo Team
Managing Complex Data Relationships
Relational data becomes a major drag on performance as it scales. Simple one-to-many relationships work fine with smaller datasets. But once you exceed 100,000 related records, the performance issues start to show. Many no-code platforms lack optimized joins, so instead of efficiently pulling data, they perform full table scans, slowing everything down.
Nested relationships make things even worse. While a one-to-one relationship might hold up, introducing many-to-many relationships or nesting data more than four levels deep can stretch query times into minutes. This is especially problematic for apps managing hierarchical data, like e-commerce systems (products → orders → line items → inventory) or enterprise apps with multi-level organizational structures. Even basic tasks like counting or filtering data based on relationships require calculations for every item, dragging down performance as datasets grow.
To handle these challenges, robust data modeling is essential to keep your app running smoothly at scale.
How to Design Data Models That Scale
The way you organize data, load it, and optimize queries can make or break your database performance as your records grow. Let’s dive into some key strategies - normalization, pagination, and indexing - that can help your data model scale effectively.
Normalize Your Data Structures
Cut down on redundancy with relationships. Instead of duplicating details like an event host's name, email, and phone number across every event record, create a separate "Hosts" collection and link to it. That way, when you update a host's information, it happens in one place rather than across dozens of records.
Consolidate similar data types. Instead of managing separate collections for items like "Shoes", "Shirts", and "Pants", combine them into a single "Clothing" collection with a "Type" property. This keeps your database simpler and avoids the performance hit of managing multiple collections with overlapping properties. Also, steer clear of many-to-many relationships whenever possible - they complicate queries and can slow down performance.
Pre-calculate frequently used values. For example, instead of filtering the entire orders collection every time you want to show how many purchases a customer has made, add a "Total Orders" property to their record. Update this property whenever new orders come in, saving significant processing time.
Use Pagination and Lazy Loading
Load only what’s needed at first. Rather than pulling in your entire catalog, start with a limited set of data - such as the 10 most recent products. Pair this with sorting (e.g., by "Created Date") to ensure users see the most relevant information right away.
Fetch data progressively. Enable features like "Load items as user scrolls" to retrieve additional records as needed. This avoids overwhelming the app with thousands of records at once, which could cause freezing or lag. For example, Adalo's infrastructure improvements have reduced initial load times by 86% for data-heavy apps, but pagination remains essential for maintaining speed as datasets grow.
Be mindful of auto-refresh features. If you're working with large lists, disable or limit auto-refresh, as it reloads and re-filters data every few seconds - a process that can tax both devices and servers. For external databases like Airtable, create filtered backend views to deliver only the necessary records. This reduces the API payload and helps you stay within Airtable's 5-requests-per-second rate limit.
Index Frequently Queried Fields
Optimizing your data structure is just the start - indexing is what truly accelerates data retrieval. Focus on fields that are critical for sorting, filtering, and searching. Properties like "Created At" dates, "Category", "Status", or "Price" are excellent candidates for indexing. Properly indexed fields can significantly speed up list rendering and reduce query times.
Leverage unique identifiers. Use IDs or Order Numbers for efficient record mapping. In Adalo, the first property in a collection serves as the record's label, so using unique values here boosts organization and retrieval. Relationship fields also act as indexes, letting you look up related data without duplicating properties across collections.
Avoid on-the-fly calculations. For example, instead of calculating how many items are in a user’s cart every time they open the screen, maintain a "Cart Count" property that updates as items are added or removed. This eliminates the need for the server to perform repeated calculations during list rendering.
| Optimization Technique | Best Practice | Performance Impact |
|---|---|---|
| Record Retrieval | Limit items loaded initially | Reduces JSON payload size and rendering time |
| Data Loading | Enable "Load items as user scrolls" | Prevents app freezing by fetching data in chunks |
| Calculations | Store counts as record properties | Avoids server-side calculations for every list row |
| External Data | Use filtered backend views | Cuts down on data transfer and API call volume |
Using Adalo's Features for Scaling
Adalo makes scaling your apps straightforward by taking care of resource management behind the scenes. With its hosted backend and the ability to connect to external databases or legacy systems, the platform ensures your app can handle complex data needs as it grows.
Adalo's Hosted Backend for Automatic Scaling
Adalo’s cloud-based infrastructure automatically adjusts storage and computing resources to match your app’s growing demands. This means there’s no need to manually configure servers. For example, apps with thousands of records can handle increased traffic seamlessly thanks to Adalo’s serverless architecture, which dynamically adds workers during peak usage.
"Autoscaling allows us to automatically increase the amount of workers we're using to have even more capacity at peak load times." - Cameron Nuckols, Director of Engineering, Adalo
In 2025, Adalo expanded its server capacity by over 50% to manage rising demand, and it now processes over 20 million data requests daily. To further enhance performance, Adalo uses "Region Based Sharding", deploying servers in different geographic locations. This reduces latency by serving users from the nearest server. Whether your app has 100 users or 10,000, this setup ensures it stays responsive.
This automatic scaling feature works hand-in-hand with Adalo’s ability to connect to external databases, making it easier to manage even larger data volumes effectively.
Connecting to External Databases
Adalo allows direct connections to external databases like PostgreSQL, MS SQL Server, Airtable, or Google Sheets. By offloading data storage to these systems, you can work with massive datasets - like millions of rows in PostgreSQL - while still using Adalo’s visual tools for app logic.
For instance, a business app displaying sales data might connect to a PostgreSQL database with over 500,000 records. Adalo retrieves only the filtered data needed via API, keeping the app fast and responsive. This approach has helped enterprises launch data-heavy mobile apps in weeks, saving 5–10× the cost compared to custom development.
To connect to an external database, you’ll need at least the Professional plan, which starts at $52/month. When setting up Airtable, use a Personal Access Token for authentication, set the "Results Key" to records, and switch the update method from PUT to PATCH to avoid overwriting data. Creating filtered views - such as "Active Tasks" - instead of querying entire tables can also improve performance.
For older systems without modern API support, DreamFactory offers a practical solution.
Connecting to Legacy Systems with DreamFactory
Legacy systems like IBM DB2 or older ERP platforms often don’t support modern APIs or rely on outdated formats like XML. DreamFactory bridges this gap by automatically generating RESTful APIs from these databases, ranking among the best no-code API builders, enabling Adalo to securely access and scale their data.
Here’s how it works: Install DreamFactory and connect it to your legacy database. The tool generates APIs automatically through its dashboard. In Adalo, you can add an External Collection using the DreamFactory API endpoint, authenticate with API keys, map fields visually, and apply filters or pagination. Testing sample queries ensures smooth, low-latency access, even as data scales to enterprise levels.
This integration is particularly beneficial for Adalo Blue users who need to connect internal apps to older datasets or systems with limited API support. By using DreamFactory as middleware, you can build modern mobile apps on top of decades-old data - without needing to replatform or develop a custom backend.
Monitoring and Improving Performance as Your App Grows
When your app is live and handling large datasets, keeping an eye on its performance is key. Regular monitoring ensures your app runs smoothly, even as it scales. The aim? Keep response times under two seconds and error rates below 1%, whether your database holds thousands or millions of records. To achieve this, techniques like optimizing your no-code app performance through caching and lazy loading can help minimize load times and maintain a seamless user experience.
Use Caching and Lazy Loading
Caching is a game-changer for reducing server load, especially during high-traffic periods. By storing frequently accessed data in memory, caching can cut server workload by up to 80%. Adalo 3.0 has built-in caching capabilities that prevent unnecessary table reloads, boosting speed by 100-200% over baseline performance.
Lazy loading, on the other hand, ensures only the data needed at the moment is loaded. For example, Adalo’s "Load Items as User Scrolls" feature in advanced list options significantly reduces initial screen load times. This keeps your app snappy, even when connected to external databases like PostgreSQL with hundreds of thousands of rows.
For the best results, combine these approaches. Use caching for static data like product catalogs or user profiles, and lazy loading for dynamic content such as activity feeds or search results. Be cautious with nested lists, as they can lead to multiple database queries that negate the benefits of lazy loading.
Track Performance Metrics
Keeping tabs on metrics like response times, error rates, and database query speeds helps you catch scaling issues before they affect users. Adalo 3.0 offers advanced monitoring dashboards, allowing you to track these metrics in real time. You can also integrate tools like Google Analytics to monitor page load speeds and concurrent user activity.
Think of app performance as a score, similar to tools like GTMetrix or Lighthouse. Each new feature or data addition impacts this score, so regular audits are essential. Look out for excessive groups, hidden components that load unnecessary data, or components nested more than four levels deep, as these increase processing demands. Limit the number of items in lists and sort them by date to ensure only the most relevant records are loaded, avoiding unnecessary data retrieval that slows down performance.
No-code apps that use monitoring tools report 40-60% faster response times, even with datasets exceeding one million rows. By staying proactive with performance tracking, you can optimize your app before users encounter issues.
If you notice significant processing delays during monitoring, consider offloading heavy tasks to serverless functions.
Offload Heavy Tasks with Serverless Functions
Serverless architecture is a smart way to handle resource-intensive tasks without slowing down your app. Instead of running complex calculations or bulk data exports directly on users’ devices, these tasks can be offloaded to serverless endpoints that scale automatically based on demand.
For example, if you need to generate detailed reports from a PostgreSQL database with over 100,000 records, using a serverless backend like Xano or DreamFactory ensures smooth performance. Your app can display the final results without subjecting users to long wait times. Platforms like Supabase can handle traffic spikes up to 10 times higher than normal while cutting costs by 70% compared to traditional fixed servers.
"We're working to migrate much of the application logic processing from your users' devices to our servers. This means that your users will spend less time looking at loading screens." - Cameron Nuckols, Director of Engineering, Adalo
This strategy is particularly effective for tasks like real-time analytics, data aggregation, or machine learning inferences. By keeping these compute-heavy processes off the user’s device, your app can maintain consistent performance, even as your data grows exponentially.
Conclusion
To keep your app running smoothly as your user base grows and data volumes expand, it's essential to design efficient data models and rely on a solid infrastructure. Techniques like normalization, indexing, pagination, caching, lazy loading, performance monitoring, and serverless offloading play a key role in maintaining responsiveness, even under heavy loads.
Adalo’s hosted backend, built on AWS, automatically adjusts to your needs with dynamic load management. Plus, it offers seamless integration with external databases like PostgreSQL, Airtable, and Google Sheets, allowing you to go beyond native storage limits. For enterprise solutions, Adalo Blue adds even more flexibility with DreamFactory, enabling connections to older systems that lack modern APIs.
These strategies ensure your app performs reliably, whether you're managing thousands or tens of thousands of records. As highlighted by industry experts:
"AWS will allow us to autoscale our database and be better prepared to handle large and uneven loads. So no matter how large your Adalo app gets, we'll be able to handle it".
Beyond performance, these measures lead to tangible benefits like lower costs and faster deployment. Many apps achieve 5–10× cost savings while reducing launch timelines to days or weeks instead of months. This approach ensures production-quality performance as your app scales.
FAQs
How can I make my no-code app handle large datasets more efficiently?
When working with large datasets in your no-code app, it's essential to focus on smart data management and boosting performance. Start by streamlining your database collections and queries to avoid unnecessary processing. This keeps things efficient and ensures your app runs smoothly.
Consider using pagination or lazy loading techniques. These methods load data in smaller chunks instead of all at once, reducing strain on your app and creating a smoother experience for users.
Another key tip: compress your media files. Smaller file sizes save storage space and cut down on bandwidth usage, which helps your app run faster. Also, take advantage of caching to store frequently accessed data temporarily. This reduces the need for repeated database calls and speeds up data retrieval.
By combining these strategies, you can handle large datasets more effectively while keeping your app responsive and user-friendly.
How can I manage large data sets in no-code platforms without hitting storage limits?
When dealing with large data sets, external databases can be a game-changer. Tools like Google Sheets, Airtable, or SQL-based services are excellent options for storing and managing data efficiently. Within your app, you can boost performance by implementing a few smart strategies: remove unused records, enable pagination or lazy loading to load data incrementally, compress media files to save space, and cache frequently accessed data for quicker retrieval. These approaches not only reduce storage demands but also enhance load times, keeping your app running smoothly even with a hefty amount of data.
How does Adalo ensure its platform can handle high user traffic?
Adalo has built its infrastructure to handle high-traffic demands with ease. Their U.S.-based servers have been expanded by more than 50% and include automatic autoscaling, which means extra capacity can be added whenever it's needed. This ensures your app stays responsive, even during those busy peak times.
Behind the scenes, a senior infrastructure team is always on the job. They monitor and fine-tune performance continuously, giving you the freedom to focus on creating your app without stressing over technical hurdles.
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