Scalability At A Cost

Introduction

Traditionally, companies maintained large, specialized teams to manage infrastructure and handling tasks such as server provisioning, security patching, database backups, and network troubleshooting.

• While robust, this model introduced significant operational overhead and often slowed down product development cycles.

With the rise of cloud computing and Platform-as-a-Service (PaaS) offerings, the industry has undergone a major shift.

• Organizations increasingly rely on cloud-native services that provide built-in scalability, security, and automation, allowing teams to abstract away much of the underlying infrastructure management.
• This shift enables engineering teams to focus more on building features and delivering value to users, rather than maintaining infrastructure.

As the focus moves toward developing user-centric applications, cost optimization becomes essential for ensuring commercial viability and scalability, especially when using cloud platforms with pay-as-you-go pricing models.

• Every operation, from data reads and writes to API calls and authentication events, contributes to the overall cost.
• Unlike traditional fixed-cost infrastructure, cloud expenses grow proportionally (and sometimes exponentially) with user activity and can quickly become a significant operational concern.

Choosing Firebase or Supabase

The choice of backend platform profoundly influences both the development process and the long-term cost structure.

• Firebase and Supabase are two popular choices, offering comprehensive integrated backend services beyond basic data storage.

Firebase includes Cloud Firestore and Realtime Database, each with distinct pricing models.

• Firestore charges for document reads, writes, deletes, storage, and network egress, making it suitable for structured, scalable, serverless applications with granular, low-volume operations and robust query capabilities.
• Realtime Database, conversely, provides cheaper writes and connection-based pricing, ideal for applications with lightweight but high-frequency updates, such as live presence indicators or chat.

Supabase, an open-source Firebase alternative built on PostgreSQL, bases its billing on database storage, compute resource usage (CPU and memory), and bandwidth.

• It is well-suited for write-heavy applications and developers comfortable with managing query optimization and relational schemas.
• Because it offers a different, often more predictable, pricing model, Supabase has gained immense popularity.

Both Firebase or Supabase offer a suite of integrated backend features:

• Global CDN delivery: Static files (HTML, JS, CSS, JSON, images) are served from edge servers close to the user, enhancing load speed, reducing latency, and offloading traffic from origin servers.
• Authentication: Built-in authentication systems provide secure user management with support for email/password, anonymous logins, and third-party OAuth providers (e.g., Google, Apple).
• Real-time capabilities: Firestore and Supabase offer real-time data synchronization without the need for custom WebSocket servers or polling infrastructure.
• Serverless compute: Firebase Functions and Supabase Edge Functions enable backend logic for validation, aggregation, webhook processing, or scheduled jobs without server management.

These managed backend services eliminate the need for traditional server infrastructure, reducing both cost and complexity for startups and small teams.

• This fosters rapid iteration, easier scaling, and cost savings across hosting, bandwidth, compute, and support.
• However, for large-scale, high-throughput systems like WhatsApp or Telegram, custom-built, highly optimized infrastructure is typically employed to maximize performance and minimize costs at massive volumes.

Efficient Data Operations and Structuring

Cost-effective data usage begins with how data is written to the database and the structure used for those writes.

In Firestore, every document write, read, or delete incurs a cost.

• One of the most effective ways to reduce write costs is through batching.
• Firestore supports atomic batch operations, allowing up to 500 document changes in a single request. During data imports, bulk updates or transactional writes, batching significantly minimizes the number of billable operations.

For read-heavy applications using Firestore, denormalization can be beneficial.

• This involves duplicating essential values across multiple documents to reduce the number of required reads.
• While this increases write complexity and storage size, the reduction in repeated reads can lead to substantial savings at scale.

Another common pattern involves preview documents.

• In planner-style apps or journal-based systems, list views can display only a summary of each document (such as title, status, and last-updated timestamp) without fetching the full content.
• The complete document is only loaded when a user selects it.
• Similarly, older or infrequently accessed content can be archived into separate collections or marked for exclusion during initial queries, improving the performance and cost profile of the primary dataset.

Applications can also adopt on-demand syncing strategies.

• For instance, planner apps might initially load data only for the current week or month, fetching past or future entries only when the user navigates to them.
• This ensures that unused data is not loaded or charged until absolutely necessary.

Instead of syncing or rewriting entire documents on every user change, applications can send only the changed fields (deltas).

• Delta syncing reduces the number of write operations and significantly cuts data transfer costs.

By combining efficient document structure with thoughtful data access patterns, backend costs can be drastically reduced while maintaining a responsive and scalable application.

Client vs Server Execution

The choice between client-side and server-side execution has significant implications for scalability, performance and operational cost.

• Effective applications often employ a hybrid approach.

Server-side logic, implemented via Firebase Cloud Functions or Supabase Edge Functions, excels at enforcing security, controlling data access, and optimizing performance for critical operations. It enables developers to:

• Aggregate data from multiple sources into single efficient responses.
• Protect sensitive operations and secrets from exposure on the client.
• Cache frequently accessed or computationally expensive data.
• Optimize complex queries, ensuring only the necessary data is returned.
• Control access through fine-grained logic beyond client-side rules.
• Log and rate-limit interactions centrally.
• Improve initial load performance for dynamic applications through server-rendered HTML or API response hydration.

However, server-side power comes at a cost: these platforms bill for function invocations, execution time, memory and outbound traffic.

• Inefficient or high-frequency usage can make server-side operations expensive at scale.

In the lifecycle of a Progressive Web App (PWA), the temptation to deploy every small change immediately is strong.

• However, while this accelerates feature delivery, it introduces significant operational costs and stability risks that demand careful consideration.
• Each deployment triggers a cascade of consequences: it forces cache invalidation across your user base, creates sudden spikes in backend load, and can disrupt the experience for offline users.
• These factors lead to higher operational costs and unnecessary data synchronization.
• Therefore, unless deploying an urgent fix, a more deliberate strategy of weekly or batched deployments is preferable for maintaining both stability and efficiency.

Client-side execution reduces server load by offloading processing, UI logic, and even data management directly to the user’s device. This is particularly advantageous for:

• Filtering and formatting datasets already stored locally (e.g., in IndexedDB).
• Displaying static or semi-static content such as preloaded drug data.
• Applying delta syncing, where only modified fields are sent to the backend.
• Parsing external public data (e.g., Google Sheets via Google Visualization API).
• Managing UI state and interactions without round-trips to the backend.

By reducing backend interactions, client-side execution can significantly cut down on read/write costs, improve perceived responsiveness and support offline functionality.

• However, without careful design, client-side access can lead to uncontrolled or insecure operations.

Most scalable applications adopt a hybrid model.

• Server-side operations are used for sensitive, complex, or high-impact tasks, while the client handles display logic, lightweight interactivity, and safe data management.
• Balancing execution between client and server based on task sensitivity, complexity, frequency and volume is essential for building fast, secure, and cost-efficient applications.

Preloading, Caching and Static Delivery

For applications built with frameworks like Next.js using a client-side App Router, effective use of preloading, caching, and static delivery can lead to dramatic improvements in both performance and cost savings.

For certain types of applications, especially those dealing with static or semi-static datasets, data can be preloaded using a compact prefetched JSON file that syncs into a local IndexedDB store during initial installation or first launch.

• This strategy avoids repeated reads from the backend and ensures that the core dataset remains available offline, while updates can be synced incrementally as deltas.

Complementary offline strategies, such as service workers, IndexedDB, and smart caching policies, enable fast, reliable, and cost-effective delivery of both UI and data content.

• These approaches are particularly well-suited for Progressive Web Apps (PWAs) that need to perform efficiently on mobile devices or in low-connectivity environments.
• With this setup, your app can:
• Load essential data and static assets during the initial visit.
• Continue functioning even without an internet connection.
• Automatically resume syncing when connectivity is restored.

Summary

Cost optimization is not a one-time setup; it requires continuous awareness.

• Both Firebase and Supabase offer usage dashboards to monitor reads, writes, bandwidth and function invocations.
• Setting up billing alerts in the Google Cloud Console or Supabase dashboard is crucial to avoid unexpected costs.

For personal projects, configure your apphosting.yaml to retain only the most recent version (or the two most recent versions for quick rollback) of your application, and set minInstances to 0 under runConfig to reduce idle resource usage. 

• To minimize container size, clean up unused dependencies, add a .dockerignore file to exclude unnecessary files from the build context, and ensure development-only packages are listed under devDependencies in your package.json. This prevents them from being installed in the production environment, improving both build performance and security.