Backend Systems for Mobile Apps

Four years ago, I built a product for a client that almost crashed from its own success. We launched fast. MVP was live in three weeks. Users loved it until they started uploading massive PDFs, images, and videos, despite having a max upload size of 100MB per file Suddenly: ❌ Uploads started timing out ❌ Server CPU spiked ❌ Storage filled up ❌ And complaints rolled in daily It wasn’t a code bug though, it was a scaling problem. And it taught me a painful but crucial lesson: 👉 If your SaaS involves file uploads, and you don’t architect for scale early on, you’re building a ticking time bomb. That aside, here’s how I scale file upload systems to handle millions of uploads today: ✅ 1. Object Storage First Never store files on your app server. Ever. I go straight to Amazon S3, Cloudflare R2 or Backblaze B2 Reason for that is - Virtually infinite storage - Built-in redundancy - Compatibility with CDNs Easy lifecycle & permission management ✅ 2. Use Resumable Uploads Big files + spotty connections = user frustration. That’s why I implement chunked + resumable uploads using Tus.io. There are more options but DYOR This means if your internet drops, you don’t have to start over. ✅ 3. Presigned URLs for Direct Uploads Let the client talk to the storage directly, not your backend. Typical flow: 1. Client: “I want to upload.” 2. Server: “Here’s a secure presigned URL.” 3. Client uploads directly to storage. This results in less backend load, faster upload speeds and a much cleaner architecture ✅ 4. Process in the Background Once uploaded, files usually need some love: 🪏 Compress images 🪏 Transcode video 🪏 Analyze or extract metadata I use: - Background queues (Inngest, RabbitMQ) - Workers (Node, Python, AWS Lambda) - ffmpeg / Exif tools N.B: Don’t block the user, process it async and notify when done. ✅ 5. Secure Your Pipeline - Limit file types & extensions - Enforce file size limits - Use virus/malware scanning (I use ClamAV) - Validate uploads on the backend As for that client project I rebuilt it using this system. It now processes hundreds of uploads a day with zero downtime. The takeaway? You don’t need a massive DevOps team to scale smart. You need architecture that makes sense for what you’re building. Most SaaS founders and CTOs are so busy shipping that they don’t think about this until it’s too late. If you’re building or rebuilding a SaaS and plan to handle user uploads at any scale, build like you already have 10,000 users. That’s how we build at Sqaleup Inc. Let’s chat if you want this kind of bulletproof upload architecture in your product 🚀

How do you Design a scalable system (like Instagram) with #AIagents? Whether you're a backend engineer, an AI developer, or someone diving into infra design, here’s a must-know architecture breakdown. 📌 Instagram-like architecture, simplified: 1.Client actions (like, follow, upload) trigger calls to backend via APIs. 2.Backend servers process these with frameworks like Django or Express. 3.Relational DBs like PostgreSQL store structured data (user profiles, comments). 4.NoSQL databases like Cassandra handle high-throughput data (feeds, logs). 5.Caching systems (Redis/Memcached) ensure fast access to frequently used data. 📌 Object storage holds media (videos, images), with metadata in DBs. Background workers (e.g., Celery) process async tasks like notifications. 📌 CDNs serve static assets efficiently across geographies. 📊 This is how high-scale systems are built for real-world performance. 💡 Now MCP (Model Context Protocol) a game-changer from Anthropic. Most AI tools stop at answering questions. But MCP connects models like Claude to real-world systems, APIs, databases, tools with zero custom integration code. 🔗 How it works: MCP Client (inside AI) → formats requests MCP Server → bridges to tools like PostgreSQL, Google Drive Building Blocks: ◾ Roots: Secure file access ◾ Sampling: Request AI support ◾ Resources/Tools/Prompts: Everything the AI can act on 🧠 Imagine an AI not just replying but querying data, generating reports, and debugging infra with autonomy. Pair this with the system architecture above, and you're no longer just building apps, it is for system design, or AI-driven workflows. #AIworkflows #AIarchitecture

Backend development is more than just writing an API It’s easy to think backend development is just about building CRUD operations. However, there is so much more to creating a real backend system. • Resilience & Fault Tolerance – Handling failures gracefully. • Databases – Efficient storage, indexing, & query optimization. • Authentication – OAuth, JWT, role-based access, & data protection. • Monitoring & Logging – OpenTelemetry for tracing for system health. • Scalability – Load balancing, rate limiting, & autoscaling infrastructure. • Caching – Redis, Memcached, & HTTP caching to improve performance. • Async Processing – Message queues (Kafka, RabbitMQ) & background jobs. Building a scalable, secure, and efficient system can be challenging. But that’s what makes it exciting!