AI-powered platforms are easy to imagine but hard to deliver at scale. A proof of concept may run beautifully in a demo, but when thousands of users arrive, the same system often slows to a crawl. The consequences are costly: budgets balloon, developers scramble to fix bottlenecks, and users lose trust just when adoption should be taking off.
At Six Feet Up, we’ve worked with teams building platforms that blend real-time video, audio, and AI chat. Scaling them to tens of thousands of users has revealed challenges and lessons that apply broadly to anyone building AI/ML-powered applications.
If you’ve ever launched an AI feature into production, you know traffic rarely arrives in a steady stream. It comes in waves, including sudden spikes that overwhelm legacy scaling strategies. The result? Either outages that frustrate users or costly overprovisioning that burns cash.
Kubernetes helps orchestrate services, but it can’t handle elasticity on its own. Pairing it with Karpenter changes the game: it provisions nodes instantly based on demand, packs pods efficiently across nodes, and can even tap into spot instances to keep costs under control. With this combo, your platform scales seamlessly when demand surges and contracts when it quiets down.
Early prototypes often rely on a pod-per-call model: every user interaction spins up its own pod. While it feels clean in a demo, this approach collapses under scale. Every pod carries startup overhead, consumes its own CPU and memory, and often opens new database connections. Multiply that across thousands of users, and infrastructure costs surge while performance slows.
A more modern approach would be to adopt a request-based architecture where a single pod handles multiple concurrent calls. This cuts wasted resources, reduces strain on the database, and creates a system that can grow sustainably. For one of our clients, making this change reduced resource usage by more than 70% and opened the path for real growth instead of runaway costs.
Postgres can buckle under pressure if it’s not tuned for scale. We’ve seen misconfigured probes or missing connection pooling bring entire systems to a crawl, exactly when users need responsiveness the most.
Fixing this isn’t glamorous, but it’s essential. PgBouncer connection pooling stabilizes Postgres. Redis caching reduces repetitive queries that add needless strain. And with async views, I/O-heavy workloads like streaming real-time transcriptions suddenly become feasible at scale.
AI features like speech-to-text, text-to-speech, and LLM responses sound amazing on paper, but they quickly lose impact when chained together in a monolithic design. Latency creeps in, responses slow down, and suddenly the “wow” factor becomes a “why is this so slow?” moment.
The answer is a modular, service-based pipeline. When each AI component scales independently, transcription spikes don’t drag down chat performance, video, and audio can scale separately, and swapping in a new provider (e.g., trying a new speech API) doesn’t require a rebuild. It’s a design choice that keeps your system adaptable as workloads evolve.
Perhaps the most frustrating scaling issue isn’t performance itself. It’s not knowing what went wrong until your users tell you. By then, debugging is slower, more complex, and more costly.
That’s why observability must be baked in from day one. Grafana + Prometheus let you visualize metrics and set alerts when something looks off. Loki centralizes logs so you can catch issues before your users do. And with load testing tools like Locust (or even lightweight Python scripts), you can simulate traffic, test breaking points, and discover bottlenecks on your own terms.
From working through these challenges, a few principles consistently prove essential for scaling AI apps:
Scaling AI-driven apps isn’t about throwing hardware at the problem. It is about making strategic choices in architecture, elasticity, and observability. By planning for scale from the beginning, you can move beyond fragile prototypes and deliver resilient platforms that thrive under real demand.
If you’re building AI-driven applications and need to move beyond prototypes, our team can help you architect for growth. Learn how we helped one client go from zero to tens of thousands of users in production in our Multimodal AI Personas case study.
Six Feet Up, Inc.
11208 Windermere Blvd.
Fishers, IN 46037 - USA
Phone: +1 (317) 861-5948
Email: info@sixfeetup.com
