How do you scale a Node.js backend?

With the right architecture: services split by load, event streaming, queues and caching, observability, and databases chosen per workload — proven on a platform that grew to 3M+ users.

How do you scale a Node.js backend?

With the right architecture: services split by load, event streaming, queues and caching, observability, and databases chosen per workload — proven on a platform that grew to 3M+ users.

AI consulting & engineering

Architecting and scaling Node.js backends

Q: How do you actually implement AI in a business?

I find where AI creates real value, design the architecture, and build it end-to-end — running systems, not slideware.

Q: Can you build AI into a healthcare product?

Yes — I have architected clinical decision support, AI meal analysis and LLM health assistants on a custom model + vector database, with the privacy and reliability healthcare demands.

Q: Can you build a production mobile app in React Native?

Yes — I ship native iOS and Android apps from one Expo/React Native codebase, with payments, provisioning, offline-first flows and clean native integrations.

Q: Can you build an MVP and then scale it?

Yes — I build MVPs end-to-end fast, then evolve the same codebase to production scale instead of throwing it away. Several of mine grew to acquisition or millions of users.

Q: How do you build a project with agentic AI development?

I drive AI coding agents (Claude Code, Codex, Cursor) with custom rules, hooks and context, spec-first — then gate everything with tests and AI review so speed never costs quality.

Q: How do you use CodeRabbit for code review and quality?

CodeRabbit reviews every pull request automatically — catching bugs, security issues and smells — as a gate alongside human review and a real test suite, so nothing weak reaches main.

Q: Can you build a custom AI model for my product?

Yes — self-hosted models on Ollama (Llama, Mistral, DeepSeek), fine-tuned or RAG-grounded, when you need privacy, control or a task an off-the-shelf API can’t do well.

Q: How do you build a RAG system?

Chunk and embed your data into a vector database, retrieve and rerank the right context per query, ground the model in it, and evaluate — so answers are accurate and cite your sources, not hallucinations.

Q: Which AI model should you use — Claude, GPT, Gemini, Perplexity or Grok?

It depends on the task — and the real answer is usually more than one. I pick and route across frontier models by strength, with evals and fallbacks, so you get the best result without lock-in.

Node.js scales beautifully — until an architecture that was fine at launch meets real traffic. I design backends that keep responding as load grows, and I have taken one to more than three million signed-up users without it falling over.

Split by load, not by diagram

I draw service boundaries around what actually scales differently — auth, ingestion, AI inference, billing — rather than an org chart. On aichatapp.ai (GPT-4o, Gemini, Claude, Grok, DeepSeek in one product) I built the backend on GCP Cloud Run with Pub/Sub and Firestore; it grew to 3M+ users. On OptAI I used a Bun runtime with Kafka event streaming for the crawl-and-score pipelines.

Streams, queues, cache — in that order

Most scaling problems are back-pressure problems. Kafka or RabbitMQ decouple producers from consumers, BullMQ absorbs spikes, and Redis caching takes the read load off the database. Add tracing and dashboards (Datadog, Sentry, Grafana, PostHog) so you scale from evidence, not guesses.

The right database for each job

One database rarely fits a whole system. I mix PostgreSQL, MongoDB, DynamoDB/Firestore, Elasticsearch and vector stores by workload, and I have run NestJS, Go, Java Spring Boot and Rust services side by side behind a Kong API gateway on Kubernetes when the load justified it.

Key points

Service boundaries drawn around what scales differently — proven to 3M+ users.
Event streaming, queues and caching to handle back-pressure and spikes.
Observability first, and the right database per workload.

Tools I reach for

Node.jsNestJSBunKafkaRabbitMQRedisBullMQPostgreSQLKubernetesDatadog

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Architecting and scaling Node.js backends

Split by load, not by diagram

Streams, queues, cache — in that order

The right database for each job

Related work

Have something like this in mind?

More questions I'm asked