Backend Engineering
Build the services, pipelines, and infrastructure that power AI-integrated production systems
19 articles
Backend engineering for AI-integrated systems requires different decisions than traditional API development. When your backend runs inference pipelines, manages embedding services, streams model responses, and coordinates async workers — standard CRUD patterns don't hold.
The core challenge: AI components are expensive, slow, and non-deterministic. ML inference has variable latency. Embeddings are computationally heavy. Building reliably around these characteristics requires explicit strategies for batching, queue management, resource isolation, and graceful degradation.
This series covers 19 articles from building the backend infrastructure for a production AI-powered Bible app — semantic search, embedding pipelines, OCR, streaming AI responses, and a graph database layer for relationship queries.
Start Here
Three articles that give you the strongest foundation in this topic
Monolith vs Microservices
The architectural decision that shapes everything else
Tradeoffs and reasoning behind splitting monoliths into microservices.
Embeddings
How embedding services work as backend infrastructure
Embedding services in AI backend architectures.
Streaming
Streaming responses from AI models in production
Streaming services and their use in backend systems.
Learning Path
A structured progression through the articles in this category
Architecture Decisions
The foundational choices that determine how your system scales, how teams work, and how AI components fit into your infrastructure.
Core Service Patterns
Authentication, embeddings, vector search, and streaming — the backend primitives that power modern AI applications.
AI-Specific Infrastructure
The backend services purpose-built for AI workloads: inference, embedding pipelines, OCR, and agent orchestration.
Performance & Scale
Optimization patterns for AI-heavy backends — async workers, GPU allocation, batching, latency tuning, and model loading.
Graph & Data
Graph database patterns for relationship-heavy data models, using PostgreSQL with the AGE extension.
Case Study
Bible Verse — Case Study
Production SaaS Platform · Full-Stack · Founder & Sole Engineer
A domain-driven SaaS platform with five independently scalable system boundaries: scripture content delivery, RAG-backed AI study, real-time community interaction, async media processing, and infrastructure services — built and operated end-to-end.
Our Results
How We Built It
- RAG pipeline grounding AI responses in actual scripture rather than model memory
- Hybrid Llama / OpenAI routing — local inference for cost, API fallback for quality at the edge
- Non-blocking media processing — FFmpeg jobs enqueued via BullMQ, API never waits on transcoding
- Cross-instance real-time consistency via Redis pub/sub behind WebSocket and WebRTC layers
Lessons Learned
- Domain boundaries enforced at the service layer prevent coupling long before scale demands microservices.
- RAG retrieval quality matters more than model size — better embeddings outperform a larger model on poor context.
- Async queue design should be first-class, not bolted on; BullMQ worker isolation saved the request path repeatedly.
Stack
Frequently Asked Questions
Common questions about Backend Engineering
When should I use microservices instead of a monolith for an AI system?
Start with a monolith unless you have a specific, proven reason to split. For AI systems specifically, premature service decomposition creates coordination overhead around shared models, embedding caches, and vector stores that's hard to manage. Split when a specific component — like an embedding service or inference worker — has clearly different scaling requirements or failure characteristics from the rest of the system. Separate what needs to be separate; keep together what changes together.
What is an embedding service and why does it need to be a backend service?
An embedding service converts text into high-dimensional vectors using a language model. It needs to be a dedicated backend service because: embedding computation is CPU/GPU-intensive and should be isolated from request-handling processes, embeddings are often reused and benefit from caching, and multiple parts of your system (ingestion pipelines, search endpoints, recommendation logic) all need embeddings but shouldn't duplicate the model loading overhead. Treating it as a first-class service makes it independently scalable and cacheable.
How do I handle streaming responses from AI models in a backend API?
Use server-sent events (SSE) or WebSocket streams to forward the model's token-by-token output to the client. In Node.js/Nuxt backends, you pipe the stream from the model API through your server to the client response. Key implementation concerns: set appropriate timeouts (AI inference can take 10-30+ seconds), handle stream interruption gracefully, decide whether to persist the full response only after stream completion, and consider whether the client needs a message ID to reconnect if the stream drops.
What is vector search and how does it differ from full-text search?
Full-text search matches keywords — it finds documents containing the words you typed. Vector search finds documents that are semantically similar — it finds documents that mean something similar to your query, even if they use completely different words. Vector search requires pre-computed embeddings stored in a vector database or pgvector-enabled PostgreSQL. For AI applications like RAG, semantic search, and recommendation systems, vector search is often more useful than full-text search. For filtering and exact matching, full-text search is still the right tool.
How should I approach GPU allocation in a shared backend environment?
Treat GPU as a scarce, dedicated resource rather than a general compute pool. Use a queue (BullMQ, Redis-based) to serialize inference requests rather than allowing concurrent GPU access that causes memory contention. Profile your models' VRAM usage and set hard limits per worker process. For multi-model environments, consider model loading strategies: lazy loading saves memory but adds latency, eager loading is the opposite. In Kubernetes/K3s environments, use resource limits and node taints to ensure GPU nodes only run GPU workloads.