# Databases

Vector Databases at Scale: Architecture Patterns for High-Throughput AI Applications

Vector databases power AI applications from semantic search to RAG systems. Learn how to architect vector databases for massive read/write workloads, billion-scale embeddings, and low-latency similarity search.

Transactional AI Agents: Patterns for Database Consistency and Safe Rollbacks

AI agents that write to databases introduce new consistency risks. Learn the patterns for ACID-compliant agent actions, compensation transactions, and safe rollbacks when agent-driven operations fail.

Scaling Database Writes for AI-Generated Data: Handling Massive Write Loads from AI Systems

AI systems generate enormous write loads—content, embeddings, metadata, and logs. Learn how to architect databases that handle millions of writes per second from AI applications.

Multi-Model Database Patterns for AI Systems: Combining Relational, Vector, and Document Stores

AI systems need multiple database models—relational for structured data, vector for embeddings, document for flexible content. Learn patterns for combining databases into a cohesive architecture.

AI Agents for Database Schema Evolution: Automated Migrations and Safe Schema Changes

Database schema changes are high-risk, high-friction operations. AI agents can analyze code changes, generate migration scripts, validate them against best practices, and execute them safely with rollback plans.

Caching Strategies for AI Applications: Managing High Read Loads and Latency-Sensitive Inference

AI applications face intense read loads—LLM responses, embeddings, and inference results. Learn caching strategies that handle millions of reads per second while keeping latency low.

AI Agents That Write SQL: Patterns for Reliable Database Query Generation

AI agents that generate and execute SQL queries can automate analytics, debugging, and reporting. Learn the patterns for reliable query generation, schema grounding, and safe execution against production databases.

AI Agents for Database Monitoring: Autonomous Performance Tuning and Anomaly Detection

AI agents can monitor database performance, detect anomalies, and suggest optimizations—often before humans notice a problem. Learn how to build agents that keep your databases healthy.

2026-05-05 5 min read

Database Architecture for AI Agent Memory: Storing State, Conversations, and Knowledge at Scale

AI agents need memory—short-term context, long-term knowledge, and episodic recall. Learn how to architect databases that handle high-throughput reads/writes for agent memory systems.

2026-04-16 3 min read

Tech Stack Strategy Consulting: A Framework to Choose Backend, DB, Cache, and Cloud

Tech stack strategy consulting framework: align backend, databases, caching, cloud patterns, and deployment workflows to performance, reliability, cost, and team velocity.

2026-04-13 4 min read

Tech Stack Consulting Playbook: Choose a Stack That Scales

Tech Stack Consulting framework to choose backend frameworks, databases, caching, cloud patterns, and deployment workflows that scale without unnecessary complexity.

2026-04-12 3 min read

Backend System Scaling Checklist: Find Bottlenecks and Stabilize Performance

Backend System Scaling checklist to stabilize APIs and data pipelines. Identify bottlenecks, fix query and caching issues, and implement measurable SLOs.