The Monolith Decision
You're the incoming CTO of FinScale, a Series B fintech processing $2M in daily transactions. The monolith is cracking under 10x growth pressure. You have a weekend to choose a strategy.
Learn proven system design patterns and architectural approaches from experienced CTOs and engineering leaders.
114 articles, frameworks, and resources
You're the incoming CTO of FinScale, a Series B fintech processing $2M in daily transactions. The monolith is cracking under 10x growth pressure. You have a weekend to choose a strategy.
You're the new CTO of a mid-stage startup with a 500K-line monolith. Revenue is growing 3x YoY, but deployments take 4 hours and the on-call team is burning out. It's time to make some hard decisions.
Build scalable, decoupled systems with event-driven architecture. Production-ready event bus, domain events, outbox pattern, and dead letter queues.
The Cloudflare crash on November 18th, coupled with recent AWS and Azure outages, reveals an uncomfortable truth: our industry's consolidation has created catastrophic single points of failure at scale. It's time for CTOs to rethink resiliency architecture.
A comprehensive guide to creating, communicating, and executing a technical strategy that drives business value. Includes frameworks, templates, and real-world examples.
A practical framework for evaluating and choosing technologies that will serve your team and business for years to come.
Strategic comparison of REST, GraphQL, and gRPC API architectures. Performance, complexity, tooling, team expertise, and when to choose each approach.
Strategic comparison of microservices and monolithic architectures. Team size, complexity, deployment, costs, and when to choose each approach.
Strategic comparison of MySQL and PostgreSQL. Performance, features, costs, team expertise, and when to choose each relational database.
A systematic framework for reviewing architecture proposals that balances speed with rigor. Includes review criteria, decision templates, and governance models for teams of all sizes.
A systematic approach to evaluating and choosing technology stacks. Includes comparison matrices, risk assessments, and decision frameworks for databases, languages, frameworks, and infrastructure.
A systematic approach to making high-stakes technical decisions that balance speed, cost, and long-term maintainability. Includes decision matrices, evaluation templates, and real-world examples.
Enterprise AI is rapidly shifting toward agentic systems that execute multi-step work across data and tools.
Engineering organizations are moving from microservice-centric design toward agentic, AI-assisted systems where code is cheaper and faster to produce, but reliability, governance, and infrastructure...
n8n vs Temporal: choosing between business automation and durable execution
Enterprises are standardizing “bounded agency” patterns: multi-agent systems with deterministic controls, decision models instead of free-form generation, and tighter governance over the data and...
AI systems are moving from prototype agents to production agent platforms, forcing architectural changes in three places at once: stateless integration protocols, context-ready data layers, and...
Enterprises are moving from ad hoc AI assistants to fleets of production AI agents, which is forcing a new layer of governance: identity, policy, observability, and architectural guardrails that look...
Engineering orgs are consolidating complexity into fewer, more controllable primitives (databases and constrained execution paths) while adding explicit governance mechanisms for AI outputs and...
Ross McFarlane and Kevin Holditch discuss Form3's evolution from a single-cloud setup to a triple active multi-cloud architecture.
AI programs are shifting from prompt-to-PR wins toward context-rich, governed, architecture-heavy systems that can extract, remember, and act across enterprise data.
Data platforms are consolidating around open table formats (Iceberg) and streaming ingestion, while workflow orchestration becomes more declarative and engine-agnostic.
AI adoption is moving from capability scaling to trust engineering: verifiable agent execution, stronger data governance, and faster security response are becoming the core constraints on production...
AI agents are moving from demos to production workflows, pushing teams to build “agent-ready” foundations: persistent runtimes, strong identity, governed context, and token-cost-aware data modeling.
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