The Cost-Efficient AI Stack: Ship AI Features Without the Runaway Bill
Most teams overpay for AI by routing every request to a frontier model. This is the architecture we build instead — hybrid cloud+local routing, self-hosted inference, agent orchestration, and cost-per-request observability — and the single principle that ties it together: send each unit of work to the cheapest model that can do it well.
The Local AI Inflection Point: What the Next Three Years Actually Look Like
Local AI is crossing a threshold where on-device and self-hosted models stop being cost-cutting compromises and start being the default choice. Here's what's driving that shift and what it means for how you build software.
Securing Self-Hosted LLMs and AI Agents on Kubernetes
Harden self-hosted vLLM and AI agents on Kubernetes: an auth/rate-limit gateway, gVisor tool sandboxing, prompt-injection guardrails, scoped secrets, and signed model weights — mapped to the OWASP LLM Top 10.
Build a Personal AI Dev Environment: Hybrid Models, Local Inference, and a Workflow That Costs Almost Nothing
The production patterns we deploy for teams — hybrid cloud/local routing, self-hosted models, agent orchestration — scaled down to a single developer's workstation. A practical guide to building a personal AI dev environment with Ollama, Claude Code, and a local router that keeps your token bill near zero.
The Agent Control Plane: Frontier Models Plan, Your Kubernetes Fleet Executes
How to orchestrate a fleet of AI agents using a shared task queue — frontier models like Claude handle planning and decomposition, while a local Kubernetes worker pool runs the high-volume execution tasks. Covers the task ledger, dynamic task creation, lane-based routing, and KEDA autoscaling.
The Hybrid AI Playbook: Cloud Models for Thinking, Local Models for Doing
How to cut your AI costs by 60-80% using a hybrid approach — Claude or GPT for planning and complex reasoning, local models like Llama and Qwen for execution tasks like code generation, summarization, and data extraction.
AI Data Residency in Canada: A Practical Guide for Businesses
What Canadian businesses need to know about PIPEDA, data residency, and customer data when adopting AI — and when self-hosting is actually the right answer.
Using AI to Monitor Kubernetes Clusters and Make Dynamic Scaling Decisions
How to move beyond static thresholds and use AI-driven observability to detect anomalies, predict traffic patterns, and automate scaling decisions across your Kubernetes infrastructure.