Insights on agentic AI, SRE automation, and cloud infrastructure from the engineers who build it.
Your data science team built a model that works. Six months later, it is still not in production. Here is the playbook we use to ship ML models in weeks, not quarters.
A FinTech company's AWS bill hit $65K/month with zero visibility into where the money was going. We brought it down to $26K without touching performance.
If your team dreads Friday releases, your deployment pipeline is the problem. Here is how we make deployments boring, in the best way possible.
Every AWS compute option has tradeoffs. We break down when EKS, ECS, and Lambda actually make sense based on real workloads, not vendor marketing.
AWS offers two very different paths to production AI. We explain the tradeoffs between Bedrock and SageMaker based on real deployment experience.
Lambda is great until it is not. We break down the real cost crossover points and the signs that your serverless architecture is costing you more than containers would.
Traditional APM tools do not cover LLM-specific failure modes. Here is what to monitor, why, and the stack we use to keep AI systems reliable.
GPU instances, model inference, data pipelines, storage. We break down the actual cost structure of production AI workloads and where most teams overspend.
Most EKS clusters run at 20 to 35% utilization. We walk through the specific misconfigurations that cause waste and the tools that fix them.
Most RAG implementations fail because of bad retrieval, not bad models. Here is how we build retrieval-augmented generation pipelines that give accurate, grounded answers.
MCP is the open standard for connecting AI agents to tools and data sources. Here is a practical guide to building MCP servers, integrating them with Claude, and deploying them in production.
AI agents that work in demos often fail in production. Here is how to build, evaluate, and deploy reliable AI agents using real frameworks and battle-tested patterns.