Engineering the Future of Infrastructure
GPUs changed the unit economics of the cloud, and the architecture is following. Inside the ground-up rebuild of the production stack for AI workloads.
More top stories
A practical production checklist covering security, reliability, networking, observability and cost — the configuration that separates a demo from a system that carries revenue.
From Savings Plans to Graviton to the storage classes nobody reads about — a field guide to cutting AWS spend without cutting reliability.
What actually happens between an API call and a token. A tour of the serving stack — batching, KV cache, autoscaling and the cost of every millisecond.
One standard for metrics, logs and traces. How OpenTelemetry actually fits together, and how to adopt it without a big-bang migration.
The paved-path playbook: what to build first, what to buy, and how to earn adoption when nobody is forced to use what you ship.
FinOps is not a cost-cutting team — it is a practice for making spend a shared, engineering-owned decision. Here is how it works.
Declarative delivery is easy in the demo. Secrets, drift, multi-cluster and rollbacks are where GitOps earns or loses trust.
The infrastructure powering the next generation of AI applications.
Latency and errors are not enough. Tracking quality, cost-per-request and hallucination signals for systems whose output is probabilistic.
Production operations, security, observability and cloud-native architecture.
The honest decision tree. When a mesh earns its operational cost, and when a good ingress and library get you there cheaper.
They are used interchangeably and they are not the same thing. The distinction changes how you staff, budget and measure infrastructure work.
Data, benchmarks and analysis from the infrastructure community.
How Indian engineering teams are adopting platform engineering, cloud-native tooling and AI infrastructure. Survey in field.
Cluster sizes, managed vs self-hosted, and the operational maturity curve across the cloud-native community.
A structured benchmark of where organizations recover the most cloud spend, and what separates top-quartile FinOps practices.
Team size, tooling, golden-path adoption and how platform teams measure return on the platforms they build.
Conversations with the people building modern technology platforms.
On the first three hires, resisting the urge to mandate the platform, and how they measured whether it was working.
VP Engineering
A fintech scale-up
Error budgets as a negotiation tool, the on-call redesign that stuck, and what they wish they had automated sooner.
Director of SRE
A B2B SaaS company
Build vs buy for inference, the GPU commitments they would not repeat, and where the real cost hid.
CTO
An AI product company
Practical DevOps, cloud, AI infrastructure and engineering insights — delivered weekly. Read by engineers and engineering leaders.
