Engineering insights, product updates, and the future of cloud infrastructure.
Building a system that can match thousands of bids per second while maintaining sub-millisecond latency required some creative engineering. Here's how we did it.
Our auction engine processes over 100,000 bids per minute across 40 regions. In this deep dive, we explore the distributed systems architecture, the consensus algorithms we use for price discovery, and the lessons we learned building real-time financial infrastructure.
Read ArticleLessons learned from managing massive multi-tenant Kubernetes clusters and the optimizations that made it possible.
New feature announcement: Set custom bidding strategies to automatically balance cost savings with workload requirements.
How we implemented hardware-level isolation, network policies, and encryption to keep tenants completely separated.
A step-by-step guide to deploying a PyTorch model on GPU instances with auction pricing. Save 70% on inference costs.
From 50K to 200K clusters, our incredible second year. A look back at milestones, customer wins, and what's coming in 2026.
Why do prices fluctuate? What drives demand? A data-driven analysis of compute pricing patterns and how to optimize.
Set up continuous deployment with ArgoCD. Automatic rollbacks, drift detection, and policy enforcement for your clusters.
The architecture decisions, chaos engineering practices, and on-call culture that makes our platform highly available.
How we built operators to manage auction-based node provisioning and automatic bid optimization.
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