Engineering Blog

DynamoDB is one of AWS's most powerful databases — but most teams that struggle with it are making the same schema design mistakes. Here's what to know before you build.

Kubernetes jobs creating metric loss and cardinality explosions? Learn how vmagent streaming aggregation solves observability for ephemeral workloads at scale.

Tokenomics is the discipline of measuring and attributing AI token consumption to customers, features, and agents. Here's why traditional FinOps tagging breaks down for AI spend.

Part 2 of our Iceberg data lake series walks through the hands-on implementation: deploying Debezium Server on GKE to replicate Postgres into Iceberg tables on GCS.

AI infrastructure breaks the tagging and SDK playbook that powered traditional cloud cost allocation. Kernel-level eBPF measurement offers a structural fix without code changes.

eBPF sensors run sandboxed programs inside the Linux kernel to observe every syscall, packet, and GPU interaction. Here's why they're becoming the foundation for modern AI cost attribution.

Tokens tell you what AI costs, but not why. A former FinOps practitioner explains why attribution — not tagging — is what turns cost numbers into real business decisions.

Only 15% of enterprises can calculate AI ROI without friction. The real bottleneck isn't proving value, it's attributing token and compute costs to the outcomes they produce.

Stopping a GPU instance for maintenance can mean losing it to the shared capacity pool. Here's how On-Demand Capacity Reservations keep your EC2 GPUs available when you need them back.

FinOps for AI extends cloud cost discipline to AI workloads, but tags can't reach shared GPUs, LLM gateways, or agentic spend.