Your cloud bill keeps growing, but nobody can say which pipeline, job or AI call is driving it. I map every dollar of spend to the workflow and owner behind it, then fix the costly parts in code and infrastructure, not just in a report.

Spend audit on AWS, GCP or Azure, mapped to workloads and owners

Right-sizing of compute, storage lifecycle rules and a savings-plan strategy

Query tuning for BigQuery, Athena and Snowflake

LLM cost control: model routing, caching and smaller models where quality holds

Budgets, alerts and rollback paths so the savings last

Van Data Team cost engagements target a 20 to 60% cut in cloud spend, with reliability guardrails kept in place

Cut LLM cost by about 92% on a Slack sales-KPI reporting agent by moving the summary step to Claude Haiku

Production systems running on AWS ECS Fargate, GCP BigQuery and Azure

Timeline: a typical engagement runs 8 to 14 weeks. We can start with a fixed-scope spend audit.

AWS cost optimization and FinOps reviews

Google Cloud and BigQuery cost reduction

Azure cost optimization and architecture review

Cloud architecture and infrastructure as code with Terraform

LLM and AI inference cost reduction

Pricing: the starting price covers a 2-week fixed-scope spend audit with a prioritized savings plan. Implementation expands to your requirements.