Your Data Platform Should Not Cost This Much to Run
Cloud infrastructure that's right-sized, cost-attributed, and built to stay performant as your data volumes, pipeline complexity, and team all scale.
Explore Our Infrastructure Architecture ↓Snowflake credits burning and nobody knows why
Full cost attribution mapped to teams and workloads
Infrastructure set up manually with no documentation
Terraform-managed infrastructure your team can reproduce
Queries running slow with no visibility into why
Optimized query patterns and warehouse sizing that perform
Cloud bill growing with no clear explanation
Right-sized data infrastructure with spend you can justify
The Cloud Bill Is a Data Engineering Problem, Not a Finance Problem
When cloud bills grow faster than the business value the data platform delivers, the root cause is almost never the cloud provider's pricing. It is the infrastructure decisions made under delivery pressure: overprovisioned warehouses, inefficient query patterns, pipelines running on oversized compute, and nobody tracking which workloads are generating which costs.
Anavii approaches cloud infrastructure as a data engineering discipline. We audit your existing data platform spend, identify the highest-cost workloads, and optimize the infrastructure layer from the inside: warehouse sizing, pipeline scheduling, query performance, storage tiering, and Infrastructure as Code that makes every configuration reproducible and auditable.
The result is a data platform that performs reliably, scales predictably, and costs what it should with full visibility into where every dollar of compute spend is going.
Architectural Solutions
Core cloud infrastructure deliverables for data organizations from initial platform setup to ongoing cost optimization and performance management.
Cloud Data Platform Setup
Infrastructure-as-Code provisioning of data platform environments across AWS, GCP, and Azure. Reproducible environment configuration, IAM and security frameworks, and data-specific network architecture built for the reliability your pipelines require.
Snowflake & BigQuery Optimization
Warehouse sizing, auto-suspend configuration, clustering key design, materialization strategy, and query performance tuning. Cost attribution mapped to specific workloads, teams, and business functions so every credit is accounted for.
Pipeline Infrastructure Right-sizing
Airflow and Prefect cluster optimization, DAG scheduling efficiency, compute allocation for dbt runs, and storage tiering for pipeline outputs. Eliminating overprovisioned infrastructure that runs at a fraction of its allocated capacity.
DataOps & CI/CD for Data Teams
Automated testing, deployment pipelines, and environment promotion for data infrastructure changes. Version-controlled configuration, rollback capabilities, and monitoring that catches infrastructure failures before they affect downstream systems.
What the Best Data Platform Teams Are Building Now
Cloud optimization for data teams has expanded beyond cost cutting. These are the engineering practices that define a modern, production-ready data platform infrastructure.
FinOps for Data Organizations
Cost attribution frameworks that map every dollar of cloud spend to specific data products, teams, and business outcomes. Chargeback models, budget alerting, and optimization dashboards that give engineering and finance a shared view of data platform economics.
Multi-Cloud Data Architecture
Data infrastructure designed to run workloads across AWS, GCP, and Azure without vendor lock-in with consistent security controls, unified cost visibility, and the flexibility to use the best service for each workload type.
Observability & Self-Healing Infrastructure
End-to-end monitoring of data pipeline health, query performance, and infrastructure costs with automated remediation for common failure patterns. Infrastructure that surfaces problems before they become incidents.
Production-Grade Cloud Infrastructure Tooling
The cloud and infrastructure stack selected for data workload reliability, cost transparency, and long-term operational maintainability.
AWS
GCP
Azure
Terraform
Docker
Kubernetes
Datadog
Grafana
GitLab CI
GitHub Actions
Snowflake
BigQuery
Redshift
Databricks
Airflow
Prefect
dbt
The Execution Methodology
From cloud spend audit to optimized production infrastructure prioritized by cost impact and performance improvement, with complete documentation throughout.
Context & Alignment
Audit of your existing cloud data infrastructure current spend by workload, warehouse configuration, pipeline compute allocation, storage architecture, query performance patterns, and cost attribution gaps.
Strategic Architecture
Infrastructure optimization roadmap prioritized by cost impact and performance improvement. Target-state architecture designed for your specific data workloads documented and aligned before any changes are made to production environments.
Build & Operationalize
Infrastructure-as-Code implementation, warehouse optimization, pipeline right-sizing, CI/CD pipeline setup, and monitoring configuration delivered with complete documentation so your team can extend and maintain the infrastructure independently.
Optimize & Scale
Ongoing cost attribution review, query performance monitoring, infrastructure efficiency analysis, and optimization as data volumes grow, new pipelines are added, and team size expands. Built to stay cost-efficient as the platform scales.
Ready to Make Your Data Platform Cost What It Should
We review warehouse sizing, idle compute, query patterns, DAG scheduling, storage tiering, and workload cost attribution, then deliver a prioritized roadmap for reducing costs without sacrificing performance.
Book a Cloud Infrastructure Audit