GCP Cloud Optimization
Mayan.Host audits your Google Cloud billing account, projects, labels, workload architecture, and operating model, then builds and executes a practical optimization plan. We tune Compute Engine, GKE, Cloud Run, Cloud SQL, BigQuery, Cloud Storage, networking, commitments, security, and observability so cost decisions remain tied to reliability and business value.
Best Fit
Optimization works best when the cloud bill is treated as an architecture signal, not just an accounting problem.
Google Cloud project sprawl makes cost attribution difficult when labels, budgets, folders, and billing exports are incomplete.
Elastic platforms still need practical limits, right-sized resources, and a clear policy for baseline versus burst usage.
BigQuery, Cloud Storage, logs, and egress can become major cost drivers when governance is delayed.
Scope
Each recommendation is ranked by savings potential, engineering risk, reliability impact, security impact, and reversibility before anything changes in production.
We establish a clean billing baseline that finance and engineering can both trust.
We tune virtual machines and Kubernetes capacity against observed workload behavior.
Serverless cost is optimized through concurrency, scaling, CPU allocation, memory sizing, and request behavior.
We evaluate commitments only after separating predictable baseline usage from experimental or burst capacity.
We control the data layer where query patterns, retention, and lifecycle decisions drive long-term cost.
We reduce waste while preserving the controls that keep production supportable.
Method
The goal is not a one-time cleanup. It is a repeatable operating loop that keeps spend, performance, reliability, and ownership visible.
We connect billing data to the people, products, environments, and workloads responsible for it.
We turn recommendations and architecture findings into a sequenced delivery plan.
We implement through the same discipline expected from production infrastructure work.
Google Cloud optimization is continuous because usage, product features, and business priorities change.
Deliverables
Outcomes
GCP can be the right platform for analytics, managed services, global infrastructure, and elastic workloads. For steady 24/7 services, Mayan.Host can compare optimized GCP against managed private cloud and build a phased plan when migration makes sense.
Start the Review
Use the form to request a GCP cost review. A Mayan.Host cloud engineer will review your Google Cloud project structure, workload profile, cost drivers, and the level of optimization support you need.
FAQ
Yes. We use them as inputs, then validate recommendations against workload context, ownership, reliability needs, security posture, and roadmap. The final backlog is implementation-focused, not just a list of console suggestions.
Yes. GKE Standard and Autopilot both have valid use cases. We review node pools, workload requests and limits, autoscaling, scheduling, bin packing, availability needs, and operational ownership before recommending either model.
Yes. We review query patterns, scanned bytes, partitioning, clustering, materialized views, scheduled queries, reservations, slot usage, storage, and governance so teams can keep analytics useful without uncontrolled spend.
Not automatically. CUDs are useful when baseline usage is predictable enough. We review coverage, utilization, break-even, migration plans, and product roadmap before recommending spend-based or resource-based commitments.
Yes. Mayan.Host can continue with managed DevOps, SRE, monitoring, incident response, backup, security guardrails, and recurring FinOps review across GCP, AWS, private cloud, or hybrid environments.
