Metrics collection
CPU, memory, storage, network, application latency, queues and database metrics are reviewed.
Capacity and scaling determines the right compute, memory, storage, network and scaling approach by measuring current utilization, traffic patterns and growth targets.
Capacity planning is not guess-based server sizing. Average and peak load, sustained demand and bursts, and application and infrastructure bottlenecks must be separated.
We combine measurements with business targets and cost constraints to clarify when vertical, horizontal or architectural scaling is justified.
CPU, memory, storage, network, application latency, queues and database metrics are reviewed.
Normal, peak, growth and failure scenarios are modeled.
Resource, application, query, lock, pool and external-service effects are separated.
Vertical and horizontal scaling, caches, queues, replication, load balancing and autoscaling are compared.
Alerts, triggers, headroom and reassessment points are defined.
Assess the current environment, target and dependencies
Document scope, risks, acceptance and rollback
Implement, validate and document
Real metrics provide the strongest result. New systems can begin with load tests, comparable data and explicit assumptions.
No. Application, query, locking or external-service bottlenecks may only make larger hardware more expensive.
We review your current environment, target and technical requirements in a 20–30 minute call. Scope, assumptions, deliverables and pricing are documented before work begins.
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