Notes from the team building C3X. Cost estimation for Terraform, the economics of cloud infrastructure, and how to ship FinOps tooling without a SaaS gate.
EC2 prices vary by region, and the cheapest regions for a given instance are usually the large US regions. Choosing a low-cost region for latency-tolerant compute is easy savings. Here is how to find and use the cheapest region.
Azure prices services differently by region, and the cheapest regions for compute are often large US and Western European regions. Choosing a low-cost region for latency-tolerant workloads is easy savings. Here is how to pick one.
GCP prices Compute Engine and other services differently by region, with US regions often cheapest. Choosing a low-cost region for latency-tolerant workloads is simple savings. Here is how to find and use the cheapest GCP region.
Region choice affects compute price, data transfer, latency, and compliance all at once. The cheapest region is not always the right one. Here is a framework for choosing a region that minimizes total cost without hurting users.
CI/CD cost comes from compute minutes, caching misses, oversized runners, and redundant builds. Right-sizing runners, caching aggressively, and skipping needless work are the biggest levers. Plus, pipelines are the place to catch cloud cost before deploy.
Budget alerts notify you when spend crosses a threshold or is forecast to, on AWS, Azure, and GCP. Setting them up per team and service, with actionable thresholds, is the simplest cost safety net. Here is how to configure them well.
A content delivery network bills mostly for data transfer out and requests, and the rates vary by provider and region. Here is how CloudFront, Google Cloud CDN, and Azure CDN compare, and how to keep CDN cost predictable.
A good cost dashboard turns an opaque bill into an actionable picture: spend by team and service, trend, unit cost, commitment health, and anomalies. Here is what belongs on a cloud cost dashboard and how to make it drive decisions.
Unit economics ties cloud spend to a business metric, cost per customer, per transaction, per request, so you measure efficiency instead of just total dollars. This guide covers which units to pick, how to compute them, and how to improve them.
CloudFront bills for data transfer out, requests, and optional features, and the biggest savings come from raising cache hit ratio, choosing the right price class, and using commitments. Here is a complete guide to optimizing CloudFront cost.
CloudWatch can quietly become a large line item through custom metrics, high-cardinality dimensions, verbose logs, and frequent API calls. Here is what drives a CloudWatch surprise bill and how to keep observability cost under control.
Standard reserved instances offer the deepest discount but lock you into an instance family; convertible RIs discount a bit less but let you change instance type. Here is how to choose based on how stable your workload is.
A cloud load balancer bills for an hourly rate plus a capacity or data-processing unit, so cost scales with both uptime and traffic. Here is how load balancer pricing works across AWS, GCP, and Azure, and how to keep it low.
Managed message queues like SQS, Pub/Sub, and Azure Service Bus bill per request or per message, sometimes plus data and features. Here is how queue pricing works, what drives the bill, and how to keep messaging cost low.
Object storage bills for stored GB, requests, and data retrieval or egress, with storage classes that trade retrieval cost for lower storage price. Here is how S3, Google Cloud Storage, and Azure Blob compare and how to pick the right tier.
Managed Redis (ElastiCache, Memorystore, Azure Cache) bills mostly for the node size and count you run, per hour, plus data transfer and optional replicas. Here is how Redis pricing works and how to size it without overpaying.
Cost per customer, cloud spend divided by customers served, is the unit metric that drives SaaS gross margin. Knowing it, and reducing it as you scale, is essential to healthy economics. Here is how to measure and improve it.
Replicating data across regions for durability or latency adds cross-region data transfer charges plus duplicated storage, and the transfer is often the bigger surprise. Here is how cross-region replication is priced and how to control it.
Data transfer out, egress to the internet, is one of the least understood and most surprising cloud charges. Inbound is usually free, outbound is not, and it scales with traffic and region. Here is how egress pricing works and how to cut it.
Data warehouse cost is dominated by how much compute you provision and how long it runs. Sizing the warehouse to the workload, and scaling it down or off when idle, is the biggest lever. Here is how to size for cost.
Connection pooling lets a database serve more clients with fewer resources, so you can run a smaller, cheaper instance. The cost impact comes from avoiding over-provisioning driven by connection limits. Here is how pooling saves money.
Databricks cost is DBUs (the platform fee) plus the underlying cloud compute. Optimizing means right-sizing clusters, using autoscaling and spot, choosing the right compute type, and avoiding idle clusters. Here is the full playbook.
Comparing on-premise datacenters to cloud means looking past the sticker price to total cost of ownership: hardware, power, staff, utilization, and elasticity. Cloud is not always cheaper, but it shifts the cost shape. Here is how to compare.
Datadog bills by hosts, log ingestion and retention, custom metrics, and per-product SKUs, and it can quietly become one of your largest cloud-adjacent bills. Here is how to find and cut the drivers of Datadog cost.