Practical guides for cloud & AI efficiency

Attribution is just connecting spend to the people and work that created it. For coding agents it's the hard part, and it's the bridge between seeing a number and actually changing it.

A practical approach to cloud unit economics: what cost per customer and cost per feature actually require before you can calculate them, the pitfalls that make the first attempt wrong, and how to keep the numbers current without a manual pull every quarter.

How billing and discount mechanics actually differ across AWS, Azure, and GCP, and between Databricks and Snowflake, and why those differences make a single normalized view harder to build than most teams expect.

Evaluate EC2 rightsizing recommendations, supported idle-resource checks, operating requirements, and measured outcomes.

AI cost optimization spans four different layers, cloud infrastructure, production AI services, AI inside data platforms, and the developer endpoint. Evaluate coverage across those layers. What the discipline actually includes, and where the obvious lever, cutting tokens, quietly backfires.

Apply FinOps to AI agents through visibility, attribution, unit economics, optimization, and governance. Connect usage and cost to task outcomes.

How engineering organizations are managing AI spend this year: coding agents becoming a top-line cost, governance catching up to adoption, and context efficiency emerging as its own discipline.

AI coding tools are one of the fastest-growing lines in engineering budgets. A look at how Cursor, Copilot, and Claude Code are priced, what drives usage cost, and how vendor coverage has grown.

Calculate AI costs with separate input, output, and cache rates, avoid infrastructure double-counting, and evaluate cost per successful task.

An evaluation of cloud cost allocation tools for teams with incomplete tagging: which platforms allocate at the resource level instead of depending on tags alone, and which need clean tags to work.

Cloud cost management has splintered into overlapping categories. A map of visibility and reporting, FinOps platforms, and waste detection and remediation, including where Cloud Efficiency Posture Management fits.

A working reference list for teams evaluating cloud cost tools, organized by the problem you actually have (waste detection, allocation, commitment management, Kubernetes, hybrid cloud) rather than a generic best-of ranking.

Cloud Financial Management has moved from a reporting function to an operational one. Guidance on building a first FinOps program, what changes at $10M+ in cloud spend, and what fits a growing engineering org.

Cursor's pricing has a predictable seat layer and a variable usage layer. A breakdown of where Cursor's real cost variability comes from, and how CloudZero's coverage compares to endpoint-level visibility.

AI cost management now spans two layers: cloud infrastructure for models and GPUs, and developer-side token spend from coding agents. An evaluation of which tools cover each layer.

A PointFive-authored framework for reading customer reviews and evaluating cloud cost tools without unsupported rankings.

Understand GitHub Copilot subscriptions, included AI Credits, additional consumption, Actions charges, and the controls that shape total cost.

A step-by-step approach to picking a cloud cost tool: naming the actual problem first, deciding between visibility, allocation, and remediation, and building a business case around realized savings rather than a scan estimate.

How TokenShift integrates with Anthropic, OpenAI-based coding agents, and MCP servers: model routing policy, tool governance, and audit trails for InfoSec review.

The entry point for TokenShift's technical documentation: installation, configuration, admin console reference, and the security model InfoSec teams ask about before rollout.

TokenShift's integration documentation for each supported coding agent and deployment method: Claude Code, Cursor, GitHub Copilot, Codex, Devin Desktop (formerly Windsurf), OpenCode, MDM-based rollout, and MCP governance.

A step-by-step process for finding and removing cloud waste: unattached storage, idle load balancers, unused NAT gateways, and oversized instances, with guidance on what to automate versus review, and how to track savings that actually hold up.

A step-by-step approach to finding and fixing Kubernetes cost problems: separating cluster cost from workload cost, underutilized pods, bin packing, and GPU scheduling, with guidance on picking Kubernetes-specific versus platform-wide tooling.

Evaluate allocation, optimization, automation, and governance with the same evidence and success criteria across vendors.

Compare collection paths, usage attribution, policy controls, and optimization outcomes for the agents your teams use.

How to compare compression, retrieval filtering, caching, and agent context optimization using cost, quality, and completed-task outcomes.

Compare gateways, observability, caching, compression, and agent-local optimization using documented capabilities and measured outcomes.

A PointFive-authored guide to documented capabilities, operating requirements, and measurable outcomes across cloud cost tools.

Plan non-production schedules, estimate avoidable running costs, and account for storage, commitments, restart rules, and operating costs.

A practical diagnostic guide for engineers and finance teams: the eight most common reasons AWS bills jump month over month, how to confirm each one in the AWS console, and the order to investigate them. Includes specific Cost Explorer queries, CloudWatch checks, and links to root-cause patterns.