You can try. A lot of platform and engineering teams already have.
They export a billing report. They paste it into Claude. They ask: where is this spend going, and what should we fix? Claude reads the file, finds some patterns, and returns a list of suggestions. Rightsize some instances. Check your auto-suspend settings. Review your commitment coverage.
The suggestions are not wrong. They are just not yours. They describe the waste that is broadly true across most cloud environments, which is exactly what a general-purpose model trained on general-purpose content would produce. Your actual waste, specific to your environment, your usage patterns, your workload architecture, sits in the gap between the generic suggestion and what would actually move your bill. That gap is where the real money is, and getting to it requires manual investigation, manual triage, manual routing to the engineers who can act. By the time that loop completes, the spend has already happened.
Why a general-purpose model cannot answer a specific question about your bill
Ask Claude: "What was my EC2 spend in March?"
To answer that correctly, you need to join data across more than 30 columns in the AWS Cost and Usage Report. Some are additive. Many are not. The number in the CUR is not the number on your bill once credits, EDP discounts, and savings plan drawdowns are applied. The semantic meaning of those fields is not in the file you export. It is built through years of working with how cloud billing data is actually structured, normalized, and connected across providers.
This is the question that comes up in almost every enterprise sales conversation: why do I need PointFive if I can just point Claude at my billing data? The answer is always the same. The most capable model available cannot produce an accurate answer to a basic question about your own cloud bill without semantic meaning attached to every field. That meaning is not something you can paste into a prompt.
What it actually takes to find waste that others miss
PointFive Labs is a team of cloud, data, and AI engineers whose full-time work is finding new waste patterns, validating detection logic, and updating that library as cloud providers change their services, pricing models, and usage patterns. Their findings are specific, counterintuitive, and verified against real billing data.
These are not findings that surface from reading documentation. They emerge from years of looking at how production environments actually behave at scale, and building detection logic precise enough to catch them automatically. The team adds dozens of new saving opportunities every week across cloud infrastructure, data platforms, and AI services.
That work is encoded in DeepWaste: 500+ validated detections across AWS, GCP, Azure, Kubernetes, Snowflake, Databricks, OpenAI, Bedrock, and more. Each one encodes a specific type of inefficiency: a resource sized for peak load running at 3% utilization, a Bedrock model handling classification tasks that could run on a model costing 15x less, a production AI workload routing requests to the most expensive tier when a cheaper one produces equivalent results. Every finding is verified against actual billing before it reaches you.
A team running AI workloads on Bedrock recently found they were on track to run 40-50% over their AI budget for the year. The issue was model selection: high-complexity tasks were being routed to frontier models by default, including tasks where a cheaper model performed identically. No billing dashboard surfaced that pattern. It took detection logic built specifically to catch model-level routing inefficiency across production workloads.
That is a finding Claude cannot produce from your CUR file, because the pattern is not in the CUR. It is in the relationship between invocation logs, model pricing tiers, task complexity, and output quality, analyzed across enough production environments to know what the pattern looks like when it is hiding.
The data problem, and what Brain does about it
PointFive is built as an AI Efficiency OS: a system that runs continuously across your cloud, data platforms, and AI services, surfacing inefficiencies, routing them to the right owners, and verifying every result against actual billing. Chat, Agents, and Apps are how your team interacts with it. The Brain is what makes the answers correct.
Brain is the data and intelligence foundation underneath every module. It is what separates PointFive from connecting an LLM to a billing export.
It works in three layers. InfraFabric connects to 40+ data sources across cloud providers, data platforms, and AI services and normalizes everything: different billing formats, different discount models, different cost allocation schemes. Without it, a model sees raw billing exports. With it, it sees clean, semantically labeled cost data.
DeepWaste sits above that, where the 500+ validated detections live. And Brain also gives your team the ability to bring in their own context: budget files, capacity forecasts, headcount data, product tier mappings. A customer wants to know what it actually costs to serve a free-tier user versus an enterprise user. The billing data is in the platform. The mapping from infrastructure to product tier lives in a Google Sheet. Brain joins them, makes the result queryable by Chat, actionable by Agents, and buildable into an App, all within the same platform, using data that stays where it already lives.
When your FinOps team asks why their AI spend is trending 40% over forecast, the answer requires billing data, invocation logs, model pricing tiers, and the budget file they maintain in a spreadsheet. Getting that answer from a general-purpose model means exporting everything, assembling the context manually, and hoping the joins are correct. Brain is built to do that work accurately, with the semantic layer already in place.
Finding it is only half the problem
Even with perfect detection, most cloud and AI waste does not get fixed on its own. The finding lands in a dashboard. The engineer who owns the resource is in a sprint. The ticket gets deprioritized. The bill keeps running.
PointFive's Agents module closes that gap. It continuously routes findings to the right owners with the context they need: root cause, risk level, business impact, and suggested fix. It routes complex work through Jira, Slack, ServiceNow, or whatever tools the team already uses. Low-risk improvements are applied automatically. Anything consequential requires human-in-the-loop approval before it runs. Every completed action is verified against actual billing data, so savings are confirmed, not estimated.
The operational work that takes a FinOps engineer hours each week runs continuously. Nothing waits for someone to pull a report or chase an engineer for an update.
The honest answer
Asking Claude what is driving your cloud and AI bill will return a general picture of what usually drives cloud and AI bills. It will not return your specific inefficiencies, routed to the right people, with the context needed to act and the verification that confirms the fix worked.
PointFive does that work. It finds what generic tools miss, gets it to the right owners automatically, and measures every outcome against your actual bill.
500+ validated detections. Verified against your billing. Dozens of new saving opportunities are added every week.