The FinOps

Every engineering team gets its own weekly cost report in its own Slack channel, scoped to the services it owns.

Split shared EKS cluster costs across teams in proportion to what each team's workloads actually use, measured in Datadog.

Allocate shared databases, networking and platform services to the teams that use them, based on real usage metrics.

Allocate untagged or shared API costs, like a single LLM key or a shared data platform account, to teams based on actual usage.

Break EKS, GKE and AKS costs down by cluster, namespace and workload, and roll them up to teams.

Map accounts, projects, resource names and partial tags to teams with rules, instead of waiting for a tagging project to finish.

Find which label keys could become dimensions, how much spend each one covers, and where untagged cost is concentrated.

Allocate every cost line to prod, staging, dev or sandbox using account names, projects, clusters and labels.

Teams see all their costs in one place, whatever the provider: cloud, Kubernetes, data platforms and LLM APIs.

A monthly, frozen view of allocated cost per cost center, exported to BigQuery or shared as a PDF for finance.

The agent compares what you pay for compute with what services actually use in CloudWatch or Datadog, then recommends instance sizes to the owning team, with the deploy history attached.

Measure the gap between requested and available CPU and memory, convert it to cost, and get request changes per deployment.

The agent watches covered versus uncovered spend and utilization, and recommends where additional commitments could make sense as your steady baseline grows.

Import CUD metadata, see coverage per project and team, and size spend-based commitments on your steady baseline.

Provider events put commitment purchases, marketplace renewals and SKU price changes on the cost timeline, so nothing expires silently.

Surface storage waste and idle resources by owner, so each team gets a short cleanup list with the cost of doing nothing.

Find buckets in expensive storage classes, check access patterns, and get lifecycle and tiering suggestions per owner.

The agent breaks down data transfer by provider, service, account and team, flags jumps and points to the architecture choices behind them.

Use the compute metadata in your billing data to list older instance families by owner and estimate the impact of upgrading.

The agent tracks per-model spend for each AI feature across OpenAI, Anthropic, Bedrock and Vertex AI, and recommends where a cheaper model or a routing change could fit.

Use warehouse and query history from Snowflake account usage to find oversized warehouses, idle credits and the queries that cost the most.

Log each optimization as an event and compare cost before and after, so the team can show what each change saved.

Every Monday, each team gets three optimization candidates for its own services, with evidence, in its Slack channel.

When spend moves, the agent finds the deploy, team and service behind it, checks incidents and provider events from the same window, and posts the likely cause.

When an alert fires in #finops or a team channel, reply in the thread and the FinOps agent investigates: which team, which service, which deploy.

Preview how often an alert would have fired over the past weeks before turning it on, so alerts stay rare and useful.

Every significant cost shift ranked by impact, with an AI summary, delivered to Slack, Teams or email.

Incidents are logged as events. After each one, the agent reports what it cost: retries, autoscaling, failover traffic and extra LLM calls.

Provider events flag SKU price changes and marketplace renewals on the timeline, so unexplained increases get explained.

Combine allocated cloud cost with your business and usage metrics to build unit costs that leadership can track over time.

Correlate cloud cost with Amplitude active users and track cost per thousand MAU or WAU.

Allocate shared infrastructure to tenants using labels, namespaces or usage metrics, and track infrastructure cost per client.

Divide a service's allocated cost by its request volume from Datadog or CloudWatch, and track it release after release.

Use hourly cost data to measure bursty jobs, price each event or batch, and connect optimization work to per-event cost.

Allocate infrastructure to product lines and combine it with revenue from a sheet or warehouse table to track COGS and margin.

Monthly budgets per team, with Slack or email alerts when actual spend or the end-of-month forecast crosses a threshold.

See where each team will land at month end, based on a forecast model, and investigate drifts while there's still time.

A ready-made dashboard and monthly report with target, actual, forecast and delta per team, for the VP Infrastructure review.

Follow credits received and consumed per provider and estimate when they run out, so the switch to paid usage is planned.

Start budget season from 12 months of allocated cost per team, growth trends and known changes, with every version tracked.

A monthly report with an executive summary, root causes and trends, generated from a Cost digest and delivered automatically.

Use Costory's frozen month-end numbers, allocated to cost centers, as the cloud input to your close.

Give teams a usage-driven cost view that doesn't swing when Savings Plan discounts are shared across accounts.

Finance, engineering and leadership each get the cost metric that fits their decisions: billed, contracted or unit cost.

Report in your company currency while providers bill in USD, with consistent conversion rules across AWS, Google Cloud, Azure and Datadog.

Total spend trend, top drivers, budget progress, unit costs and month-over-month comparison in one executive view.

Estimate the month's cloud cost for accruals using month-to-date data and forecasts per cost center.

Match provider invoices with normalized billing data and flag differences.

Capture deploys, PR merges and releases from GitHub or your CI/CD pipeline, and see which ones changed cost.

Track AI coding tool spend per user and per model, from Cursor and the Anthropic Claude Code connector, next to the rest of your cloud.

Connect the Costory MCP to Claude Code so the agent checks what a service costs, how it changed and who owns it before you ship a change.

Each team gets its own alerts in its own Slack or Teams channel, scoped to the services it owns.

Connect AWS, Google Cloud, Azure and Datadog billing with Terraform or CloudFormation, then send your first report.

Add current cost context for the resources touched by a Terraform pull request.

Allocate model spend from OpenAI, Anthropic, Azure OpenAI, Bedrock, Vertex AI and OpenRouter to teams, features and customers.

The agent tracks total AI spend across model APIs, cloud AI services and AI tools, and reports the weekly trend in one place.

Combine model API spend with the GPU and serving infrastructure behind each AI feature, then divide by usage for a true cost per request.

Import voice, video and media generation spend next to cloud and LLM costs, and allocate it to products.

Import per-user Claude Enterprise cost with an Analytics API key, and allocate seats and usage to teams.

Label BigQuery jobs with dbt metadata and allocate processing and storage costs to models, packages and owners.

Allocate Snowflake credits to teams and workloads using warehouses and query history, next to your cloud bill.

Import Datadog usage and billing estimates, then allocate observability spend to the teams generating it.

Import Confluent Cloud and Aiven billing and allocate streaming and managed database costs to teams.