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Finally, a cloud cost management tool built for engineers. No more switching between billing consoles, spreadsheets, and monitoring dashboards. Cost Explorer is Costory’s main interface for analyzing cloud spend. It combines billing data from all your connected providers into a single view where you can filter, group, drill down, and overlay events to understand what changed and why.
Costory Cost Explorer showing a multi-cloud cost breakdown across AWS, GCP, and Azure with event overlays
AWS, GCP & Azure·Event correlation·Unit economics

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Connect your cloud providers and explore your costs with full context in under 30 minutes. No credit card required.

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Overview

Cost Explorer pulls normalized billing data from AWS, GCP, and Azure into a single queryable interface. You can group costs by any dimension (provider, service, account, region, team), compare periods, and overlay deploy or pricing events directly on the cost chart. The goal is to replace the workflow of switching between billing consoles, spreadsheets, and monitoring dashboards. Where it fits in your day-to-day:
  • Investigating a cost spike by drilling down from the top-level view to the specific resource responsible
  • Building a saved view scoped to your team’s infrastructure, then scheduling it as a Slack Report
  • Tracking unit economics by combining cloud spend with usage metrics like DAU or transactions

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Key terms used in this article

  • Standard Columns (cos_*): Costory’s unified billing schema. All normalized fields are prefixed with cos_ so you can query across providers using the same field names.
  • Virtual Dimensions: Custom rule-based groupings that map cloud resources to teams, products, or environments, regardless of tagging inconsistencies.
  • View: A saved Cost Explorer configuration with specific filters, group-bys, and date ranges. Views can be scheduled as reports or shared with teammates.
  • Feature Engineering: Costory’s automatic cleanup of billing labels, merging equivalent names like k8s_label_env, env, and environment into one.
  • Waterfall Chart: A visualization showing how individual factors (services, accounts, teams) contribute to an overall cost change between two periods.
See the Glossary for a full list of terms.

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Get started

1

Connect your cloud providers

Link your AWS, GCP, or Azure billing data to Costory.
2

Open Cost Explorer

Navigate to Cost Explorer from the left sidebar. You’ll see your aggregated spend across all connected providers.
3

Group and filter

Use the Group by dropdown to break costs down by provider, service, account, region, or any Virtual Dimension. Add filters to scope the view to a specific team or environment.
4

Drill down into a cost change

Click any segment in the chart to drill deeper. Costory suggests the most relevant dimensions to explore next.
5

Save and share

Save your configuration as a View. From there you can schedule it as a Slack Report, feed it into the Digest for automated anomaly triage, or share the link with your team.

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Key Capabilities

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Drill down to root cause

Start from a high-level cost breakdown and click into any segment to drill down. You can go from a cross-provider view all the way to the specific EC2 instance or GCP project driving a cost change.
Drilling down from a cross-provider cost breakdown to the exact AWS EC2 instance causing a cost spike
You can explore costs using:
  • . Normalized billing data from AWS, GCP, Azure, and Datadog unified into a consistent schema (provider, service, account, region, etc.)
  • Your for business context: Consolidated label groupings that align legacy naming conventions (like “env” → “environment”)
  • Your : Custom business groupings (by team, product, and more)

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AI-Suggested drill-down dimensions

Costory suggests the most relevant dimensions to group by based on your current filters. Instead of guessing which grouping to try next, you get a ranked list of the dimensions most likely to explain what you’re seeing.
Costory suggesting the most relevant dimensions to group by based on the current cost query filters
Costory automatically suggests the most useful dimensions to drill down into based on your current filters — so you reach the root cause faster without guessing which grouping to try next.

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Pre-built FinOps templates

Costory includes templates for common analysis scenarios, pre-configured for your cloud setup:
  • Savings Plan and CUD coverage: track reservation utilization across AWS and GCP
  • Network cost analysis: identify cross-AZ and egress cost drivers
  • Kubernetes waste detection: find over-provisioned pods and idle workloads. See the K8s waste and EKS/ECS visibility guides.
  • BigQuery cost attribution: break down BigQuery spend by dbt model, team, or package. See BigQuery + dbt visibility.
  • EBS and storage cost optimization: spot unattached volumes and underused disks
Selecting a pre-built FinOps template for Savings Plan coverage analysis in Costory
Have a specific use case? Reach out to us — we’re always learning from our users.

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Cost waterfall

The waterfall chart shows which services, accounts, or teams drove a cost increase or decrease between two periods. It answers “where did the extra $10k come from?” at a glance.
Cost Waterfall chart breaking down an AWS cost increase by service, showing which services contributed most to the change

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Event correlation

Overlay technical and business events directly on your cost charts to explain why costs changed:
  • Cloud provider events: Savings Plans purchases, CUD activations, marketplace renewals
  • GitHub events: pull requests merged, production deploys
  • Custom events: push any event via the public API
Cost Explorer timeline showing a cost increase correlated with a GitHub deploy event
A cost spike correlated with a GitHub deploy event You can attach events to saved views so the context travels with the data when you share it with your team. For automated cost change detection, the Digest surfaces the highest-impact changes and generates AI summaries you can share directly.
Correlating a cost spike with a new feature deployment using event overlays in Costory

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Unit economics and custom metrics

Define formulas to track cost efficiency alongside raw spend:
  • Track ROI: Define formulas to track total realized savings. Example: total_savings = total_cost - list_cost
  • Monitor Efficiency: Build custom KPIs for Savings Plan and CUD coverage
  • Measure Unit Economics: Correlate cloud spend with usage metrics to track Cost Per Daily Active User (DAU)
  • Link cost with business metrics: Connect your cloud bill to the metrics that matter
Defining a Cost Per DAU formula in Costory by dividing total cloud spend by daily active users from Amplitude

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Frequently Asked Questions

Costory supports AWS, Google Cloud (GCP), and Microsoft Azure. You can analyze costs across all three providers in a single view.
Use the Unit Economics feature to define custom formulas. Connect your usage metrics (e.g., DAU from Amplitude, transactions from your database) and divide by cloud spend. See the Cost Per Active User guide for a step-by-step example. You can apply the same approach to BigQuery costs with dbt or shared database costs.
Yes. Costory integrates with GitHub to overlay deploy events on your cost charts. You can also push custom events via our API. This makes it easy to explain cost spikes to your team.
Native billing consoles only show one provider at a time and lack context like deploys or usage metrics. Costory gives you a multi-cloud view with event correlation, custom labels, unit economics, and automated Slack reports in one place. Costory also offers Contracted Cost, a stable cost metric that removes misleading Savings Plan fluctuations so your engineering teams can trust the numbers. For guidance on which metric to show each audience, see Cost Metrics Per Persona.

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Next Steps

Cost Per Active User

Track unit economics by correlating cloud spend with DAU

Executive Cost Reports

Build the dashboards your leadership team wants to see

Automated Slack Reports

Send cost digests to your team automatically

Kubernetes Cost Visibility

Break down costs by namespace, workload, and pod

Automate Cloud Budgets

Define per-team budgets and automate alerts when teams exceed targets

Reallocate Shared API Costs

Split shared API costs across teams using an external usage metric
Last modified on March 11, 2026