惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Jina AI
Jina AI
博客园 - Franky
Apple Machine Learning Research
Apple Machine Learning Research
酷 壳 – CoolShell
酷 壳 – CoolShell
阮一峰的网络日志
阮一峰的网络日志
量子位
雷峰网
雷峰网
宝玉的分享
宝玉的分享
V
Visual Studio Blog
博客园_首页
小众软件
小众软件
The Cloudflare Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
大猫的无限游戏
大猫的无限游戏
博客园 - 聂微东
S
SegmentFault 最新的问题
博客园 - 【当耐特】
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 叶小钗
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
人人都是产品经理
人人都是产品经理
WordPress大学
WordPress大学

Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis
Analyze cloud costs with flexible spreadsheets in Datadog...
Katherine Broner, Reva Ranka · 2026-05-06 · via Datadog | The Monitor blog

Cloud cost data is most useful when teams can adapt it to their own reporting and planning needs. In addition to viewing cost breakdowns, FinOps teams often need to calculate forecasts, reshape datasets, and present tailored views to finance and leadership teams. In many workflows, those steps happen outside the observability platform. Once the data is exported, it quickly becomes outdated and requires repeated manual updates.

Datadog Sheets addresses this issue in combination with Datadog Cloud Cost Management (CCM). With Sheets, you can analyze CCM cost data across providers, services, and teams by using structured tables that support calculated columns and pivot tables. Sheets also offers flexible spreadsheet-style tabs (in Preview), where you can write formulas, reference table data, and design custom layouts while staying connected to live Datadog data.

In this post, we’ll show examples of how you can use Sheets and CCM to:

  • Track monthly cloud spend by provider

  • Forecast yearly cloud spend by team

A Datadog Cloud Cost Management view with an option to open cost data in Datadog Sheets for further analysis.

Track monthly cloud spend by provider

FinOps reporting often requires combining raw cost data with business-specific transformations, such as currency conversion. These requirements frequently go beyond static tables or dashboards, especially when different stakeholders need different views of the same data.

For example, let’s say that you’re an SRE or a FinOps analyst who is responsible for reporting monthly cloud spend across providers such as AWS, Microsoft Azure, and Google Cloud. Your finance team in EMEA needs costs in euros, and your leadership team wants a clear monthly summary of spend, grouped by provider.

You can start by creating a table in Datadog Sheets that pulls daily cloud cost by provider and service directly from CCM. The CCM-backed table gives you a continuously updated dataset that reflects the latest spend across all providers.

Next, you can create a separate sheet tab to handle currency conversion. For example, you might maintain a small table of exchange rates and use spreadsheet formulas to convert your model from USD to EUR. When exchange rates change, you can update every converted cost in the sheet by changing the value of a single cell.

In another tab, you can aggregate daily costs into a monthly report. Using spreadsheet formulas such as VLOOKUP and SUMIF, you can calculate totals by provider, track month-over-month changes, and organize the data into a format suitable for leadership. Because each step references the original CCM-backed table, the report updates automatically as new cost data arrives.

A Datadog Sheets table that shows monthly cost data by provider.

Forecast yearly cloud spend by team

Budget planning requires combining historical cost data with projections, growth assumptions, and planned changes to infrastructure. These workflows often involve layering business context on top of raw cost data so that teams can plan ahead with confidence.

If you’re planning next year’s cloud budget, you need to estimate how much each team will spend based on current usage, expected growth, and upcoming initiatives. You can begin by creating a table that pulls monthly amortized AWS costs attributed to each team. CCM maps costs to teams based on your infrastructure and resource usage, giving you a reliable baseline for planning.

From there, you can build a forecast in a spreadsheet tab. You might apply different growth rates for each team, account for planned migrations, and define budget targets. Spreadsheet formulas make it possible to adjust assumptions and immediately see how projections change.

As the year progresses, actual costs flow into your CCM table. Because your forecast references that live table, you can compare projected spend against real spend without updating or re-importing data. For example, when January costs arrive, your sheet can immediately show how actual spend compares to your forecast.

A Datadog Sheets table that shows actual monthly costs for 2025 and projected costs for 2026.

Build connected cost analysis workflows in Datadog

Flexible spreadsheet tabs in Datadog Sheets extend CCM by giving teams a place to perform custom analysis alongside live cost data. You can calculate forecasts, apply business logic, and build reports without exporting data or recreating workflows in external tools. To get started, join the Preview for flexible spreadsheets. You’ll get access to a templates gallery of prebuilt spreadsheets for common cloud cost use cases, including the ones covered in this post, that you can customize and reuse.

To learn more about Sheets and CCM, explore the Sheets documentation and the CCM documentation

If you’re new to Datadog, you can sign up for a 14-day free trial to start analyzing your cloud costs.