# cloud cost management Published articles for cloud cost management. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Manage Cursor costs with Datadog Cloud Cost Management DevFeed: [Manage Cursor costs with Datadog Cloud Cost Management]() Original publisher: [Read original article]() Author: Dom Nguyen; Doug Gunter Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [cloud cost management](), [cursor](), [finops](), [ai-coding]() Tags: [ai](), [ai-coding](), [analysis](), [cloud-cost-management](), [configuration](), [datadog](), [features](), [filter](), [finops](), [monitor](), [monitors](), [product](), [software]() ### AI overview This tutorial explains how Datadog Cloud Cost Management helps teams analyze Cursor spending by user, model, usage mode, and billing group. It covers identifying cost drivers, detecting unexpected changes, correlating spend with usage, and using monitors, budgets, and dashboards to manage AI coding costs alongside cloud and SaaS spending. ### Source excerpt Analyze Cursor spend by user and model, catch unexpected cost changes, and manage AI coding costs alongside your cloud and SaaS spend. ## How AI Cost Management Agents Connect Cloud Spending to Engineering Workflows DevFeed: [How AI Cost Management Agents Connect Cloud Spending to Engineering Workflows]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-09-11T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [cloud cost management](), [Artificial Intelligence](), [Cloud](), [Monitoring]() Tags: [agents](), [ai](), [cloud](), [cloud-cost-management](), [ec2](), [gpu](), [inference](), [kubernetes](), [llm](), [monitoring]() ### AI overview The article argues that engineers overlook cloud costs because spending data arrives too late and lacks the context needed for action. It explains how AI cost management agents can surface AI and cloud infrastructure spend in real time, flag waste, right-size resources, and enforce budget policies within engineering workflows. ### Source excerpt Engineers ignore cloud costs because of broken feedback loops, not apathy. Learn what AI cost management is, why AEO matters more than ever. | Blog ## Beyond the $1 AI era: How federal agencies can build the evidence for FY27 renewals DevFeed: [Beyond the $1 AI era: How federal agencies can build the evidence for FY27 renewals]() Original publisher: [Read original article]() Author: Jimmy Cesario; Chris Leffler; Sophie Wang Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Chat](), [cursor]() Tags: [ai](), [amazon-bedrock](), [anthropic-claude](), [chatgpt](), [cloud](), [cloud-cost-management](), [cost](), [cursor](), [finops](), [gemini](), [government](), [kubernetes]() ### AI overview Datadog explains how federal agencies can establish AI cost, usage, and value baselines before OneGov promotional pricing expires and FY27 renewal decisions are made. ### Source excerpt Learn how federal agencies can use Datadog Cloud Cost Management to build the cost, usage, and value evidence needed for FY27 AI renewals as OneGov promotions expire. ## How Datadog saves over $1 million each month by optimizing AI usage DevFeed: [How Datadog saves over $1 million each month by optimizing AI usage]() Original publisher: [Read original article]() Author: Bowen Chen; Yosra Chelbi; Nader Khalil; Dmitrii Ulianin Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Optimization](), [Artificial Intelligence](), [cloud cost management](), [Claude](), [Agent Skills]() Tags: [agent-skills](), [ai](), [anthropic](), [claude](), [claude-code](), [cloud-cost-management](), [cost](), [optimization]() ### AI overview Datadog describes how it tracks AI spending and optimizes tool configurations, model choices, and workflows to reduce monthly AI costs by over $1 million while balancing performance. ### Source excerpt Learn how Datadog uses agent evaluations, spending guardrails, and context optimization to save over $1 million each month while balancing cost and performance. ## Proactive FinOps Strategies for Optimizing Cloud Savings DevFeed: [Proactive FinOps Strategies for Optimizing Cloud Savings]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [finops](), [cloud cost management](), [Optimization](), [Cloud](), [Automation]() Tags: [automation](), [cloud](), [cloud-cost-management](), [cost-optimization](), [finops](), [forecasting](), [governance](), [optimization]() ### AI overview The article argues that FinOps teams should move beyond reactive overspend reduction toward proactive savings optimization. It describes continuous visibility, automated governance, forecasting, and ongoing optimization as ways to identify missed cloud savings before inefficiency compounds. ### Source excerpt Shift your FinOps paradigm from overspending to under-saving. Discover proactive cloud cost optimization strategies. Learn more with Harness CCM. | Blog ## Cloud Cost Visibility at Scale: Best Practices DevFeed: [Cloud Cost Visibility at Scale: Best Practices]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Cloud](), [finops](), [cloud cost management](), [Amazon Web Services](), [Amazon EC2](), [Amazon S3](), [Kubernetes](), [Microservice]() Tags: [aws](), [best-practices](), [cloud](), [cloud-cost-management](), [ec2](), [finops](), [how-to](), [kubernetes](), [microservices](), [s3]() ### AI overview The article explains why cloud cost visibility and allocation practices break down as organizations scale across accounts, teams, services, and workloads. It argues that periodic reporting, manual reviews, and static tagging models become ineffective, and highlights FinOps governance and automation as ways to improve control and trust in cost data. ### Source excerpt Discover why cloud cost visibility at scale breaks down and how to regain control. Learn proven FinOps strategies. Explore now. | Blog ## Strategic Cloud Cost Management: Evolution Guide DevFeed: [Strategic Cloud Cost Management: Evolution Guide]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-07-23T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [cloud cost management](), [finops](), [Optimization](), [Platform Engineering](), [Provisioning]() Tags: [aws](), [cloud-cost-management](), [cost-optimization](), [finops](), [governance](), [optimization](), [platform](), [platform-engineering](), [provisioning]() ### AI overview This guide explains the shift from reactive cloud cost monitoring to strategic cloud cost management. It covers the FinOps maturity path, governance frameworks, cost accountability, and optimization practices for aligning engineering and finance around cloud spending. ### Source excerpt Transform from reactive spending to strategic cloud cost management. Learn the FinOps maturity path and optimization tactics. Explore now. | Blog ## Answer any cost question faster with the Cloud Cost skill in Bits Chat DevFeed: [Answer any cost question faster with the Cloud Cost skill in Bits Chat]() Original publisher: [Read original article]() Author: Dom Nguyen Published: 2026-07-17T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [cloud cost management](), [finops](), [observability](), [Artificial Intelligence](), [anthropic](), [OpenAI]() Tags: [ai](), [analysis](), [anthropic](), [bits-ai](), [cloud](), [cloud-cost-management](), [cost](), [finops](), [metrics](), [monitor](), [observability](), [openai](), [optimization](), [root-cause-analysis](), [saas]() ### AI overview This article introduces the Cloud Cost skill in Bits Chat, a conversational workflow for investigating cloud, SaaS, AI, custom, and Datadog costs. It explains how teams can ask cost questions in plain language, investigate anomalies, identify the teams or services driving spend, compare spending with budgets, and correlate cost changes with observability metrics to support root-cause analysis and optimization. ### Source excerpt Use the Cloud Cost skill in Bits Chat to investigate cost anomalies, perform root cause analysis on cost spikes, and get personalized savings. ## Making agentic token costs visible in production DevFeed: [Making agentic token costs visible in production]() Original publisher: [Read original article]() Author: Natasha Silva Published: 2026-07-10T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Bots]() Tags: [agent-observability](), [agentic](), [ai](), [ai-observability](), [cloud-cost-management](), [cost](), [finops](), [instrumentation](), [mcp](), [monitor](), [production](), [tools]() ### AI overview A guide to identifying, reducing, and monitoring token costs in production agentic systems. It explains how tool schemas, session history, and retrieval loops add recurring token usage, and recommends loading only the tools required for each task. ### Source excerpt Learn where agentic token costs come from, how to reduce them across tool definitions, session history, and retrieval loops, and how to monitor spend. ## Improved cost transparency with usage-based billing DevFeed: [Improved cost transparency with usage-based billing]() Original publisher: [Read original article]() Author: Markus Sample Published: 2026-06-25T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [API](), [cloud cost management](), [Cloud](), [observability](), [Monitoring]() Tags: [api](), [cloud](), [cloud-cost-management](), [cost](), [datadog](), [metrics](), [observability](), [product-news](), [usage-based-billing]() ### AI overview Temporal announces general availability of the Temporal Cloud Billing API and Billable Action Count metric, providing more granular cost attribution, usage data, reporting, and optimization signals. ### Source excerpt Learn how the Temporal Cloud Billing API and Billable Action Count metric give teams clearer cost attribution, usage trends, and optimization signals. ## Announcing the Billing API for ClickHouse Cloud with Vantage support DevFeed: [Announcing the Billing API for ClickHouse Cloud with Vantage support]() Original publisher: [Read original article]() Author: The ClickHouse & Vantage teams Published: 2025-03-04T15:19:53Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [SDKs]() Tags: [analytics](), [api](), [clickhouse](), [cloud](), [cloud-cost-management](), [cost](), [duckdb](), [integration]() ### AI overview ClickHouse Cloud announces a Billing API that provides programmatic access to billing and usage data. The article describes Vantage's integration for cloud-cost visibility and compares ClickHouse with DuckDB in a cited benchmark. ### Source excerpt Learn how the new ClickHouse Cloud Billing API helps teams track and optimize costs, with Vantage already integrating it for seamless cloud cost visibility. ## How Groupon Tracks Cloud Costs Across AWS and Google Cloud DevFeed: [How Groupon Tracks Cloud Costs Across AWS and Google Cloud]() Original publisher: [Read original article]() Author: Michael Published: 2023-06-16T20:11:47Z Content type: article Language: en Sources: [Groupon Engineering -- Medium]() Topics: [Cloud](), [Amazon Web Services](), [Databases](), [migration](), [Amazon S3](), [data]() Tags: [amazon](), [aws](), [cloud](), [cloud-cost-management](), [cost](), [data](), [database](), [infrastructure](), [migration](), [s3](), [scale](), [services](), [team]() ### AI overview Groupon describes its cloud migration and its approach to tracking infrastructure costs across AWS and Google Cloud. The article explains how service tags connect cloud resources to individual systems and engineering teams, enabling more detailed cost analysis. ### Source excerpt Groupon completed its migration to the cloud in February 2023. Shortly thereafter our data centers were completely shut down and the equipment cleaned up. The move to the cloud has brought many advantages to Groupon engineering. Our infrastructure can now dynamically scale with our traffic throughout the year and engineering resources can be easily started up or turned off as our needs change. The move to the cloud has also brought a wealth of information regarding our infrastructure costs and utilizations. Groupon operates in both Amazon AWS and Google GCP. Our main consumer and merchant applications reside in AWS while our backend data warehouse and reporting systems are in GCP. Both clouds provide extensive data on service costs including granular usage information. As more Groupon systems migrated to the cloud it became clear there were many opportunities for making our infrastructure and systems more efficient. To do this though we wanted to ensure we were tracking the costs appropriately and making the best decisions with our infrastructure and systems. Cost Tracking The first part of this journey involved improving our cost tracking. While the cloud provides extensive cost data it's important to match this data with individual systems to get a more complete picture. Understanding that we're spending a lot on databases is useful but being able to map that to particular systems and teams gives us more information on the course of actions we can take. Tags are an oft recommended approach to tracking service costs and Groupon is no exception. Groupon applied "service" tags across all our cloud resources. This allows us to map cloud resources to individual services and with it the individual engineering teams. So an RDS database may have the tag service=payments to map that to our Orders and Payments team while an S3 bucket may have service=images to map it to our global image management team. This now allows us to see not only how much we spend overall on databas