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Key takeaways
1
Don't migrate more than you need to. You may want to change commitment management while retaining Datadog workflows your Finance or Engineering teams still use.
2
Compare like for like. Establish your current coverage, utilization, commitments, and savings before evaluating a different approach.
3
Make the handoff controlled. Evaluate Usage.ai first, review access and economics, establish purchasing authority, and validate the first commitment before expanding.
If you’re considering moving from Datadog Cloud Cost Management to Usage.ai, don’t start by asking which platform has more features. Rather you should be evaluating which parts of your workflow actually need to change and what happens to the commitments you’ve already made.
Datadog covers a broad set of cloud-cost and Engineering workflows, including visibility, allocation, budgets, forecasting, recommendations, and commitment programs. You can review the current scope in Datadog’s Cloud Cost Management documentation.
Usage.ai takes a more focused approach to cloud commitment optimization and management. So the safest migration isn’t about replacing everything. It’s about changing how commitments are evaluated and managed while keeping the workflows that already work for your team.
In this guide, you’ll learn how to baseline your current Datadog setup, separate commitment management from the workflows you want to keep, evaluate Usage.ai, and make a controlled handoff without disrupting your existing cloud commitments.
Datadog covers a broad set of cloud-cost and Engineering workflows, including visibility, allocation, budgets, forecasting, recommendations, and commitment programs. You can review the current scope in Datadog’s Cloud Cost Management documentation.
Usage.ai takes a more focused approach to cloud commitment optimization and management. So the safest migration isn’t about replacing everything. It’s about changing how commitments are evaluated and managed while keeping the workflows that already work for your team.
In this guide, you’ll learn how to baseline your current Datadog setup, separate commitment management from the workflows you want to keep, evaluate Usage.ai, and make a controlled handoff without disrupting your existing cloud commitments.
A practical approach looks like:
Baseline → separate workflows → evaluate → compare → hand off → verify
Migration at a glance
| Phase | What you do | Output |
|---|---|---|
| Baseline | Capture current cost and commitment data | Comparable starting point |
| Workflow review | Decide what stays with Datadog and what moves | Keep/move list |
| Portfolio review | Review existing commitments and expirations | Reassessment list |
| Usage.ai evaluation | Run the Savings Test | Savings opportunity |
| Economics | Compare current and proposed approaches | Go/no-go decision |
| Handoff | Establish access and purchasing authority | Controlled cutover |
| Verification | Compare results with your baseline | Validated migration |
| Cleanup | Remove unnecessary dependencies | Completed transition |
Before you migrate: establish your baseline
Before changing anything, capture how commitment decisions are made today. Datadog’s Commitment Programs documentation can help you review commitment inventory, coverage, utilization, realized savings, and underused commitments.
Capture:
Current cloud spend and accounts in scope
Services and regions
Existing commitments, utilization, and coverage
Expiration dates and renewal process
Realized savings and underutilized commitments
Current purchasing and approval workflow
For AWS, also preserve your provider-side commitment data. AWS Savings Plans documentation explains how coverage works, while AWS Cost Explorer provides native Savings Plans utilization and coverage data.
Finally, document the Datadog workflows your teams still rely on, such as cost allocation, budgets, forecasting, cost monitors, dashboards, and Engineering cost analysis.
Not everything needs to move just because commitment management does.
Finally, document the Datadog workflows your teams still rely on, such as cost allocation, budgets, forecasting, cost monitors, dashboards, and Engineering cost analysis.
Not everything needs to move just because commitment management does.
Migration principle:
Preserve the baseline first. Decide what to change second.
Separate your commitment portfolio from your Datadog workflows
Your existing commitments are separate from the decision about your management platform. Before changing anything, sort them into three groups.Healthy commitments
If a commitment is well utilized and still has meaningful time remaining, there may be no reason to change it. Moving to Usage.ai doesn’t mean replacing commitments that are already working.Commitments approaching expiration
These are the natural points to reassess. Instead of automatically renewing, use the opportunity to compare your current approach with what we identify through the Usage.ai Savings Test. For AWS, review the current Savings Plans terms and purchasing guidance before making assumptions about changes or cancellation.Underutilized commitments
Review these individually. An existing commitment doesn’t disappear when you change management platforms, so separate the cost of today’s commitment from the decision about tomorrow’s purchasing.The goal isn’t to replace everything. It is to keep what works and use better data to make the next commitment decision.
Also read: Datadog Cloud Cost Management Reviews: Is It Worth It in 2026?
Evaluate Usage.ai before changing purchasing authority
This is where we recommend starting. Run the Usage.ai Savings Test using your existing baseline, then compare the opportunity with your current approach.Look at:
Coverage and utilization: Where is spend uncovered or existing commitments underused?
Recommendations: What commitments do we identify, and what usage supports them?
Economics: What savings remain after applicable Usage.ai fees?
Downside: What happens if committed usage falls?
Protection: Which commitments qualify for protection under the applicable Flex Commitment Program terms?
Our security and compliance documentation explains the read-only evaluation path and access model. This lets you validate the opportunity before changing purchasing authority.
Compare the economics, including the downside
Don’t compare vendor fees in isolation. Compare the outcome:
Gross commitment savings − applicable Usage.ai fees = net savings
Then test three scenarios:
| Scenario | Question |
|---|---|
| Base | What if usage stays broadly consistent? |
| Downside | What if committed usage falls materially? |
| Growth | What if usage grows faster than expected? |
Commitment economics ultimately depend on actual utilization. For AWS, compare your historical usage with Savings Plans pricing and commitment mechanics rather than treating published discounts as guaranteed realized savings.
Our pricing model is based on a percentage of realized savings generated through the Flex Insured Commitment Program. For qualifying Flex Commitments, our cashback program provides protection when the commitment cost exceeds the equivalent on-demand cost for the same usage, subject to applicable terms.
Our pricing model is based on a percentage of realized savings generated through the Flex Insured Commitment Program. For qualifying Flex Commitments, our cashback program provides protection when the commitment cost exceeds the equivalent on-demand cost for the same usage, subject to applicable terms.
The question isn’t which platform reports more savings. It’s what net outcome you retain under the same usage assumptions.
Review access and purchasing control
Before changing the operating model, have FinOps, Security, and Procurement review the access and purchasing requirements.Our security and compliance documentation explains our access model, while the AWS integration guide documents the AWS connection requirements. For AWS, your Security team can also review these alongside AWS IAM best practices.
Before enabling commitment management, confirm:
Accounts and commitments in scope
Required cloud-provider permissions
Who approves purchases
Who can execute purchases
How access will be reviewed or revoked
Treat this as an explicit approval gate before handing over commitment-purchasing authority.
Once you approve a recommendation, we initiate the applicable commitment purchase through the cloud provider API. The resulting commitment is identified as a Flex Commitment in the Usage.ai dashboard.
See our Flex Commitment Program documentation for the current operating model and eligibility requirements.
Keep the first move small, validate the outcome, then scale.
Datadog provides a broader set of capabilities across cost visibility, allocation, budgets, forecasting, cost recommendations, and commitment programs.
If your Engineering or FinOps teams use those workflows, you may choose to keep Datadog alongside Usage.ai and change only how commitments are evaluated and managed.
If you’re considering reducing your Datadog footprint, first confirm that the workflows you rely on have suitable alternatives. Then evaluate that change separately from the commitment-management migration.
How to migrate from Datadog to Usage.ai
Once the economics support the move, keep the transition simple. You don’t need to change everything at once.1. Lock the baseline
Save your Datadog commitment inventory, coverage, utilization, realized savings, expiration dates, and the historical period used in your analysis. This gives you a clean reference point after the transition.2. Map your Datadog dependencies
Take a quick look at which CCM workflows your teams actually use and need to keep. Don’t create a bigger migration than necessary.3. Classify your commitments
Group existing commitments into three buckets: healthy, approaching expiration, or requiring reassessment. This helps separate what can stay in place from what needs a fresh decision.4. Run the Usage.ai Savings Test
Connect the relevant cloud environment and use the Savings Test to evaluate new opportunities against the baseline you’ve already established.5. Validate the economics
Review the recommendations using the same scope, historical period, pricing assumptions, and usage scenarios as your original analysis. The goal is a like-for-like comparison.6. Establish one purchasing authority
Decide exactly when new commitment purchasing moves to the new workflow. Your existing commitments can continue operating while you make that transition.7. Start with a controlled commitment
There’s no need to optimize the entire portfolio on day one. Start with a stable, well-understood opportunity.Once you approve a recommendation, we initiate the applicable commitment purchase through the cloud provider API. The resulting commitment is identified as a Flex Commitment in the Usage.ai dashboard.
See our Flex Commitment Program documentation for the current operating model and eligibility requirements.
8. Verify the result
Compare the first results with your baseline:- Coverage
- Utilization
- Realized savings
- Commitment performance
- Usage.ai fees
- Applicable cashback
9. Expand only after validation
If the first transition performs as expected, expand gradually. If the results differ materially from the approved business case, pause, understand why, and adjust before making additional purchases.Keep the first move small, validate the outcome, then scale.
What happens to Datadog?
Moving commitment management to Usage.ai doesn’t necessarily mean replacing Datadog. The right answer depends on which parts of Cloud Cost Management your team relies on today.Datadog provides a broader set of capabilities across cost visibility, allocation, budgets, forecasting, cost recommendations, and commitment programs.
If your Engineering or FinOps teams use those workflows, you may choose to keep Datadog alongside Usage.ai and change only how commitments are evaluated and managed.
If you’re considering reducing your Datadog footprint, first confirm that the workflows you rely on have suitable alternatives. Then evaluate that change separately from the commitment-management migration.
In other words:
move the part that needs to change, and keep the parts that are still delivering value.
That is not evidence of surprise fees. Buyers can find the model reasonable and still struggle to forecast it without their own commercial terms.
Datadog → Usage.ai migration checklist
Use this checklist to keep the migration controlled, measurable, and easy to validate at each stage.Before
Capture your current Datadog commitment baseline
Record coverage, utilization, savings, and expiration dates
Map Datadog CCM workflows and dependencies
Separate existing commitments from future purchases
Evaluate
Run the Usage.ai Savings Test
Match the baseline and scope
Review Usage.ai access requirements
Compare gross and net savings
Model base, downside, and growth scenarios
Review applicable Flex Commitment terms
Transition
Establish one purchasing authority
Select a stable first commitment
Approve the recommendation
Verify the cloud-provider purchase
Monitor actual performance
Expand
Reconcile results against the baseline
Validate Finance reporting
Review utilization and realized savings
Expand only after the first transition passes
Also read: Usage.ai vs Datadog Cloud Cost Management: Visibility or Executed Savings?
If Datadog is delivering the cost visibility, allocation, forecasting, engineering workflows, and commitment analysis your organization needs, keeping those capabilities may make sense. Datadog’s current product positioning combines cloud-cost data with broader observability and Engineering workflows.
A migration is worth pursuing when your evaluation shows a specific improvement in net economics, commitment-risk management, purchasing control, or operating model.
The best way to find out is to test the decision against your own data. Bring your current commitment inventory, representative cloud-spend data, and one realistic downside scenario.
We’ll help you compare the current approach with the Usage.ai model and show where the economics differ. Talk to an Expert
Should you migrate from Datadog to Usage.ai?
Not automatically.If Datadog is delivering the cost visibility, allocation, forecasting, engineering workflows, and commitment analysis your organization needs, keeping those capabilities may make sense. Datadog’s current product positioning combines cloud-cost data with broader observability and Engineering workflows.
A migration is worth pursuing when your evaluation shows a specific improvement in net economics, commitment-risk management, purchasing control, or operating model.
The best way to find out is to test the decision against your own data. Bring your current commitment inventory, representative cloud-spend data, and one realistic downside scenario.
We’ll help you compare the current approach with the Usage.ai model and show where the economics differ. Talk to an Expert
Frequently asked questions
Do I need to replace Datadog when I move commitment management to Usage.ai?
No. You can continue using Datadog for the cost-management, Engineering, and observability workflows your team relies on while using Usage.ai for commitment optimization and management.
What happens to my existing AWS commitments when I migrate?
Changing your management platform doesn't transfer or replace commitments already purchased in your AWS account. Existing commitments continue according to the cloud provider's terms. The migration primarily changes how you evaluate and manage future purchases.
Can I evaluate Usage.ai before giving it purchasing authority?
Yes. Usage.ai provides a read-only evaluation path through the Savings Test. You can use this to assess potential savings and access requirements before enabling commitment management.
How should I compare Usage.ai with my current Datadog approach?
Use the same cloud scope, historical period, usage assumptions, and pricing basis. Compare coverage, utilization, gross savings, applicable Usage.ai fees, and downside scenarios rather than comparing headline savings or vendor fees alone.
Do I need to move my entire commitment portfolio at once?
No. A gradual transition is often easier to validate. You can keep existing commitments in place, start with a stable opportunity, verify the results against your baseline, and expand only when the first transition performs as expected.
Disclosure: Datadog information in this guide is based on public materials reviewed September 2, 2026. Datadog’s product capabilities, pricing, and terms may change. Usage.ai information is based on public documentation reviewed September 2, 2026. Usage.ai fees, cashback, eligibility, protection, permissions, and operating terms are subject to applicable terms.
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