Cloud spend is a relentless beast. With global cloud infrastructure and platform services (CIPS) spend projected to grow 21% to $182B USD in 2025 (Gartner Cloud Infrastructure & Platform Services Forecast 2025), effective cloud cost optimization FinOps 2026 strategies are non-negotiable for SMBs and scale-ups. FinOps isn’t just about cutting costs; it’s about fostering a culture of financial accountability and operational efficiency, embedding cost awareness directly into DevOps workflows.
The data is clear: 28% of cloud spend is wasted (Flexera State of the Cloud Report 2026), and 82% of organizations prioritize optimizing existing cloud costs. Rightsizing alone accounts for 49% of identified savings opportunities (Flexera 2026). This isn’t just a finance problem; it’s a DevOps challenge requiring technical insights and the right tooling.
As Lead DevOps, CTOs, or Cloud Architects, you need to navigate a complex landscape of tools. This guide cuts through the noise, comparing AWS Cost Explorer, Datadog, and Thalaxo, focusing on their capabilities for robust cloud cost optimization FinOps 2026 practices.
Comparing Cloud Cost Optimization FinOps 2026 Tools
Let’s examine the strengths and weaknesses of each solution:
| Feature/Tool | AWS Cost Explorer | Datadog Cloud Cost Management | Thalaxo |
|---|---|---|---|
| Primary Focus | AWS cost visualization & basic recommendations | Observability (metrics, logs, traces) with cost as an overlay | Dedicated FinOps platform for multi-cloud cost optimization |
| Multi-Cloud Support | AWS only | Yes (AWS, Azure, GCP, Kubernetes) | Yes (AWS, Azure, GCP) |
| Rightsizing Recommendations | Basic (EC2, RDS, Redshift) | Yes, integrates with performance metrics | Advanced, threshold-driven (e.g., P95 CPU < 40%) |
| Idle Resource Detection | Manual analysis of reports | Requires custom dashboards/metrics | Automated (e.g., VM CPU < 5% over 24h) |
| Scheduling Automation | No native feature, requires custom scripts | No native feature, relies on external tools/scripts | Native scheduling for non-prod environments |
| Commitment Management (RI/SP) | Yes, purchase recommendations | Basic visibility, not active management | Yes, utilization tracking & purchase recommendations |
| Pricing Model | Free (part of AWS console) | Consumption-based (per host, per metric, etc.) | Subscription-based, often tied to managed spend |
| Strengths |
|
|
|
| Weaknesses |
|
|
|
AWS Cost Explorer: The Native Baseline
AWS Cost Explorer is your starting point for understanding AWS spend. It’s free and provides a good overview of your costs, allowing you to filter by service, region, and tags. You can forecast future costs and get basic recommendations for Reserved Instances (RIs) and Savings Plans (SPs).
While useful for initial insights, its capabilities are limited to AWS and lack the advanced automation needed for proactive FinOps. For instance, detecting idle EC2 instances or suggesting precise rightsizing based on granular CPU/memory utilization often requires custom reports or external tools. Here’s how you might pull some raw cost data:
aws ce get-cost-and-usage \
--time-period Start="2024-01-01",End="2024-02-01" \
--granularity MONTHLY \
--metrics "UnblendedCost" \
--group-by Type=DIMENSION,Key=SERVICE \
--filter '{"Dimensions":{"Key":"SERVICE","Values":["Amazon Elastic Compute Cloud - Compute"]}}'
# Get unblended EC2 cost for a month.
# Filter by service dimension.
This command directly queries your AWS billing data. The output, while detailed, still requires significant manual analysis to translate into actionable savings. For example, rightsizing an m5.large (2 vCPU / 8 GB) in eu-west-1 costing ~$70/month to an m5.medium (2 vCPU / 4 GB) at ~$35/month saves ~$35/month per instance. Identifying these opportunities at scale is where native tools fall short.
Datadog Cloud Cost Management: Observability-Driven FinOps
Datadog excels at observability, providing deep insights into your infrastructure’s performance. Its Cloud Cost Management module extends this by correlating performance metrics with cost data. This allows you to identify overprovisioned resources based on actual utilization, rather than just billing data.
Datadog’s strength lies in its ability to show you why something is costing what it is, by linking it directly to CPU, memory, network, and disk I/O metrics. However, its cost features are an extension of its core observability platform. While powerful for analysis, it doesn’t natively offer automated scheduling or direct actionability for FinOps beyond alerting. Setting up comprehensive cost reports and recommendations often requires significant configuration and custom dashboards.
For multi-cloud environments, Datadog aggregates data well, but converting performance insights into direct cost actions (like stopping an idle Azure VM) often still requires manual intervention or integration with other automation tools. For instance, identifying an idle Azure VM for potential deallocation:
az vm list --query "[?powerState=='VM deallocated'].{Name:name,ResourceGroup:resourceGroup}" -o table
# List deallocated VMs.
# Identify potential idle resources.
While Datadog can show you a VM’s low CPU, you’d still need to execute the deallocation via Azure CLI or a custom script, unless integrated with a broader automation platform.
Thalaxo: The Dedicated FinOps Platform
Thalaxo is built from the ground up for multi-cloud FinOps. It focuses on automated detection of waste and providing actionable recommendations across AWS, Azure, and GCP. Its engine uses specific thresholds to identify opportunities, for example:
- Idle Detection: VMs with average CPU < 5% over 24 hours or stopped for more than 7 days.
- Rightsizing: VMs with P95 CPU < 40% or P95 Memory < 60% are candidates for downsizing.
These thresholds, based on real-world FinOps best practices, translate directly into savings. For an Azure Standard_D2s_v3 (2 vCPU / 8 GB) in West Europe costing ~€71/month, rightsizing to a Standard_B2s (2 vCPU / 4 GB) at ~€33/month yields ~€38/month in savings per instance. Thalaxo automates the identification of these instances at scale.
Beyond detection, Thalaxo includes native scheduling capabilities. Stopping non-production environments during nights and weekends can lead to approximately 65% compute savings (based on 128 hours running out of 168 total hours in a week). This is a significant lever that often requires custom scripting with other solutions.
A key advantage is its multi-cloud approach, consolidating all billing and usage data into a single pane of glass, which is critical as 45% of teams now have a FinOps practice (FinOps Foundation State of FinOps 2025). However, as a younger platform, Thalaxo has limitations: it is not yet SOC 2 certified, lacks native Kubernetes integration, and has a smaller community compared to established giants. For detailed information on integrations, visit our integrations page.
To identify potential idle resources across different clouds, a tool like Thalaxo aggregates this. Manually, for GCP, you might look for unattached disks:
gcloud compute disks list --filter="users:()" --format="table(name,zone,sizeGb)"
# List all unattached disks.
# Unattached disks are often idle waste.
Thalaxo automates the detection and recommendation for such resources across your entire multi-cloud footprint, offering a unified view of your cloud waste, which averages 32% according to the FinOps Foundation State of FinOps 2025.
For more insights on optimizing Azure costs, consider reading our article: Quel est le meilleur logiciel d’optimisation des coûts Azure pour votre PME ? Or, for a broader comparison focused on MSPs, check out: Logiciel gestion facturation Cloud pour MSP : Comparatif AWS Cost Explorer, Datadog et Thalaxo.
The verdict for your profile
Choosing the right tool depends on your specific operational context and scale. Here’s a direct recommendation:
- If you are AWS-only with less than 50 VMs: AWS Cost Explorer, coupled with diligent manual review and basic tagging, is likely sufficient. You don’t need a third-party tool adding complexity or cost at this scale. Focus on foundational FinOps practices within AWS’s native console.
- If you are multi-cloud, or manage over 100 VMs: A dedicated FinOps platform becomes essential. Native tools like AWS Cost Explorer won’t give you a unified view, and manual analysis scales poorly. You need automated detection of idle resources, rightsizing recommendations, and cross-cloud visibility to effectively manage costs and avoid the 28% average cloud waste.
- If observability is already covered by Datadog: Datadog’s Cloud Cost Management is powerful for correlating performance with cost. It’s enough if your primary need is deep analytical insight into why costs are what they are, and you have the internal resources to translate those insights into manual actions or custom automation. However, if you need automated actions like scheduling or proactive commitment management across multiple clouds, Datadog will require significant supplementary tooling or a dedicated FinOps platform like Thalaxo.
Conclusion
Effective cloud cost optimization is no longer optional; it’s a strategic imperative. While AWS Cost Explorer provides a foundational view for AWS-only environments, and Datadog offers deep performance-driven cost insights, dedicated FinOps platforms like Thalaxo are designed to deliver actionable, automated savings across complex multi-cloud infrastructures.
Understanding your cloud spend is the first step, but taking proactive, automated action is where true savings are realized. Explore how Thalaxo’s approach to FinOps can streamline your operations and reduce your cloud bill. Learn more about our approach and pricing.
For further reading on FinOps principles and best practices, visit the FinOps Foundation.