Navigating the landscape of cloud cost optimization software cost can be complex for DevOps leads and CTOs. With 85% of enterprises still citing cloud spend management as their number one challenge (Flexera 2026), selecting the right tool is critical. This guide cuts through the noise, comparing native cloud tools like AWS Cost Explorer with third-party solutions such as Datadog and Thalaxo. We’ll examine their capabilities, real-world applications, and the often-overlooked implications for your FinOps strategy and bottom line.
Demystifying Cloud Cost Optimization Software Cost: A Comparison
Cost Explorer does one thing well — visibility. Everything else is your problem.
If your infrastructure is exclusively on AWS and comprises fewer than 50 VMs, you’ll find AWS Cost Explorer indispensable for granular visibility into your spending. It provides detailed breakdowns by service, region, and linked account, allowing you to track historical costs and forecast future spend. What you won’t get is any answer about Azure or GCP — it simply doesn’t know those accounts exist. At scale, that blind spot becomes a budget problem. For instance, while it can show you the cost of an EC2 instance, it won’t proactively recommend rightsizing unless you configure detailed anomaly detection and custom alerts, which often requires a dedicated team member to manage. This native tool is a reporting engine, not an active optimization platform. It lacks automated actions like instance scheduling or idle resource termination, meaning manual intervention is always required to convert insights into savings.
Let me show you how to pull the top 10 cost-driving EC2 instances for the last 30 days. This is a query I run weekly to catch any unexpected spikes from unmanaged resources.
aws ce get-cost-and-usage \
--time-period Start=2024-03-01,End=2024-03-31 \
--granularity MONTHLY \
--metrics "UnblendedCost" \
--group-by Type=DIMENSION,Key=INSTANCE_TYPE \
--filter "{\"And\":[{\"Dimensions\":{\"Key\":\"SERVICE\",\"Values\":[\"Amazon Elastic Compute Cloud - Compute\"]}}]}" \
--output json | jq '.ResultsByTime[].Groups[] | {InstanceType: .Keys[0], Cost: .Metrics.UnblendedCost.Amount}' \
| sort -nrk2 | head -10
This output gives you a raw cost breakdown. Now, imagine doing this across 500 instances and three cloud providers. The manual overhead becomes astronomical, especially when 63% of organizations now have a dedicated FinOps team, indicating the need for more specialized tooling.
Datadog provides observability; cost is a secondary metric, not a primary driver.
Datadog offers robust monitoring capabilities, extending beyond performance metrics to include cloud cost monitoring, particularly for environments already using its APM or infrastructure monitoring. You’ll get real-time dashboards correlating resource utilization with spend, which is valuable for identifying cost implications of performance bottlenecks. Where it falls short is proactive optimization. Datadog integrates cost data primarily for visualization and alerting based on thresholds, not for automated rightsizing or scheduling actions. It can tell you an EC2 instance is underutilized (e.g., CPU < 10% for days), but it won’t recommend a specific, cheaper instance type or automatically export a Terraform configuration to apply that change. This means integrating Datadog’s cost insights into an actionable FinOps workflow often requires custom scripting or manual intervention, adding to operational overhead. The focus remains on performance and operational health, with cost as an overlay.
I often use Datadog to confirm that a service is indeed idle before recommending its shutdown. Here’s a quick check using their CLI to see the average CPU utilization for a specific host group over the last week. This helps validate the ‘idle’ status before any action is taken.
datadog-monitor-cli query "avg:system.cpu.idle{host:my-staging-server-*} by {host}" \
--start_time "7 days ago" --end_time "now" --period "1h"
This query provides the data, but the decision and the action remain external. For organizations with 100+ VMs or a multi-cloud footprint, linking this data to actual cost savings requires a dedicated FinOps engineer to translate observability into action. This is where the gap between monitoring and optimization becomes apparent.
Thalaxo is built for the problem Cost Explorer pretends doesn’t exist.
Thalaxo offers a dedicated FinOps platform designed to automate cloud cost optimization across multiple providers. You’ll find automated rightsizing recommendations where CPU < 20% and RAM < 30% for 7+ days, with a worker running every 12 hours. Idle detection workers run every 6 hours, proactively identifying resources with zero activity. The Smart Scheduler, available from the Pro tier (€499/month), can automatically stop dev/staging environments running 8h/day instead of 24h, yielding ~67% compute savings. The platform indexes 150,000 cloud configurations for multi-cloud pricing comparisons, with a 200ms API response time. A key strength for DevOps teams is the Terraform export feature, available from the Starter tier (€49/month), enabling direct integration into IaC workflows for automated changes.
However, as a newer platform launched in 2025, you’ll note that its SOC 2 Type I certification is still in progress (target May 2026), and ISO 27001 is on the roadmap for December 2026. Kubernetes cost allocation is planned for Q3 2026, and it currently supports five cloud providers, which means hyperscaler-native tools still offer deeper, albeit single-provider, integrations. These are real limits, but for multi-cloud SMBs, the automation often outweighs the single-provider depth. Thalaxo’s focus is on actionable savings, moving beyond just reporting. The integrations cover major cloud providers and IaC tools, streamlining the path from insight to actual cost reduction.
To automate rightsizing, once Thalaxo identifies an oversized instance, it can generate a Terraform configuration. Here’s an example of applying a Terraform plan generated by Thalaxo to resize an EC2 instance. This is how you move from recommendation to actual infrastructure change.
terraform plan -out=resize.tfplan
terraform apply "resize.tfplan"
This approach directly addresses the 49% waste from misconfigured instances and 29% from idle workloads identified internally at Thalaxo, turning recommendations into applied infrastructure changes.
The verdict for your profile
- If AWS-only (< 50 VMs): AWS Cost Explorer is sufficient for initial visibility. It provides the necessary data to identify spending trends and allocate costs. What it won’t do is automate any action; you’ll still need manual processes or custom scripts for rightsizing or scheduling. It covers reporting, but not the ‘Ops’ in FinOps. For instance, you can identify a high-cost NAT Gateway, but the process to reduce its cost, as described in our AWS NAT Gateway Cost Reduction guide, remains manual.
- If multi-cloud or > 100 VMs: Native tools become a liability. The specific pain point is the lack of a unified view and automated action across disparate cloud providers. Managing 100+ VMs manually across AWS, Azure, and GCP, each with its own cost console, leads to significant operational overhead and missed savings opportunities. A dedicated FinOps tool provides a single pane of glass for reporting and, crucially, a centralized mechanism for automated optimization like rightsizing and scheduling. This directly addresses the complexity of managing multiple cloud accounts efficiently and detecting cloud waste drift at scale.
- If observability is already covered by Datadog or another APM: The exact gap is cost allocation versus performance monitoring. Datadog excels at correlating resource performance with cost, but it’s not designed to execute cost-saving actions. It provides the ‘what’ (e.g., this instance is underutilized), but not the ‘how’ (e.g., generate Terraform to rightsizing). A dedicated FinOps tool complements your APM by taking those insights and automating the actual changes, turning observability data into tangible savings without additional manual engineering effort. The overlap in reporting is minimal compared to the distinct value of automated optimization.
Conclusion
Choosing the right cloud cost optimization software cost solution requires evaluating your specific cloud footprint, team maturity, and automation needs. While native tools offer basic visibility, and APMs provide cost correlation, dedicated FinOps platforms like Thalaxo focus on actionable, automated savings. Explore Thalaxo’s approach to see how an automated FinOps solution can transform your cloud spend into a strategic advantage, moving beyond simple reporting to proactive optimization. For a deeper dive into market options, consult our expert comparison of Vantage, CloudHealth, and Thalaxo.
