SaaS Guide: Smartly Manage Multiple AWS Accounts Cost

The Challenge: Optimizing Cloud Spend Across Multiple AWS Accounts

For DevOps leads and CTOs at SMBs/scale-ups, managing cloud costs is a persistent challenge. The complexity compounds when operating across multiple AWS accounts or embracing a multi-cloud strategy. A dedicated SaaS manage multiple AWS accounts cost solution becomes critical to achieving true FinOps maturity. Organizations on average waste 28% of their cloud spend (Source: Flexera State of the Cloud Report 2026), with 49% of that waste attributed to misconfigured instances. Native tools offer a starting point, but often lack the automation and cross-cloud visibility needed at scale.

AWS Cost Explorer: The Native Baseline

AWS Cost Explorer provides granular insights into your AWS spend, allowing you to visualize, understand, and manage your AWS costs and usage over time. It’s built into the AWS console and offers a robust free tier for its core functionalities.

Strengths:

• Native Integration: Seamless access to all AWS billing data, including Reserved Instances (RIs) and Savings Plans utilization.
• Granular Data: Detailed cost breakdown by service, resource, and even tags, offering high fidelity for AWS-specific analysis.
• Forecasting: Can project future costs based on historical usage patterns.

Weaknesses:

• AWS-Only Focus: By design, it provides zero visibility into costs from other cloud providers like Azure or GCP, making multi-cloud FinOps impossible.
• Limited Automation: Primarily a reporting and visualization tool. It doesn’t offer automated rightsizing, idle resource termination, or scheduling capabilities.
• UI Complexity: Can be overwhelming for new users, requiring significant time to configure custom reports and dashboards.

# Example: Get daily unblended costs for EC2 for the last 7 days
# Requires AWS CLI configured with appropriate permissions
aws ce get-cost-and-usage \
  --time-period Start="2024-06-01",End="2024-06-08" \
  --granularity DAILY \
  --metrics UnblendedCost \
  --group-by Type=DIMENSION,Key=SERVICE \
  --filter '{"Dimensions":{"Key":"SERVICE","Values":["Amazon Elastic Compute Cloud - Compute"]}}'

Datadog: Observability with a Cost Lens

Datadog excels as an observability platform, correlating metrics, logs, and traces. While its primary focus is performance monitoring and incident response, it has expanded to include cloud cost management features, especially for identifying resource inefficiencies tied to application performance.

Strengths:

• Performance-Cost Correlation: Unique ability to link application performance metrics (e.g., CPU utilization, latency) directly to cloud spend, helping identify over-provisioning that doesn’t improve performance.
• Multi-Cloud Monitoring: Strong multi-cloud and hybrid-cloud monitoring capabilities, consolidating operational data from various environments.
• Container Visibility: Excellent for identifying over-provisioned Kubernetes pods or idle containers (54% of containers are overprovisioned, 29% are idle – Source: Flexera State of the Cloud Report 2026).

Weaknesses:

• Cost Allocation Secondary: While it identifies inefficiencies, its core strength isn’t granular cost allocation or advanced FinOps automation like automated scheduling or reserved instance recommendations.
• Pricing Model: Can become expensive when fully deployed across an entire infrastructure for deep observability *and* cost features, potentially duplicating efforts if a dedicated FinOps tool is also used.
• No Direct Remediation: It flags issues but doesn’t offer one-click rightsizing or resource termination; actions must be taken manually or via separate automation tools.

# Example: Check current CPU utilization for a specific instance using AWS CLI
# This data would then be ingested by Datadog for correlation
aws cloudwatch get-metric-statistics \
  --namespace AWS/EC2 \
  --metric-name CPUUtilization \
  --dimensions Name=InstanceId,Value=i-0abcdef1234567890 \
  --start-time $(date -v-1H +%Y-%m-%dT%H:%M:%SZ) \
  --end-time $(date +%Y-%m-%dT%H:%M:%SZ) \
  --period 300 \
  --statistics Average

Thalaxo: Multi-Cloud FinOps Automation

Thalaxo is a newer SaaS platform designed specifically for multi-cloud FinOps, focusing on automated cost optimization. It targets the common pain points of waste and lack of visibility across diverse cloud environments.

Strengths:

• Multi-Cloud by Design: Supports 5 major cloud providers, offering a unified view and optimization strategy across AWS, Azure, and GCP. Its multi-cloud pricing engine indexes 150,000 configurations with 200ms API response times.
• Automated Rightsizing: Identifies over-provisioned VMs where CPU < 20% sustained and RAM < 30% for 7+ days, with a worker running every 12 hours.
• Idle Resource Detection: Automatically flags and recommends termination for idle resources, with a worker running every 6 hours, leading to 100% cost recovery for those resources.
• Smart Scheduler: Available from the Pro tier, this feature can achieve ~67% compute savings by automatically stopping non-production environments (e.g., Dev/staging running 8h/day instead of 24h).
• Terraform Export: Allows exporting optimization recommendations as Terraform configurations, enabling GitOps-driven cost management (available from Starter tier).

Weaknesses:

• Newer Platform: Launched in 2024, it’s a newer entrant compared to established players.
• Security Certifications In Progress: SOC 2 Type I certification is 85.7% complete, targeting May 2026. ISO 27001 is on the roadmap for December 2026.
• Kubernetes Cost Allocation: This specific feature is planned for Q3 2026, meaning current container-level cost allocation is not as deep as for VMs.
• Provider Depth: While multi-cloud, it currently supports 5 cloud providers, meaning hyperscaler-native tools may offer deeper single-provider integration for niche services.

# Example: List instances in a GCP project, filtering by low CPU utilization
# This helps identify candidates for rightsizing, similar to Thalaxo's worker
gcloud compute instances list \
  --project=my-gcp-project \
  --filter="status=RUNNING AND NOT labels.environment=prod" \
  --format="table(name,machineType,zone,cpuPlatform,creationTimestamp,status)" # CPU utilization needs Stackdriver metrics, not directly here

For more details on how Thalaxo integrates with your existing cloud infrastructure, visit our integrations page. You can also review our various tiers and features on our pricing page.

Comparison Table: AWS Cost Explorer vs. Datadog vs. Thalaxo

Feature	AWS Cost Explorer	Datadog (Cost Mgmt)	Thalaxo
Core Focus	AWS Cost Reporting	Observability & Performance	Multi-Cloud FinOps Automation
Multi-Cloud Support	No (AWS only)	Yes (Monitoring)	Yes (Optimization, 5 providers)
Automated Rightsizing	No	No (identifies only)	Yes (CPU & RAM thresholds)
Idle Resource Detection	No	No (identifies only)	Yes (automated worker)
Smart Scheduling	No	No	Yes (Pro tier)
Terraform Export	No	No	Yes (Starter tier)
Kubernetes Cost Allocation	No	Yes (via monitoring)	Planned Q3 2026
Security Certs	AWS Standard	SOC 2 Type II, ISO 27001	SOC 2 Type I in progress (May 2026)
Avg Waste Reduction	Manual analysis	Indirect via performance	Direct (32% avg waste detected before FinOps – FinOps Foundation 2024)

The verdict for your profile

Choosing the right tool depends on your specific operational context and scale. Generic advice is unhelpful; let’s break down real scenarios:

• If you are AWS-only and operate fewer than 50 VMs: AWS Cost Explorer is sufficient for initial cost visibility. It provides direct access to your AWS billing data, allowing you to track spend by service, account, and tags. You can manually identify high-cost resources and apply basic optimizations based on its reports. However, it requires significant manual effort for analysis and remediation.
• If you are multi-cloud or manage over 100 VMs: Native tools like AWS Cost Explorer will not scale. The specific pain point is the lack of a unified view across cloud providers and the absence of automated optimization. Manually aggregating data from multiple billing consoles, identifying cross-cloud idle resources, or implementing scheduled shutdowns across different APIs becomes an operational nightmare. A dedicated multi-cloud FinOps SaaS solution like Thalaxo becomes necessary to automate rightsizing, idle resource detection, and scheduling at scale, providing a single pane of glass for all your cloud spend.
• If observability is already covered by Datadog or another APM: Datadog provides excellent performance-cost correlation, showing you *where* over-provisioning exists relative to application metrics. However, its primary function is not cost *allocation* or *automated remediation*. The exact gap is that Datadog tells you a VM is oversized based on CPU, but doesn’t automatically recommend the exact smaller instance type or export a Terraform configuration for the change. A dedicated FinOps tool like Thalaxo complements Datadog by taking those insights and automating the next steps: rightsizing recommendations, automated scheduling for non-prod environments (67% compute savings), and idle resource termination, directly impacting your cloud bill. The overlap is minimal when you differentiate between performance monitoring and active cost optimization.

Global cloud spending is forecast to exceed $1.1 trillion in 2026 (Source: Gartner Forecast: Public Cloud Services, Worldwide, 2026). Proactive FinOps is no longer optional.

Conclusion

While native cloud tools and observability platforms offer valuable insights, a dedicated multi-cloud FinOps SaaS solution provides the automation and unified visibility required for modern cloud environments. The average waste detected before FinOps implementation is 32% (Source: FinOps Foundation State of FinOps 2024). Solutions that focus on rightsizing, idle resource detection, and smart scheduling can directly address this waste.

Explore how Thalaxo’s approach can transform your cloud cost management, moving beyond reporting to automated, impactful savings across your entire multi-cloud footprint.

Outbound link: Learn more about FinOps Foundation