Single Pane of Glass View of Engineering Infrastructure and Services Costs

In the cloud-native era, engineering teams rely on a vast array of platform services beyond just cloud infrastructure. From monitoring stacks like Datadog to data platforms like Snowflake, container services like EKS/AKS to serverless compute like AWS Fargate— the modern tech stack continues expanding.

This gives rise to a new discipline — Total Platform Cost Management (TPCM). TPCM is the holistic practice of managing and optimizing costs across an organization's entire cloud technology footprint, spanning infrastructure, platform services, data pipelines, and more. As IT spend intensifies, TPCM has become critical for maximizing engineering efficiency while aligning investments with business goals. However, achieving comprehensive visibility and governance over total platform costs at scale is an immense challenge. The self-service, dynamic nature of the cloud coupled with a multitude of services makes costs difficult to centrally monitor, forecast and optimize. Traditional IT finance practices are siloed and ill-equipped for TPCM.

Yotascale’s AI-powered solution transcends these hurdles by unifying technical and financial intelligence through machine learning models into a single pane of glass view of your engineering costs. Now you can seamlessly allocate costs from across any provider to present a unified view for the business.

Single Pane of Glass Total Cost Visibility

At its core, Yotascale integrates seamlessly across multi-cloud environments and services to construct a centralized cost structure capturing:
• Cloud infrastructure (VMs, storage, networking)
• Kubernetes container workloads
• Data/analytics platforms (Snowflake, Databricks, etc.)
• Monitoring/observability (Datadog, Dynatrace, etc.)
• Serverless, CI/CD, streaming and more

Machine learning categorizes and reports on cloud costs with granular tagging by business unit, product, team and more. Yotascale Lens allows customizing this centralized cost data into tailored views for stakeholders like engineering teams, FinOps and finance.

This single pane of glass finally provides the transparency organizations need to understand their total cloud investment. But Yotascale goes beyond just passive reporting.

Intelligent Cost Monitoring & Forecasting

Yotascale’s AI/ML models continuously monitor cost anomalies, trends and predicted overages in real-time based on usage patterns and scaling activities. This enables proactive detection of potential cost risks before they escalate.

Using advanced forecasting techniques, Yotascale predicts future costs and consumption based on growth projections, making it simple to plan cloud capacity needs. The Yota Copilot assists with this by providing:

• Tailored reserved instance and pricing model recommendations to optimize commitments
• Automated threshold alerts for forecasted budget overages by product, team or service
• Insights on underutilized resources and scaling inefficiencies to cut waste

This economics-driven cost intelligence enables engineering teams to continually maximize their cloud efficiency at scale.

Governance Through AI-Powered FinOps

At its core, Yotascale integrates seamlessly across multi-cloud environments and services to construct a centralized cost structure capturing:

• A unified system of record for all cloud costs
• Accurate showback models for cost allocation
• AI-driven forecasts and optimization recommendations
• Live reporting via the Yota Copilot for all managers

This collaborative framework ensures engineering consumes cloud resources responsibly while empowering finance to plan capex and map technology ROI to company goals. FinOps processes driven by AI-enabled cost transparency eliminate surprises and accelerate initiatives.

As the cloud's complexity grows, Yotascale’s Total Platform Cost Management solution equips companies to efficiently engineer at scale while governing tech investments for business success. By fusing advanced AI with FinOps disciplines, Yotascale lights the path for TPCM’s future.