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Introduction

Managing costs can be challenging, especially when dealing with multiple services and providers. DoiT DataHub is designed to support the ingestion of third-party cost, usage, and metric-based data, giving you a comprehensive view of your cloud expenditures and facilitating effective financial decision-making.

Benefits​

DoiT DataHub integrates cost and usage data from various sources into a single, unified cost control and attribution platform, bringing benefits in the following aspects.

  • Cost Unit Economics: Organizations need to understand costs at a granular level to calculate unit economics. DataHub makes it easy to define and track unit metrics that tie spending to business value, such as cost per transaction, cost per customer, or cost per gigabyte of data stored, helping quantify the value derived from your investments.

  • Unified cost structure and enhanced visibility: DataHub helps you integrate cost, usage, and metric data from various third-party sources, including software-as-a-service (SaaS) services, operational costs like rent and employee expenses, and business metrics. Data are aggregated into a single pane of glass for a unified financial view, allowing you to gain deeper insights into your spending patterns, identify cost-saving opportunities and make informed decisions across services.

Key features​

  • Flexible data ingestion via API and direct upload: Seamlessly import external cost data through APIs or direct CSV uploads, enabling automated data synchronization and reducing manual effort.

  • Customizable cost attribution: Break down costs by teams, projects, environments, or other units that make sense for your organization.

Use cases​

  • Granular cost analysis: By connecting service providers' native cost management tools to DataHub through the DataHub API, you can easily import cost and usage data into the DoiT platform. This enables comprehensive cost tracking, reporting, and detailed cost analysis at a granular level.

  • Streamlining data ingestion from external providers: Managing cost data from external providers often requires complex workflows involving scripts, cloud storage, and pivot reports in spreadsheets. DataHub allows you to upload external cost data directly, eliminating manual workflows.

  • Consolidating 3rd-party cloud costs: DataHub provides a centralized platform for importing cost and usage data from various providers. It allows you to analyze all costs in one place, reducing complexity and creating a unified view of costs across providers.

  • Tracking consumption for teams from external sources: Teams often consume resources from external services, making it challenging to monitor and allocate costs accurately. DataHub allows tracking and aggregating external cost data in association with team consumption. This approach simplifies cost attribution and helps organizations monitor team-specific resource usage effectively.

  • Unit economics analysis: Pulling data from multiple sources for reporting on unit economics can be cumbersome. DataHub allows you to set up API connections to import data into the DoiT platform programmatically. From there, you can build custom reports to analyze unit economics by associating costs with specific metrics and get actionable insights for product optimization. Examples of unit metrics include:

    • Cost per unit: customer, run, token, session, ride, or other SKUs unique to the business.
    • Cost per cost center: feature, product, team, environment, or other entities.

  • Budgeting and forecasting across sources: Budgeting across services and external data sources often involves manual workflows and siloed reporting tools. DataHub provides the flexibility to aggregate cost and usage data from various sources, enabling unified reports and accurate forecasting. It reduces operational overhead and improves financial planning efficiency.

While DataHub is a versatile feature, it might not be the best choice for the following scenarios:

  • Real-time data processing: DataHub is primarily designed for cost and usage data aggregation and analysis. It's not suitable if you require real-time data processing with minimal latency.

  • High-frequency data ingestion: DataHub is not optimized for high throughput such as IoT applications with thousands of data points per second or other applications that require high-frequency data ingestion.

  • Logging and high frequency metrics: If your use case involves advanced logging data and high-frequency metrics in their raw forms, DataHub may not be the best fit due to high dimensionality/cardinality.

See also​

  • Request product training: While DataHub is a powerful tool, accurately attributing costs and understanding the implications of data usage often require familiarity with the DoiT console. Building a deeper understanding of the DoiT platform can help overcome these challenges and unlock its full potential.
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