Key Differences #

1. Resource Types #

• Google Cloud Asset Inventory (GCAI): GCAI supports around 140 resource types across various Google Cloud services, making it comprehensive within the GCP ecosystem. However, it is limited to GCP assets, and any changes or additions to supported resources are fully managed by Google.
• CloudQuery: CloudQuery supports over 300 GCP resource types. Although this is fewer than GCAI, CloudQuery’s open-source nature allows users to easily contribute new resource types or extend existing ones. This flexibility ensures that users aren't blocked by vendor roadmaps and can quickly adapt to their own specific needs.

2. Database Agnostic and Raw Access to Data #

• Google Cloud Asset Inventory: GCAI stores asset data in proprietary formats with limited support for exporting or querying raw data outside the platform. While GCAI integrates with Google BigQuery and supports Google’s stack, accessing raw data outside of this ecosystem can be cumbersome.
• CloudQuery: CloudQuery is database-agnostic, allowing users to store cloud asset data in various databases such as PostgreSQL, BigQuery, Snowflake, and others. This means users have full access to raw, normalized data, and can leverage a wider SQL ecosystem. Additionally, CloudQuery’s raw data storage makes it easier to integrate with third-party tools like Grafana, BI platforms, and custom analytics workflows.

3. Search and Querying #

• Google Cloud Asset Inventory: GCAI offers predefined query capabilities, allowing users to search and filter assets based on a limited set of predefined attributes. While useful for basic querying, this structure limits advanced filtering and data manipulation possibilities, as users are constrained to the built-in attributes provided by GCAI.
• CloudQuery: CloudQuery offers unrestricted search and querying using standard SQL, enabling users to search, filter, and query across all available data without limitations. This makes it possible to perform complex queries, custom filtering, and advanced analytics using familiar SQL syntax.

4. Cloud Agnostic and Multi-Cloud Support #

• Google Cloud Asset Inventory: GCAI is specific to Google Cloud and only tracks assets within the GCP ecosystem. For organizations using multi-cloud environments or SaaS services, this poses a limitation since GCAI cannot track assets across AWS, Azure, or other cloud providers.
• CloudQuery: CloudQuery is cloud-agnostic and provides support for tracking, auditing, and monitoring cloud resources across multiple providers, including AWS, Azure, GCP, Oracle Cloud, and many popular SaaS platforms. This makes it a superior choice for organizations with multi-cloud strategies or those needing a unified view of all cloud and SaaS infrastructure.

5. Policy Language #

• Google Cloud Asset Inventory: GCAI provides basic auditing and security checks based on Google’s predefined security and compliance tools. However, it does not provide a structured policy language that can be customized and versioned for advanced rules and queries.
• CloudQuery: CloudQuery supports HCL (HashiCorp Configuration Language) for codifying and version-controlling security, compliance, and audit policies, with SQL as the query engine. This enables users to create custom, complex policies, aggregate multiple queries, and version-control their rules for better governance and compliance tracking. CloudQuery’s use of SQL makes it easy to write flexible policies that can be reused across different cloud environments.

6. Pricing #

• Google Cloud Asset Inventory: GCAI is included as part of Google Cloud services and charges based on usage and API calls. For larger organizations or those managing significant cloud assets, costs can scale with usage, particularly when integrating with services like BigQuery.
• CloudQuery: CloudQuery has a number of premium plugins are available which charge based on the number of rows synced. You can learn more about pricing on the CloudQuery pricing page.

7. Extensibility and Customization #

• Google Cloud Asset Inventory: GCAI is limited by Google’s implementation and customization options. Users are largely restricted to what is offered within the platform, with limited options for extending functionality or customizing it for niche use cases.
• CloudQuery: CloudQuery’s open-source, plugin-based architecture makes it highly flexible. Users can build custom plugins, extend existing ones, and integrate with proprietary APIs or services. This level of flexibility allows organizations to tailor CloudQuery to their specific needs, enabling use cases that go beyond those supported by GCAI.

Summary #