How CloudQuery Compares to DIY Scripts and Per-Provider Exporters

Updated

CloudQuery is a better fit than DIY scripts and per-provider exporters when you need to sync many cloud and SaaS sources into one database you already run, query everything in standard SQL, and keep credentials inside your own network. The DIY approach still makes sense for a single source, a one-off export, or a team that wants zero new tooling. The comparison below uses the same dimensions for both so you can decide based on your source count, query needs, and how much control you want over credentials and version history.

The two approaches side by side

Dimension DIY scripts & exporters CloudQuery
Source coverage A separate script or exporter for every cloud and SaaS source One tool syncs 80+ sources into the database you already run
Querying Every tool has its own query language or dashboard filters Query everything in standard SQL, no proprietary language to learn
Data control Credentials and data spread across tools you don't fully control Self-hosted, so data never leaves your network unless you choose
Versioning & review No version history or single source of truth Every sync defined in version-controlled YAML, reviewed in pull requests
New sources Need a new source? Start from scratch Missing a source? Build it with the SDK
Speed at scale Scans slow down, or time out, as the account count grows Incremental sync keeps scans across hundreds of accounts fast

Where the DIY approach breaks down

The core problem is sprawl. Most teams end up gluing together a different exporter for every provider, which means:

  • Each new source adds another script to write, run, and maintain.
  • Each tool brings its own query language or dashboard filters, so there is no single place to ask questions across providers.
  • Credentials and data end up spread across tools you don't fully control.
  • There is no version history and no single source of truth, so changes are hard to review or roll back.
  • Scans slow down or time out as the account count grows.

Where CloudQuery changes the model

CloudQuery treats syncs as config-as-code. You define sources in YAML, point at the cloud providers and SaaS apps you want plus the destination database you already operate, and run the sync once or on a schedule in your pipeline. Data lands in Postgres, BigQuery, Snowflake, or S3.

Two properties matter most for the comparison:

  • Self-hosted. Data never leaves your network unless you choose otherwise, so credentials stay under your control.
  • One normalized schema. AWS, GCP, Azure, GitHub, Okta, and 80+ other sources land in the same relational shape, so you query them together in standard SQL instead of learning a per-tool language.

Getting to a first sync

  1. Install CloudQuery. A single binary on Linux, macOS, or Windows. macOS via Homebrew: brew install cloudquery/tap/cloudquery. Linux, CI, or agents: curl -fsSL https://cloudquery.io/install.sh | sh.
  2. Define sources in YAML. Point at the providers and SaaS apps you want, and the destination database you already operate.
  3. Run the sync. Once, or on a schedule in your pipeline. Data lands in Postgres, BigQuery, Snowflake, or S3.

When to choose which

Choose CloudQuery if you sync more than a couple of sources, want one SQL surface across all of them, need credentials to stay inside your network, or want sync definitions reviewed in pull requests like any other code.

Choose DIY scripts and exporters if you have a single source, a one-time export, or a constraint that rules out adding a new self-hosted tool.

The decision usually turns on source count and query needs: the more sources you add, the more the per-provider exporter model costs you in maintenance, and the more a single normalized schema in your own database pays off.