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Documentation Index

Fetch the complete documentation index at: https://docs.pragmatiks.io/llms.txt

Use this file to discover all available pages before exploring further.

Pragmatiks isn’t trying to replace everything. Different tools solve different problems. Here’s where we fit.

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vs Infrastructure as Code

Infrastructure as Code tools excel at provisioning. You declare what you want, they create it. But what happens after?

Terraform / Pulumi

  • Provisions infrastructure
  • Changes require manual cascade
  • Dependencies resolved at build-time
  • Best for static infrastructure

Pragmatiks

  • Provisions and maintains
  • Changes propagate automatically
  • Dependencies are runtime reactive
  • Best for dynamic data infrastructure
The key difference: IaC provisions once. When your warehouse changes, you manually update dependent projects and run terraform apply again. Pragmatiks maintains continuously—changes propagate automatically through your dependency graph.

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When to Use Pragmatiks

Multiple Tools

Your stack includes BigQuery, dbt, vector databases, and more. You need them to work together.

Dynamic Dependencies

When your warehouse changes, your transformations should adapt. When your source evolves, your pipelines should respond.

Product Focus

You’d rather ship features than chase cascading configuration changes.

No Platform Team

You need platform-team capabilities without hiring a platform team.

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When Pragmatiks Might Not Be Right

If you’re just using BigQuery, use BigQuery directly. Pragmatiks adds value when you’re coordinating multiple systems.
If nothing changes after initial setup, Terraform works fine. Pragmatiks shines when infrastructure is dynamic.
If you’ve built internal tooling that handles dependency propagation, you might not need to adopt something new.

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Summary

Pragmatiks solves maintenance

It’s not a replacement for Terraform, Airflow, or Snowflake—it’s what ties them together and keeps them in sync as your infrastructure evolves.