Terraform

Definition and overview of Statsig's Terraform integration

Statsig's Terraform Provider enables you to configure feature gates and experiments directly within your infrastructure-as-code workflow. By defining these resources in Terraform, you can manage them alongside your application and infrastructure, ensuring consistency and reproducibility.

The provider synchronizes with Statsig via the Console API, allowing you to create, update, and delete gates and experiments programmatically. This integration simplifies the management of feature flags and experimentation, making it easier to align your development and operations teams.

You can find the Statsig Terraform Provider on the official Terraform registry at statsig-io/statsig. To start using the provider, simply add the required configuration to your Terraform files and run terraform init to download and install the provider.

With Statsig's Terraform integration, you can:

  • Manage feature gates: Create, update, and delete feature gates directly from your Terraform configuration files.

  • Configure experiments: Define experiment groups, allocations, and parameter values using Terraform resources.

  • Streamline your workflow: Integrate feature flag and experiment management into your existing infrastructure-as-code pipeline.

By leveraging Terraform's declarative language and state management capabilities, you can ensure that your feature gates and experiments are consistently applied across your environments, reducing the risk of misconfigurations and improving overall reliability.

Supported features and capabilities

The Statsig Terraform Provider currently supports configuring gates and experiments. This allows you to manage these Statsig resources directly through your Terraform configuration files.

Upcoming features include support for dynamic configs and segments. These additional resource types will enable even more powerful management of your Statsig configuration via Terraform.

By leveraging the Statsig Terraform Provider, you gain the ability to manage your Statsig resources through infrastructure-as-code. This approach brings benefits such as version control, reproducibility, and automation to your experimentation setup.

Best practices and implementation guide

When deploying Statsig to production, setting up Single Sign-On (SSO) is a key step. SSO allows for seamless user management, automatically creating Statsig accounts for verified employees and simplifying onboarding.

To ensure efficient creation of metrics and assignment sources, follow Statsig's data best practices. This includes configuring custom metrics, user dimensions, metric dimensions, and funnels. Tag metrics for easy inclusion in experiments.

When designing experiments using Terraform, consider your hypothesis and select the appropriate assignment source. The Statsig Terraform Provider supports configuring gates and experiments, syncing with Statsig via the Console API. Advanced settings offer flexibility in experiment configuration.

As you roll out experiments, monitor their status with health checks and live exposure readouts. Statsig's Terraform integration enables you to manage feature flags and experiments as code, ensuring consistency and reproducibility.

To optimize your Statsig implementation, establish best practice repositories and forums for experimenters to share knowledge and seek guidance. Provide clear guidelines on when to use feature gates versus experiments. Consider leveraging Statsig's Enterprise Support for tailored training sessions.

When migrating to Statsig, set a cutoff date for creating new experiments and gates on the platform. Allow existing experiments and ephemeral gates on legacy systems to conclude before moving persistent gates to Statsig.

By following these best practices and leveraging Statsig's Terraform Provider, you can streamline your experimentation workflow and effectively manage feature rollouts in production environments.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
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Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Ancestry
Partha Sarathi
Director of Engineering
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