Production Based Environment

Understanding production based environment

What is a production environment?

A production environment is the real-time setting where your software is live. End users interact with the latest updates and features here. It's where your code meets the world, operating under real-world conditions and handling actual user data. This is the environment where your software must perform reliably and efficiently, as any issues will directly impact users.

How does it differ from a test environment?

A test environment serves a different purpose. It’s used for running complex tests before your code goes live. This is where you catch and fix bugs, ensuring that new functionalities work as intended without affecting users.

In contrast, a staging environment mimics production for final checks before release. It provides a sandbox that closely replicates the production environment, allowing you to conduct thorough testing under near-real conditions. Here, you can validate the final build and ensure that it integrates well with existing systems.

  • Test Environment: For complex tests and bug fixes.

  • Staging Environment: Mimics production for final pre-release checks.

Understanding these differences helps you manage deployments more effectively, ensuring that your software performs well in production while minimizing risks.

Key characteristics of production based environment

Real-time operations: Production environments handle operations as they happen. This ensures immediate processing and response. Users experience the software live. For more details on ensuring uptime, check out Paranoid about uptime? 9 things to do!, Moving from POC to Production, and Continuous Delivery.

Accessibility to end users: End users interact directly with the latest features. This environment is where your software is most visible. It must be user-ready. Learn more about this through Client-Side Testing, Server-Side Testing, and Continuous Delivery.

Stability and reliability: These environments prioritize stability. Any issues here affect real users. Ensuring reliability is crucial. For best practices, refer to Paranoid about uptime? 9 things to do!, Moving from POC to Production, and System Status.

Continuous monitoring and performance testing: Ongoing monitoring checks for issues. Performance testing ensures the software runs smoothly. This helps maintain high standards. Explore more on this topic in Paranoid about uptime? 9 things to do!, Moving from POC to Production, and Client-Side Testing.

Examples of production based environment

Example 1: e-commerce website

Live updates and changes are tested directly on the site. This ensures real-time feedback and immediate data collection. It helps in quickly identifying and fixing issues. Learn more about Client-Side Testing, Server-Side Testing, and Continuous Delivery.

Example 2: mobile app launch

New versions roll out to users with feature flags. This allows monitoring of user interactions and performance instantly. Adjustments can be made quickly based on user feedback. Check out more on Split Testing, Lean Hypothesis Testing, and Beta Testing.

Example 3: financial services platform

This involves real-time transactions and data processing. Immediate detection and rollback of issues using feature toggles ensures stability. This keeps the service reliable and secure for users. Learn about Canary Testing, Bucket Testing, and Multivariate Testing.

Join the #1 experimentation community

Connect with like-minded product leaders, data scientists, and engineers to share the latest in product experimentation.

Try Statsig Today

Get started for free. Add your whole team!

Why the best build with us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
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
We use cookies to ensure you get the best experience on our website.
Privacy Policy