Deployment Model

Deployment model definition

A deployment model defines the structure and management of cloud services. It describes how resources are deployed, who manages them, and the relationship between users and the infrastructure.

Key elements

  • Resource Deployment: This explains where and how your cloud resources are located. Are they on-premises, in a public cloud, or a mix?

  • Management Responsibility: Defines whether your team or a third-party manages the infrastructure. This can affect your control and flexibility.

  • User Interaction: Outlines how users will interact with the infrastructure, including access levels and permissions.

Examples of deployment models in use

  • Public Cloud: A startup leverages AWS for its scalability and cost-efficiency. It handles variable traffic loads seamlessly. AWS offers flexibility without significant upfront investment.

  • Private Cloud: A financial institution ensures data security through a private cloud. This model meets regulatory standards effectively. It provides greater control over sensitive information. Learn more about connecting Statsig to your existing tools

  • Hybrid Cloud: A healthcare provider uses a hybrid cloud for patient data. Sensitive data stays on-premises, while less sensitive workloads utilize public cloud resources. This setup balances security and scalability. Understand the different Statsig products

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!

What builders love about 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