Infrastructure SRE Team Model: When It Works Best

In a previous article on SRE models, we explored the Kitchen Sink SRE model —a common starting point for organisations launching their first reliability engineering efforts. Today, we're diving into another classic structure: the Infrastructure SRE team model. Unlike the all-encompassing Kitchen Sink approach, this model focuses exclusively on foundational systems that support all development teams, offering a scalable path for maturing engineering organizations.

Infrastructure SREs focus on enabling developer productivity and platform reliability at scale. Their responsibilities include:

• Managing shared Kubernetes clusters

• Maintaining cloud infrastructure-as-code

• Operating and evolving CI/CD pipelines

• Defining and enforcing production standards

• Simplifying infrastructure use for developers

This is a platform-first model. Instead of owning individual customer-facing services, infrastructure SREs focus on ensuring those who do have a solid, reliable base to build on.

This structure is commonly used at scale — Google outlines these models in their SRE team organization guide — where the division of labor between platform teams and product SREs becomes essential.

Rather than owning end-user-facing services, these teams support the ecosystem that enables development teams to build and ship software more reliably.

Organizations with multiple teams building on a common infrastructure get the most out of this model. It supports consistency, security, and standardization across platforms. For growing companies, it’s often a natural evolution from the Kitchen Sink setup.

In fact, Google recommends clearly defining responsibilities between infrastructure SREs and product teams —as detailed in their Engagement Models guide—to avoid overlaps and ensure reliability issues are correctly routed.

Many enterprises use a hybrid setup, combining infrastructure SREs with product SREs. The infrastructure team sets the foundation, while product SREs handle service-specific reliability for customer-facing systems.

Organizations benefit from clearer separation of concerns and improved developer experience:

• Consistency across teams: Developers don’t have to re-learn infrastructure practices per project.

• Focus on features: Developers can spend more time building product features and less on troubleshooting infrastructure issues.

• Reliability as code: Standards like deployment checks, monitoring templates, and access patterns are encoded once and reused.

At Agile Analytics, we’ve seen infrastructure SRE models reduce the time developers spend on infra-related issues by up to 47%.

Of course, this model isn’t without its limitations:

• Single point of failure: Since the entire org depends on the same infra team, any bottlenecks or issues there affect everyone.

• Less customer context: Infra-focused teams may prioritize technical improvements that don't directly enhance user experience.

• Coordination overhead: As infrastructure teams scale, they often split into subgroups (e.g., networking, CI/CD), which introduces its own complexity.

Platforms like Agile Analytics help surface these growing pains by connecting infrastructure metrics (e.g. response time, downtime) with developer satisfaction scores—helping leaders identify coordination gaps.

To implement the infrastructure SRE model successfully:

• Define clear responsibilities: Prevent confusion between infra and product SREs.

• Foster collaboration: Include infra SREs early in architectural planning.

• Link reliability to outcomes: Use developer feedback and analytics to guide investments.

By aligning infrastructure efforts with developer needs, organizations can unlock higher productivity and build more resilient systems. The key is measurement: connect infra performance with what matters to the people building and running the software.