DevOps teams often measure success through deployment frequency, lead time for changes, mean time to recovery (MTTR), and change failure rate (the DORA metrics). It's heavily focused on the delivery pipeline.

SRE teams introduce strict mathematical frameworks for reliability:

• SLIs (Service Level Indicators): The actual measurement (e.g., 99.9% of HTTP requests returned 200 OK in the last 5 minutes).
• SLOs (Service Level Objectives): The internal target (e.g., We want our SLI to be > 99.95% over a 30-day window).
• SLAs (Service Level Agreements): The business contract with customers if SLOs are not met (e.g., Financial penalty if uptime drops below 99.9%).

DevOps embraces failure as an opportunity to learn, promoting blameless post-mortems and faster rollbacks. However, it doesn't always put a strict mathematical limit on how much failure is "okay."

SRE uses Error Budgets. If your SLO is 99.9% uptime, your error budget is 0.1% downtime (about 43 minutes a month). If the error budget is depleted, the team halts new feature releases and focuses entirely on reliability until the budget recovers. It balances feature velocity with system stability objectively.

DevOps focuses heavily on CI/CD—automating testing, builds, and deployments to get code from a developer's machine to production seamlessly.

SRE focuses on eliminating Toil. Toil is repetitive, manual, tactical work that scales linearly with service growth (like manually provisioning databases or acknowledging repetitive alerts). SREs aim to spend at least 50% of their time writing code to automate these operations away, treating infrastructure problems as software engineering problems.