# slo

Published articles for slo.

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## Service Levels: SLI, SLO, SLA

DevFeed: [Service Levels: SLI, SLO, SLA](<https://devfeed.tech/articles/service-levels-sli-slo-sla-34022.md>)

Original publisher: [Read original article](<https://sridharrajarao.com/blog/service-levels/>)

Author: Sridhar Rajarao

Published: 2026-05-24T00:00:00Z

Content type: article

Language: en

Sources: [Sridhar Rajarao](<https://devfeed.tech/sources/sridhar-rajarao.md>)

Topics: [Availability](<https://devfeed.tech/topics/availability.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [availability](<https://devfeed.tech/tags/availability.md>), [rca](<https://devfeed.tech/tags/rca.md>), [reliability](<https://devfeed.tech/tags/reliability.md>), [service](<https://devfeed.tech/tags/service.md>), [slo](<https://devfeed.tech/tags/slo.md>), [sre](<https://devfeed.tech/tags/sre.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>)

### AI overview

The article distinguishes SLI, SLO, and SLA as a hierarchy: measured reality, an internal target, and a customer contract. It explains how the gap between SLO and SLA creates an error budget and describes AI uses such as detecting SLO erosion, correlating telemetry, and drafting root-cause analyses while leaving operational judgment to engineers.

### Source excerpt

What SLI, SLO, and SLA actually mean, why the order matters, and where AI helps.

## Performance engineering as a habit, not a project

DevFeed: [Performance engineering as a habit, not a project](<https://devfeed.tech/articles/performance-engineering-as-a-habit-not-a-project-38796.md>)

Original publisher: [Read original article](<https://fidelitas.io/2026/05/performance-engineering-as-a-habit/>)

Author: fidelitas.io

Published: 2026-05-16T16:00:00Z

Content type: tutorial

Language: en

Sources: [fidelitas.io](<https://devfeed.tech/sources/fidelitas-io.md>)

Topics: [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [ci](<https://devfeed.tech/topics/ci.md>), [Code](<https://devfeed.tech/topics/code.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [build](<https://devfeed.tech/tags/build.md>), [ci](<https://devfeed.tech/tags/ci.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [code-review](<https://devfeed.tech/tags/code-review.md>), [engineering-practice](<https://devfeed.tech/tags/engineering-practice.md>), [git](<https://devfeed.tech/tags/git.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [latency](<https://devfeed.tech/tags/latency.md>), [observability](<https://devfeed.tech/tags/observability.md>), [performance](<https://devfeed.tech/tags/performance.md>), [performance-engineering](<https://devfeed.tech/tags/performance-engineering.md>), [regression](<https://devfeed.tech/tags/regression.md>), [runner](<https://devfeed.tech/tags/runner.md>), [slo](<https://devfeed.tech/tags/slo.md>)

### AI overview

The article argues that teams should treat performance engineering as a recurring engineering habit rather than a temporary project. It recommends measuring every pull request, comparing results with the previous day, and making performance regressions fail the build as part of CI.

### Source excerpt

Most performance work is reactive: the bill goes up, the dashboard turns red, somebody opens an investigation ticket. Here's how to make it boring instead.