--- title: "DevOps and SRE" description: "CI/CD pipelines, infrastructure as code, observability, service level objectives, incident practice and DevSecOps, so speed and reliability stop competing." url: https://www.expandware.com/services/engineering/devops-and-sre/ section: "Services" topics: ["Engineering", "GitHub Actions", "GitLab CI", "Argo CD", "Terraform"] publisher: "Expandware Private Limited" --- # DevOps and SRE CI/CD pipelines, infrastructure as code, observability, service level objectives, incident practice and DevSecOps, so speed and reliability stop competing. Release often, sleep well. Teams slow down when releases are risky, and releases stay risky when they are rare. DevOps and site reliability engineering break that loop: automated pipelines that test and deploy every change the same way, infrastructure as code, observability that answers questions during an incident, service level objectives that decide when to ship and when to fix, and blameless reviews that turn every incident into an improvement. Where it usually starts: - Releases happen monthly, because each one needs a weekend. - Incidents are investigated by searching logs on individual servers. - There is no agreed definition of reliable enough. - Security scanning happens just before release, when fixes are most expensive. What is included: - CI/CD pipelines: Build, test and deploy automated for every service. - Infrastructure as code: Environments defined, reviewed and reproduced from code. - Observability: Metrics, logs and traces, with dashboards built around real questions. - Service level objectives: Reliability targets agreed with the business, with error budgets. - Incident practice: On-call rotations, runbooks and blameless post-incident reviews. - DevSecOps: Security scanning and policy checks in the pipeline. Where it sits: DevOps and SRE run most of the engineering architecture: the repository and pipeline, artifact registry, staging and feature flags on the way to production, and the observability and on-call that keep it running. Decisions to settle early: - Where to start. The service with the most painful releases or the most incidents usually shows the biggest gain. We start there. - Build or buy the tooling. Hosted CI and observability services are quick; self-hosted tools give control. Cost at your scale decides. - Who is on call. Teams that build a service can run it, with support. On-call is designed so nobody carries it alone. How the work runs: Measure, Lead time, failure rate and recovery. Automate, Pipelines and infrastructure as code. Observe, Telemetry and service level objectives. Improve, Reviews that change the system. How it is bought: - A fixed-fee assessment: We measure lead time, deployment frequency, change failure rate and recovery time, and review pipelines and incidents. You receive the baseline, the findings and a fixed price for the improvement program. - A fixed-price build: Pipelines, infrastructure as code, observability, service level objectives and incident practice for the services in scope, at a fixed price. - Running it afterwards: Your team runs the result with templates and runbooks, or we provide on-call and platform support on a retainer. Worked example, releases that needed a weekend. Say a booking platform releases once a month over a weekend, each incident means logging in to servers one by one to read their logs, and every release brings a run of fixes in the week after. - Measure: We record lead time, deployment frequency, change failure rate and recovery time, so progress can be shown rather than claimed. - Automate: Each service gets a pipeline that builds, tests, scans and deploys it the same way, with environments defined as code. - Observe: Metrics, logs and traces are collected centrally, and service level objectives agreed with the business decide what is alerted. - Improve: Incidents are followed by blameless reviews, and their actions are prioritized alongside feature work. At the end, releases are small and routine, an incident is investigated from one place, and the error budget tells the team whether features or reliability come first. (An illustration, not a client account.) What you have at the end: - Delivery assessment with baseline metrics: Lead time, deployment frequency, change failure rate and recovery time today, so improvement can be measured. - Pipeline templates and infrastructure code: Reusable pipelines and environment definitions that every service can adopt. - Observability dashboards and alerts: Metrics, logs and traces organized around the questions asked during an incident. - Service level objectives and an error budget policy: Reliability targets agreed with the business, and what happens when a service runs out of budget. - Incident runbooks and review templates: How incidents are run, and a blameless review format that turns each one into improvements. Standards and platforms worked with: GitHub Actions, GitLab CI, Argo CD, Terraform, OpenTelemetry, Prometheus, Grafana, DORA delivery metrics, SLSA. Where it goes wrong, and what we design out: - Rare, large releases. Infrequent releases are risky releases. Small changes ship often through the same pipeline. - Logs on individual servers. Searching machine by machine slows every incident. Telemetry is collected in one place. - Reliability as an argument. Without objectives, speed and stability are debated. Error budgets turn the debate into a rule. What it changes: - Faster, safer releases. Small, automated releases make each change low risk and easy to reverse. - Faster recovery. Observability and runbooks shorten the time from an alert to a fix. - Reliability by agreement. Service level objectives replace arguments about whether to ship. How you will know it is working: - Releases are small and routine. - Incidents are investigated from one place. - Error budgets settle whether features or fixes come first. - Recovery after a failed change is quick and practised. Who you work with: - Reliability lead. Owns the baseline, the objectives and the improvement program. - DevOps engineers. Build pipelines, infrastructure as code and observability. - SRE. Defines service level objectives, runs incident practice and coaches teams. What we need from you: - Access to repositories, pipelines and environments. - Incident history and current on-call arrangements. - A team willing to adopt the new pipeline first. - Business owners to agree reliability targets. Common questions: Q: Where should we start with DevOps? A: By measuring how changes reach production today: lead time, deployment frequency, change failure rate and recovery time. The biggest delay in that path is usually the first thing worth automating. Q: What is the difference between DevOps and SRE? A: DevOps is a set of practices for delivering software quickly and safely; site reliability engineering applies engineering to operations, with service level objectives and error budgets deciding the balance between change and stability. Most teams benefit from both. Q: Do you set up on-call for our team? A: Yes: rotations, escalation, alert tuning so on-call is sustainable, and runbooks for every alert that pages a person. Or we take on-call ourselves under a managed service. Q: How do you add security without slowing delivery? A: By scanning early and automatically, dependencies, secrets, code and images, with thresholds tuned so the pipeline fails on what matters and not on everything. Q: What are the DORA delivery metrics? A: Four measures from the DevOps Research and Assessment program: lead time for changes, deployment frequency, change failure rate and time to restore service. Together they show both speed and stability, and we use them as the baseline and as the measure of progress. Q: What is an error budget? A: The amount of unreliability a service may have while still meeting its service level objective. While budget remains, the team ships; when it is spent, reliability work takes priority. It turns an argument about speed and stability into a rule agreed in advance. Q: What is SLSA? A: Supply-chain Levels for Software Artifacts, a framework for protecting the build pipeline so an artifact can be traced to its source and shown not to have been tampered with. We apply its practices, such as provenance and hardened builds, in proportion to the risk. Q: Can these practices work with legacy systems? A: Most of them, yes. Even systems that cannot be deployed often can be built in a pipeline, monitored centrally and covered by service level objectives and incident reviews. Release pace changes most for systems designed for it, but reliability practice improves everything. Often paired with: Kubernetes and platform engineering, Software and product engineering, Monitoring and network operations. --- Canonical page: https://www.expandware.com/services/engineering/devops-and-sre/ Site index for machines: https://www.expandware.com/llms.txt Full site text: https://www.expandware.com/llms-full.txt Expandware Private Limited. Inquiries: solutions@expandware.com, +92 (333) 32 11011.