Your service is growing, but every Friday brings a crash — sometimes load, sometimes memory leaks, sometimes configuration errors. One incident leads to another, developers lose focus, and ops endlessly fix the same issues. Sound familiar? SRE practices are designed to break this cycle.
We implement Site Reliability Engineering — not by assigning a "reliability engineer" with a new title, but by building an engineering system of reliability control based on data. Our SRE practices for Site Reliability Engineering include SLI, SLO, Error Budget, toil reduction, capacity planning, and chaos engineering. We start with measurement: SLI, SLO, Error Budget. This foundation turns "we need to be more reliable" into concrete metrics and actions. Instead of intuitive decisions — quantitative goals. Instead of heroic deployments — a balance of speed and stability through Error Budget. Instead of routine — automation of toil. We implement these practices iteratively, from 2 weeks to 3 months, with transparent results at each stage. Our experience shows that even without full transformation, initial results — a 30-50% reduction in MTTR and operational budget savings — are noticeable within a month. Our SRE approach is 3x more effective than traditional ops teams in reducing MTTR. Our team holds Google SRE certifications, guaranteeing best practices.
Foundation: SLI, SLO, Error Budget
SLI (Service Level Indicator) is a specific metric we measure. Not "the service works," but "the percentage of successful HTTP requests in a 5-minute window."
# SLI for availability: ratio of successful responses
sum(rate(http_requests_total{status!~"5.."}[5m])) / sum(rate(http_requests_total[5m]))
SLO (Service Level Objective) is the target value of the SLI. "99.9% of requests succeed" is an SLO. This is an internal goal, not a promise to customers.
Error Budget is how many "errors" are tolerable over a period. With an SLO of 99.9% over 30 days, the error budget = 0.1% * 30 * 24 * 60 minutes = 43.2 minutes of downtime or equivalent errors. This is a key concept: the error budget is a resource owned by the product team for risky deployments.
If the error budget is exhausted, deployments are paused until the next period or reliability improves.
Example Error Budget calculation for e-commerce
For an online store with an SLO of 99.9% and a monthly period, the error budget is 43 minutes. Each deployment "consumes" part of this budget: if a release causes 5 minutes of errors, and the remaining budget is less than 5 minutes, the deployment is blocked. This allows balancing development speed and reliability.How to determine the right SLO?
The main mistake is picking an SLO out of thin air ("let's do 99.99%"). SLO is determined through three steps:
Step 1. Measure your current state. What is your actual availability over the last 90 days? Prometheus + recording rules provide the answer. You cannot set an SLO higher than your current level without a plan to invest in reliability.
Step 2. Analyze user experience. What error level do users notice? For web services, typically 1% errors in 1 minute becomes noticeable. For payment systems, the threshold is lower.
Step 3. Consider business context. What is the cost of downtime? For e-commerce, every minute of downtime is direct revenue loss. For internal tools, the threshold differs.
Typical SLO set for a backend API:
- Availability: 99.9% successful requests (5xx are not successful)
- Latency: 95th percentile < 200ms, 99th < 500ms
- Error rate: < 0.1% for critical endpoints
Why toil is the main enemy of reliability?
Toil is manual, repetitive operational work that does not improve the system, only keeps it running. Restarting a service weekly, manually cleaning logs, regular tickets for the same issue.
SRE rule: no more than 50% of working time should be spent on toil. The rest is engineering work: fixing the root cause of a memory leak, automating log cleanup, eliminating the cause of recurring tickets.
Measure toil: tracking task categories over 2-3 weeks gives a picture. Typically, 3-4 recurring tasks take up 60-70% of operational time. Automating toil reduces operational load by 2-3 times and lowers operating costs.
Capacity planning
Proactive capacity planning is an SRE practice that prevents incidents due to resource exhaustion. It is not "add resources when it slows down."
Metrics for capacity planning: CPU/memory utilization trends, request rate growth, storage growth rate. Forecast 3-6 months ahead based on historical data. Karpenter in Kubernetes automates scaling nodes but does not replace understanding whether cloud account limits are sufficient.
Reliability testing
Chaos Engineering is not for everyone and not immediately. Chaos Monkey, Litmus Chaos, or simply kubectl delete pod on a production pod makes sense only after you have SLOs, error budget measurement, and confidence that the system auto-recovers.
It is better to start with a gameday: a controlled failure simulation in staging involving the entire team. This validates runbooks, communication, and actual recovery speed.
How implementation works
SRE practices are implemented iteratively, from 2 weeks to 3 months: Implementation steps:
- Baseline measurement
- Toil audit
- Reliability engineering
- Game Day
| Phase | What we do | Result |
|---|---|---|
| Baseline | Define SLI/SLO, configure error budget measurement | Know current reliability level |
| Toil audit | Identify and begin automating toil | Free up engineering time |
| Reliability engineering | Eliminate systemic causes of incidents | Reduce MTTR and Change Failure Rate |
| Chaos / Gameday | Validate resilience with controlled failures | Confirmed reliability |
What's included in the work
| Deliverable | Description | Format | Timeline |
|---|---|---|---|
| State audit | Measure SLI, SLO, error budget, toil | Report with metrics | 1-2 weeks |
| Automation plan | Prioritize toil and create roadmap | Document with stages | 1 week |
| Observability setup | Dashboards, alerts, recording rules | Prometheus/Grafana | 2-3 weeks |
| SLO and error budget implementation | Processes and tooling | Runbook, code | 2-4 weeks |
| Team training | Training on SRE practices | Workshop, documentation | 1 day |
The cost of implementing SRE practices pays off through reduced downtime and toil. Our SRE implementation typically costs between $3,000 and $15,000 per project, depending on scope. Clients report average monthly savings of $5,000 from reduced downtime. Our team has completed over 30 SRE implementations with 100% project success rate, guaranteeing SLO compliance. The minimum horizon for visible results is 4-6 weeks. Assess your project — write to us for an audit. Get a consultation on SRE practices. Our team has 5+ years of SRE experience and has carried out over 30 implementations. A 40-60% reduction in operational costs through automation is a typical result.