Setting effective SLOs for cloud-native applications

Service level objectives (SLOs) are essential for balancing reliability and innovation in cloud-native applications. Without clear SLOs, teams struggle to prioritize work, manage incidents, or understand user expectations. This post explains how to implement effective SLOs for cloud-native systems, ensuring you meet user needs while fostering innovation.

Why SLOs matter for cloud-native applications

Cloud-native applications are designed for scalability and resilience, but they also introduce complexity. Microservices, containers, and distributed systems make it harder to track reliability and performance. SLOs help by defining clear, measurable goals for your services. They answer questions like:

• How reliable does this service need to be?
• What level of performance is acceptable?
• How quickly should issues be resolved?

SLOs also align teams around shared goals. Instead of guessing what users expect, SLOs provide a framework for making data-driven decisions about reliability and feature development.

What makes an effective SLO?

A good SLO is:

• User-focused: It reflects what matters most to your users. For example, availability might be critical for an e-commerce site, while latency could be more important for a real-time analytics platform.
• Measurable: It uses clear metrics, such as error rates, request latency, or system availability.
• Achievable: It balances reliability and innovation. Overly strict SLOs can stifle progress, while overly lenient ones risk user satisfaction.
• Actionable: It helps teams prioritize work and make trade-offs.

Example: An availability SLO

A common SLO for cloud-native applications is availability. For instance:

• 99.9% of requests to the API service must succeed over a 30-day rolling window.

This SLO is:

• User-focused: It ensures the service is reliable for users.
• Measurable: It uses a clear metric (request success rate).
• Achievable: 99.9% availability is realistic for many cloud-native systems.
• Actionable: It helps teams decide when to focus on reliability improvements versus new features.

How to implement SLOs for cloud-native applications

1. Start with user expectations

SLOs should reflect what users care about. Begin by identifying the key aspects of your service that impact user satisfaction. For example:

• For a payment processing system, reliability and low latency are critical.
• For a video streaming service, availability and buffering time matter most.

Talk to users, analyze usage patterns, and review incident reports to understand their priorities.

2. Define clear metrics

Once you know what matters to users, define metrics to measure those aspects. Common SLO metrics include:

• Availability: The percentage of time a service is operational.
• Latency: The time it takes to respond to requests.
• Error rate: The percentage of requests that fail.
• Throughput: The number of requests a service can handle.

Choose metrics that are easy to monitor and align with user expectations.

3. Set realistic targets

SLOs should be challenging but achievable. If your targets are too high, you risk burning out your team. If they’re too low, you risk disappointing users.

Use historical data to set a baseline, then adjust based on user feedback and business goals. For example, if your API has historically been available 99.5% of the time, you might set an SLO of 99.7% to push for improvement without overcommitting.

4. Monitor and alert on SLOs

To ensure your SLOs are met, you need to track them in real time. Use monitoring tools like Prometheus, Grafana, or Datadog to collect and visualize SLO metrics. Set up alerts to notify your team when SLOs are at risk or breached.

For example, if your availability SLO is 99.9%, you might set an alert to trigger when availability drops below 99.95%. This gives your team time to respond before the SLO is breached.

5. Use error budgets to balance reliability and innovation

Error budgets are a key concept in SLO-driven development. They represent the acceptable amount of “failure” within your SLO. For instance, if your availability SLO is 99.9%, your error budget is 0.1% (or about 43 minutes of downtime per month).

Use error budgets to decide when to focus on reliability improvements versus new features. If you’re consuming your error budget too quickly, prioritize reliability work. If you’re well within your budget, you can focus on innovation.

6. Review and iterate

SLOs are not set in stone. Regularly review your SLOs to ensure they still align with user expectations and business goals. Adjust targets as needed based on feedback, changes in user behavior, or new features.

For example, if users start complaining about slow performance, you might need to tighten your latency SLO. Conversely, if your service becomes more stable, you might relax your availability SLO to allow for more innovation.

Example: SLOs for a cloud-native e-commerce platform

Here’s how you might set SLOs for a cloud-native e-commerce platform:

• Availability: 99.95% of requests to the checkout service must succeed over a 30-day rolling window.
• Latency: 95% of requests to the product catalog must complete in under 200ms.
• Error rate: The order processing service must have an error rate of less than 0.1% over a 7-day rolling window.

These SLOs are user-focused, measurable, achievable, and actionable. They help the team prioritize work and ensure the platform meets user expectations.

Conclusion

Effective SLOs are a cornerstone of reliable cloud-native applications. By defining clear, user-focused objectives, you can align your team, prioritize work, and ensure your services meet user expectations. Start with user needs, set realistic targets, and use monitoring and error budgets to stay on track. With the right approach, SLOs can help you build resilient, user-centric cloud-native systems.