What are SLOs/SLIs/SLAs?

4 MIN READ
6 MIN READ

You’ve likely noticed how some pizza places promise delivery in 30 minutes, or they’ll give you your money back. But what are they really promising? They’re setting a clear performance goal and backing it up with confidence. How do they measure their performance? They track how long each delivery takes. And why do they make this promise? Because fast service is key to keeping their business thriving.

In a similar way, technical service providers strive to deliver the highest possible level of uninterrupted performance to ensure their business success. Site Reliability Engineering (SRE) teams responsible for managing this performance use metrics like SLOs, SLAs, and SLIs. These metrics help them quantify system availability, ensuring reliability and alignment with business goals.

Let’s break down what SLOs, SLAs, and SLIs mean and how Mezmo can help you manage them for achieving operational excellence.

What are SLOs?

A Service-Level Objective (SLO) sets a specific target system availability, defined in percentage over a period of time. For example, if you set an SLO of 99.9% uptime, it indicates that the system should not be down for more than 43.2 minutes in a month.

SLOs are driven by the specific function, target user group, and business objectives. For example, cloud services or shopping sites may require SLOs of 99.99% to ensure business success, while internal applications may work with lower SLOs, such as 99.50%.

What are SLAs?

A Service Level Agreement (SLA) is a formal contract between a service provider and a customer that documents the specific services the provider will deliver and the performance standards the provider is obligated to maintain. An SLA defines measurable metrics such as uptime, response times, and responsibilities along with the penalties applicable if these standards are not met. 

The availability target specified in an SLA is often set lower than the internal SLO to ensure compliance even under challenging conditions. In the above example, cloud services or ecommerce sites may commit 99.95% availability in the SLAs, while internally setting SLOs of 99.99 %.

What are SLIs?

A Service Level Indicator (SLI) is a metric that measures how well a service meets its SLO over a specified period. For instance, if your SLO is set at 99.95% availability and your SLI measures 99.97%, you are compliant. However, if your SLI falls below 99.95%, you are not compliant. If it falls below the committed value in the SLA, the penalties outlined in the SLA will apply.

To maintain compliance, any downtime must be quickly addressed with an effective incident response plan and the appropriate tools to minimize impact and restore service quickly.

At a Glance

SLOs, SLAs, and SLIs help align business objectives with operational performance. With these metrics, you can define, track, and measure the agreed service availability targets to establish customer trust.

SLO
SLA
SLI
Definition Availability defined in percentage over a period of time Formal contract defining the committed availability standards Measurement of specific availability metric over a specified period
Purpose Establishes the availability target Sets clear terms and consequences for availability performance Provides a precise metric to assess whether the service is meeting its SLOs and SLAs
Stakeholders Internal - SREs, DevOps, and engineering teams External - Service providers and customers or users Internal - SREs, DevOps, and observability teams
Scope Internal goal Legally binding Specific measurement of performance

How Mezmo helps you maintain your SLOs, SLAs, and SLIs

Mezmo is a powerful observability platform designed to help SREs collect, centralize, understand, and analyze operational log data in real time, enabling rapid responses to system issues. By providing deep insights into telemetry data, consisting of logs, metrics, and events, Mezmo empowers businesses to effectively monitor system performance, troubleshoot problems, and manage SLOs and SLIs with precision.

Customers have reported an 80% improvement in the time it takes to access and utilize log data with Mezmo, significantly reducing downtime and ensuring compliance with SLAs. Additionally, Mezmo’s Telemetry Pipeline optimizes telemetry data for SLO monitoring tools, making it easier to create intuitive and visual SLI definitions, as showcased in this demo featuring Nobl9, an SLO platform tool.  

Want to know more about how Mezmo can help you identify SLIs and manage your SLOs? Reach out to our Technical Services team today.

Table of Contents

    Share Article

    RSS Feed

    Next blog post
    You're viewing our latest blog post.
    Previous blog post
    You're viewing our oldest blog post.
    Mezmo + Catchpoint deliver observability SREs can rely on
    Mezmo’s AI-powered Site Reliability Engineering (SRE) agent for Root Cause Analysis (RCA)
    What is Active Telemetry
    Launching an agentic SRE for root cause analysis
    Paving the way for a new era: Mezmo's Active Telemetry
    The Answer to SRE Agent Failures: Context Engineering
    Empowering an MCP server with a telemetry pipeline
    The Debugging Bottleneck: A Manual Log-Sifting Expedition
    The Smartest Member of Your Developer Ecosystem: Introducing the Mezmo MCP Server
    Your New AI Assistant for a Smarter Workflow
    The Observability Problem Isn't Data Volume Anymore—It's Context
    Beyond the Pipeline: Data Isn't Oil, It's Power.
    The Platform Engineer's Playbook: Mastering OpenTelemetry & Compliance with Mezmo and Dynatrace
    From Alert to Answer in Seconds: Accelerating Incident Response in Dynatrace
    Taming Your Dynatrace Bill: How to Cut Observability Costs, Not Visibility
    Architecting for Value: A Playbook for Sustainable Observability
    How to Cut Observability Costs with Synthetic Monitoring and Responsive Pipelines
    Unlock Deeper Insights: Introducing GitLab Event Integration with Mezmo
    Introducing the New Mezmo Product Homepage
    The Inconvenient Truth About AI Ethics in Observability
    Observability's Moneyball Moment: How AI Is Changing the Game (Not Ending It)
    Do you Grok It?
    Top Five Reasons Telemetry Pipelines Should Be on Every Engineer’s Radar
    Is It a Cup or a Pot? Helping You Pinpoint the Problem—and Sleep Through the Night
    Smarter Telemetry Pipelines: The Key to Cutting Datadog Costs and Observability Chaos
    Why Datadog Falls Short for Log Management and What to Do Instead
    Telemetry for Modern Apps: Reducing MTTR with Smarter Signals
    Transforming Observability: Simpler, Smarter, and More Affordable Data Control
    Datadog: The Good, The Bad, The Costly
    Mezmo Recognized with 25 G2 Awards for Spring 2025
    Reducing Telemetry Toil with Rapid Pipelining
    Cut Costs, Not Insights:   A Practical Guide to Telemetry Data Optimization
    Webinar Recap: Telemetry Pipeline 101
    Petabyte Scale, Gigabyte Costs: Mezmo’s Evolution from ElasticSearch to Quickwit
    2024 Recap - Highlights of Mezmo’s product enhancements
    My Favorite Observability and DevOps Articles of 2024
    AWS re:Invent ‘24: Generative AI Observability, Platform Engineering, and 99.9995% Availability
    From Gartner IOCS 2024 Conference: AI, Observability Data, and Telemetry Pipelines
    Our team’s learnings from Kubecon: Use Exemplars, Configuring OTel, and OTTL cookbook
    How Mezmo Uses a Telemetry Pipeline to Handle Metrics, Part II
    Webinar Recap: 2024 DORA Report: Accelerate State of DevOps
    Kubecon ‘24 recap: Patent Trolls, OTel Lessons at Scale, and Principle Platform Abstractions
    Announcing Mezmo Flow: Build a Telemetry Pipeline in 15 minutes
    Key Takeaways from the 2024 DORA Report
    Webinar Recap | Telemetry Data Management: Tales from the Trenches
    What are SLOs/SLIs/SLAs?
    Webinar Recap | Next Gen Log Management: Maximize Log Value with Telemetry Pipelines
    Creating In-Stream Alerts for Telemetry Data
    Creating Re-Usable Components for Telemetry Pipelines
    Optimizing Data for Service Management Objective Monitoring
    More Value From Your Logs: Next Generation Log Management from Mezmo
    A Day in the Life of a Mezmo SRE
    Webinar Recap: Applying a Data Engineering Approach to Telemetry Data
    Dogfooding at Mezmo: How we used telemetry pipeline to reduce data volume
    Unlocking Business Insights with Telemetry Pipelines
    Why Your Telemetry (Observability) Pipelines Need to be Responsive
    How Data Profiling Can Reduce Burnout
    Data Optimization Technique: Route Data to Specialized Processing Chains
    Data Privacy Takeaways from Gartner Security & Risk Summit
    Mastering Telemetry Pipelines: Driving Compliance and Data Optimization
    A Recap of Gartner Security and Risk Summit: GenAI, Augmented Cybersecurity, Burnout
    Why Telemetry Pipelines Should Be A Part Of Your Compliance Strategy
    Pipeline Module: Event to Metric
    Telemetry Data Compliance Module
    OpenTelemetry: The Key To Unified Telemetry Data
    Data optimization technique: convert events to metrics
    What’s New With Mezmo: In-stream Alerting
    How Mezmo Used Telemetry Pipeline to Handle Metrics
    Webinar Recap: Mastering Telemetry Pipelines - A DevOps Lifecycle Approach to Data Management
    Open-source Telemetry Pipelines: An Overview
    SRECon Recap: Product Reliability, Burn Out, and more
    Webinar Recap: How to Manage Telemetry Data with Confidence
    Webinar Recap: Myths and Realities in Telemetry Data Handling
    Using Vector to Build a Telemetry Pipeline Solution
    Managing Telemetry Data Overflow in Kubernetes with Resource Quotas and Limits
    How To Optimize Telemetry Pipelines For Better Observability and Security
    Gartner IOCS Conference Recap: Monitoring and Observing Environments with Telemetry Pipelines
    AWS re:Invent 2023 highlights: Observability at Stripe, Capital One, and McDonald’s
    Webinar Recap: Best Practices for Observability Pipelines
    Introducing Responsive Pipelines from Mezmo
    My First KubeCon - Tales of the K8’s community, DE&I, sustainability, and OTel
    Modernize Telemetry Pipeline Management with Mezmo Pipeline as Code
    How To Profile and Optimize Telemetry Data: A Deep Dive
    Kubernetes Telemetry Data Optimization in Five Steps with Mezmo
    Introducing Mezmo Edge: A Secure Approach To Telemetry Data
    Understand Kubernetes Telemetry Data Immediately With Mezmo’s Welcome Pipeline
    Unearthing Gold: Deriving Metrics from Logs with Mezmo Telemetry Pipeline
    Webinar Recap: The Single Pane of Glass Myth
    Empower Observability Engineers: Enhance Engineering With Mezmo
    Webinar Recap: How to Get More Out of Your Log Data
    Unraveling the Log Data Explosion: New Market Research Shows Trends and Challenges
    Webinar Recap: Unlocking the Full Value of Telemetry Data
    Data-Driven Decision Making: Leveraging Metrics and Logs-to-Metrics Processors
    How To Configure The Mezmo Telemetry Pipeline
    Supercharge Elasticsearch Observability With Telemetry Pipelines
    Enhancing Grafana Observability With Telemetry Pipelines
    Optimizing Your Splunk Experience with Telemetry Pipelines
    Webinar Recap: Unlocking Business Performance with Telemetry Data
    Enhancing Datadog Observability with Telemetry Pipelines
    Transforming Your Data With Telemetry Pipelines