Skip to main content

How to Create Programmatic Service Level Indicators and Service Level Objectives

Ishan Mukherjee
New Relic

Programmatically tracked service level indicators (SLIs) are foundational to every site reliability engineering practice. When engineering teams have programmatic SLIs in place, they lessen the need to manually track performance and incident data. They're also able to reduce manual toil because our DevOps teams define the capabilities and metrics that define their SLI data, which they collect automatically — hence "programmatic."

Programmatic SLIs have three key characteristics: they're current (they reflect the state of a system right now), they're automated (they're reported by instrumentation, not by humans), and they're useful (they're selected based on what a system's user cares about). In this post, I'll explain how site reliability engineers (SREs) can help their teams develop and create programmatic SLIs.

SLIs — Identifying Capabilities

An important part of creating programmatic SLIs is identifying the capability of the system or service for which you're creating the SLI. Here are a few definitions:

■ A system is a group of services and infrastructure components that exposes one or more capabilities to external customers (either end users or other internal teams).

■ A service is a runtime process (or a horizontally-scaled tier of processes) that makes up a portion of a system.

■ A capability is a particular aspect of functionality exposed by a service to its users, phrased in plain-language terms.

SLIs and SLOs — Indicators and Objectives

But first, we need some more definitions. An indicator is something you can measure about a system that acts as a proxy for the customer experience. An objective is a goal for a specific indicator that you're committed to achieving.

Configuring indicators and objectives is the easy part. The hard part is thinking through what measurable system behavior serves as a proxy for customer experience. When setting system-level SLIs, think about the key performance indicators (KPIs) for those systems, for example:

■ User-facing system KPIs most often include availability, latency, and throughput.

■ Storage system KPIs often emphasize latency, availability, and durability.

■ Big data systems, such as data processing pipelines, typically use KPIs such as throughput and end-to-end latency.

Your indicators and objectives should provide an accurate snapshot of the impact of your system on your customers.

A more precise description of the indicator and objective relationship is to say that SLIs are expressed in relation to service level objectives (SLOs). When you think about the availability of a system, for example, SLIs are the key measurements of the availability of the system while SLOs are the goals you set for how much availability you expect out of that system. And service level agreements (SLAs) explain the results of breaking the SLO commitments.

Create Programmatic SLIs

You should write your programmatic SLIs in collaboration with your product managers, engineering managers, and individual contributors who work on a system. To define your programmatic SLIs (and SLOs), apply these steps:

1. Identify the system and its services.

2. Identify the customer-facing capabilities of the system or services.

3. Articulate a plain-language definition of what it means for each capability to be available.

4. Define one or more SLIs for that definition.

5. Measure the system to get a baseline.

6. Define an SLO for each capability, and track how you perform against it.

7. Iterate and refine our system, and fine-tune the SLOs over time.

Example capabilities and definitions

Here are two example capabilities and definitions for an imaginary team that manages an imaginary dashboard service:

Capability: Dashboards overview.

Availability Definition: Customers are able to select the dashboard launcher, and see a list of all dashboards available to them.

Capability: Dashboards detail view.

Availability Definition: Customers can view a dashboard, and widgets render accurately and timely manner.

To express these availability definitions as programmatic SLIs (with SLOs to measure them), you'd state these service capabilities as:

■ Requests for the full list of available dashboards returns within 100 milliseconds 99.9% of the time.

■ Requests to open the dashboard launcher complete without error 99.9% of the time.

■ Requests for an individual dashboard return within 100 milliseconds 99.9% of the time.

■ Requests to open an individual dashboard complete without error 99.9% of the time.

After you've settled on your SLIs, they should be reasonably stable, but systems evolve, and you'll need to revisit them regularly. It's a good idea to revisit them quarterly, or whenever you make changes to your services, traffic volume, and upstream and downstream dependencies.

Ishan Mukherjee is SVP of Growth at New Relic

Hot Topics

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

How to Create Programmatic Service Level Indicators and Service Level Objectives

Ishan Mukherjee
New Relic

Programmatically tracked service level indicators (SLIs) are foundational to every site reliability engineering practice. When engineering teams have programmatic SLIs in place, they lessen the need to manually track performance and incident data. They're also able to reduce manual toil because our DevOps teams define the capabilities and metrics that define their SLI data, which they collect automatically — hence "programmatic."

Programmatic SLIs have three key characteristics: they're current (they reflect the state of a system right now), they're automated (they're reported by instrumentation, not by humans), and they're useful (they're selected based on what a system's user cares about). In this post, I'll explain how site reliability engineers (SREs) can help their teams develop and create programmatic SLIs.

SLIs — Identifying Capabilities

An important part of creating programmatic SLIs is identifying the capability of the system or service for which you're creating the SLI. Here are a few definitions:

■ A system is a group of services and infrastructure components that exposes one or more capabilities to external customers (either end users or other internal teams).

■ A service is a runtime process (or a horizontally-scaled tier of processes) that makes up a portion of a system.

■ A capability is a particular aspect of functionality exposed by a service to its users, phrased in plain-language terms.

SLIs and SLOs — Indicators and Objectives

But first, we need some more definitions. An indicator is something you can measure about a system that acts as a proxy for the customer experience. An objective is a goal for a specific indicator that you're committed to achieving.

Configuring indicators and objectives is the easy part. The hard part is thinking through what measurable system behavior serves as a proxy for customer experience. When setting system-level SLIs, think about the key performance indicators (KPIs) for those systems, for example:

■ User-facing system KPIs most often include availability, latency, and throughput.

■ Storage system KPIs often emphasize latency, availability, and durability.

■ Big data systems, such as data processing pipelines, typically use KPIs such as throughput and end-to-end latency.

Your indicators and objectives should provide an accurate snapshot of the impact of your system on your customers.

A more precise description of the indicator and objective relationship is to say that SLIs are expressed in relation to service level objectives (SLOs). When you think about the availability of a system, for example, SLIs are the key measurements of the availability of the system while SLOs are the goals you set for how much availability you expect out of that system. And service level agreements (SLAs) explain the results of breaking the SLO commitments.

Create Programmatic SLIs

You should write your programmatic SLIs in collaboration with your product managers, engineering managers, and individual contributors who work on a system. To define your programmatic SLIs (and SLOs), apply these steps:

1. Identify the system and its services.

2. Identify the customer-facing capabilities of the system or services.

3. Articulate a plain-language definition of what it means for each capability to be available.

4. Define one or more SLIs for that definition.

5. Measure the system to get a baseline.

6. Define an SLO for each capability, and track how you perform against it.

7. Iterate and refine our system, and fine-tune the SLOs over time.

Example capabilities and definitions

Here are two example capabilities and definitions for an imaginary team that manages an imaginary dashboard service:

Capability: Dashboards overview.

Availability Definition: Customers are able to select the dashboard launcher, and see a list of all dashboards available to them.

Capability: Dashboards detail view.

Availability Definition: Customers can view a dashboard, and widgets render accurately and timely manner.

To express these availability definitions as programmatic SLIs (with SLOs to measure them), you'd state these service capabilities as:

■ Requests for the full list of available dashboards returns within 100 milliseconds 99.9% of the time.

■ Requests to open the dashboard launcher complete without error 99.9% of the time.

■ Requests for an individual dashboard return within 100 milliseconds 99.9% of the time.

■ Requests to open an individual dashboard complete without error 99.9% of the time.

After you've settled on your SLIs, they should be reasonably stable, but systems evolve, and you'll need to revisit them regularly. It's a good idea to revisit them quarterly, or whenever you make changes to your services, traffic volume, and upstream and downstream dependencies.

Ishan Mukherjee is SVP of Growth at New Relic

Hot Topics

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...