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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

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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

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.