For observability adoption to scale, you must eliminate the bottlenecks. A self-service approach is the only sustainable model, enabling all teams–not just a select few–to access, implement, and scale observability easily. But making the shift requires more than access: you have to design for it.
If you're responsible for the success of observability tools in your organization — whether you're on a platform engineering team, part of the observability CoE, or simply the go-to for making observability usable — this article is for you.
With any luck, you have a solid observability toolset. But usage is inconsistent, requests are piling up. Your team is buried in repetitive tasks instead of advancing the observability practice. Teams aren’t adopting the tools as expected, and the platform’s value isn’t being fully realized.
The path forward? Self-service observability!
In this article, let’s explore why self-service for observability is no longer optional, what happens when you don’t embrace it, and the key considerations to help you scale adoption across the organization — all without burning out the people behind the tools.
Self-service observability is a service delivery model that empowers teams to independently create, manage, and improve observability assets without depending solely on a centralized request-fulfillment process. Observability assets may include:
Self-service o11y is a framework for giving teams hands-on access to observability, so that the people closest to the systems they build and support can take full ownership. These teams are best positioned to:
Self-service doesn’t mean chaos or a free-for-all. It’s about equipping engineers with the access, skills, and patterns they need to build meaningful, sustainable observability.
This shift matters because it accelerates adoption and drives real value realization from your observability investments. When more teams can use the tooling effectively, the organization at large sees:
It also frees up your observability and platform engineering teams to focus where they’re needed most: enabling lower-tech users, building tighter integrations, improving tagging and telemetry standards, and automating observability through pipelines and APIs.
Self-service isn’t a lack of structure — it’s the framework for how you scale structure through enablement.
In my role here at Splunk, I work with organizations of all shapes and sizes. Some are scaling observability practices with strong momentum. Others are still stuck treating observability like a legacy monitoring service — specifically, the full-service delivery model.
Here’s a simple example that illustrates the full-service model. A team submits a request:
Then they wait. Sometimes days. Sometimes weeks. Sometimes the request falls through entirely.
When every request, no matter how basic, has to go through a central queue, both sides suffer. Requestors are left waiting. Fulfillment teams are buried in low-value, repetitive tasks. Instead of working on strategic observability initiatives — like platform integration, automation, and pattern development — the fulfillment team becomes the bottleneck.
And the entire org slows down.
As the organization grows, this model simply doesn’t scale. If you want observability to move at the pace of innovation, teams need the autonomy to help themselves — with the backing and assurance of observability standards, patterns, and support.
When observability is offered only as a full-service model, the downstream impacts are operational and organizational. This model slows teams down, creates visibility gaps, and limits the business value of your observability investments. You’ll start to see symptoms like:
The result? Visibility gaps. Slower MTTR. Underutilized tooling. And engineering teams that don’t have the insight they need to protect the customer experience.
Now, to be clear, full-service observability isn’t inherently bad. In fact, it’s essential in many orgs. Less technical teams, business units without direct engineering support, or advanced use cases (like executive dashboards or cross-domain event correlation) often require a centralized team to lead the charge.
But that model shouldn't be the default for everyone.
As organizations adopt the SRE-inspired mindset of “you build it, you own it”, observability ownership becomes a shared responsibility, through a self-service framework, that includes ensuring…
Before you can enable self-service observability, you need to ask:
Is your observability platform capable of supporting self-service?
Many teams attempt to scale observability using legacy tools built for centralized control, not team ownership. Just to operate, these platforms often require niche expertise, rigid UI paths, and deep tribal knowledge. These tools certainly weren’t built for the speed, scale, or team-based delivery model that modern organizations demand.
A self-service-capable observability platform is a requirement for scaling adoption, realizing value, and freeing up your observability and platform engineering teams to focus on what matters most.
A self-service capable observability platform must be able to support things like:
If your platform lacks these fundamentals, you’re not facing friction — you’re facing a blocker.
Looking for a platform that delivers these must-have features? Splunk Observability Cloud is a leading modern observability platform that supports end-to-end visibility and enables self-service observability across the enterprise.
Check out this Splunk Tech Talk that shows these concepts in action:
Is your observability platform self-service capable? Great! The next challenge is making it real and scaling it.
This section is especially relevant for observability platform owners, administrators, CoE members, or anyone helping scale adoption across teams. It outlines the foundational pieces that enable teams to confidently use observability tools, without bottlenecking progress or overwhelming central support.
Self-service doesn’t just happen when you open up access. It takes structure, patterns, and support systems to drive usage, maturity, and real business value.
A solid foundation sets everyone up for success. Start with readiness, not assumptions, and provide users with knowledge:
Make the right path the easiest one to follow.
Observability works best when it’s part of the build, not an afterthought.
Support more teams without overwhelming your experts.
(Related reading: see what AI can do in Splunk Observability Cloud.)
Use your observability platform to guide adoption.
Make observability matter, beyond incidents. This makes it easier for teams to see how it works and feel confident they can do it, too.
You cannot scale observability by adding more tickets, more admins, more process. You scale it by removing friction and enabling teams to help themselves.
Self-service observability isn’t just a service delivery model, it’s how you turn your observability tools into a force multiplier. With the right platform, structure, and enablement, teams can harness observability as the key driver of resilience, velocity, and insight.
Love O11Y content like this? Check out the other blogs in this series
Want to see this in action? Try it yourself with this free Observability Cloud trial and explore how the solution supports self-service observability.
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