Enterprise software has spent decades getting telemetry applications backwards. The interface you see reflects what the vendor decided to collect, to store and the workflows they decide to implement - not how your team actually works. Today we're changing that.
The problem with vendor-defined apps
When a team needs a workflow the vendor didn't anticipate, it has three choices: bend the workflow to fit the tool, cobble together a workaround, or file a request and wait. None of these scale. There is now a fourth and better option, read on to find out how to capitalize on it.
Why now
Telemetry volume is climbing fast. AI and agentic workloads are accelerating that growth, reshaping how teams think about pipelines while forcing hard trade-offs across cost, retention, and visibility. Collection, storage, and analysis remain fragmented across too many tools. The result is familiar: blind spots, dead-end investigations, climbing infrastructure costs, rising operational overhead, all landing just as IT and Security teams are being asked to support more teams, more use cases, more agentic AI solutions, and more stakeholders.
Vendor-defined apps make this worse, because they're generic by design. A team may already have the data and the platform primitives in place and still lack the workflow-specific app, the guided process, the access to the right data, or the tailored interface it needs. Every new telemetry-driven workflow that requires bespoke engineering turns your team into a bottleneck instead of an enabler. That's the failure mode we set out to design away.
The core idea: Cribl apps
Cribl apps let IT and Security teams build and run custom applications embedded directly into the Cribl platform. Cribl apps enable you to deliver the workflow experience any team or stakeholder needs without rebuilding the data foundation, buying additional infrastructure, or accepting a vendor-defined interface. Apps can use any modern front-end frameworks and can reach data wherever it lives — in Cribl Lake, federated across any data store, or ingested directly into Cribl Search.
There's a compounding benefit that I find particularly satisfying as an engineer: because the telemetry infrastructure is shared, you deploy it once and solve new and unforeseen problems on it again and again. Infrastructure cost stays predictable while the number of experiences built on top of it keeps growing. ROI improves over time rather than eroding with each new request.
What teams get to build
Using AI-assisted development and lightweight coding environments, teams build tailored apps, interfaces, and visualizations directly on top of trusted data, trusted infrastructure, mature access controls and APIs.
AI has collapsed the gap between knowing a workflow and building software for it. The shift comes down to a few things working together:
Experts become builders. Historically, an SME who understood a process deeply still had to file a ticket, queue behind other priorities, and explain their needs to someone who didn't share the domain context. Every translation step lost fidelity. Now the person who is the ‘requirements’ can describe what they want in plain language and get working software back, minimizing handoffs. The SME supplies judgment about what's actually useful; the AI supplies the code.
Software gets thinner and more agile. Enterprises used to standardize on a few heavyweight platforms because building anything was expensive. When an app costs an afternoon instead of a quarter, it becomes worth building software for a workflow used by twelve people, or by one person, or for one quarter. The economics that forced everyone into generic tools no longer apply, so software can finally fit the contour of the actual work instead of forcing the work to fit the tool.
Integration is the real unlock in SaaS specifically. Enterprise value rarely sits in a standalone app—it sits in connecting systems. MCP, APIs, and AI agents that can read from and write to existing platforms mean an SME can build the logic layer that stitches their tools together, rather than rebuilding the tools themselves. The expert builds the workflow, not the infrastructure.
We invested in designing a modern builder experience for speed: scaffolding, OpenAPI access, agent guidance, and live preview, so you can iterate quickly instead of launching a custom web project from scratch. It covers the full lifecycle - writing, previewing, packaging, upload, install, settings, listing, and in-product runtime. And because this happens inside the platform, it happens inside your guardrails: feature gating, quotas, logging, and governance controls keep app-building under platform-team oversight. Choice and control were non-negotiable design constraints, not afterthoughts.
Built-in guardrails — RBAC, access controls, governance — mean teams can move faster without the overhead of configuring every agent and environment from scratch. That matters as AI workloads become part of how you operate.
The outcome
More workflows and teams served from the same platform. Three concrete dimensions:
Operational: Reduce backlog and bespoke work by giving teams apps built around their workflows on shared, governed services.
Financial: Cut the need for separate tooling and lower the cost of managing telemetry growth, investigations, and platform sprawl.
Organizational: Reposition IT and Security teams from request bottlenecks into enablers who help the business move faster — with guardrails.
Enterprise software has spent decades asking teams to conform. We built Cribl's app capabilities to flip that. Build the workflow you need, on telemetry data and infrastructure you already trust and already paid for.
I'm looking forward to seeing what you build on it.
