Every digital company must balance the stability of their platform versus the speed of innovation. Platform engineers have to ensure consistent, stable performance; in contrast, developers prioritize speed of innovation, ease of data access, and the ability to move fast (and perhaps break some things along the way). 

I’ve lived through this chaos myself. In my previous role at a UK-based digital lender, my application teams won their independence, each group spinning up and managing their own microservices, Kafka brokers, and infrastructure. But without standardization, consistent policy enforcement, and the oversight of a central authority, we encountered large cost overruns, incompatibilities, and even issues with downstream applications.

Ultimately, I learned that developer autonomy without guardrails will just create operational chaos. But tilt too far the other way, cracking down too hard on developers, and you risk stagnation.

What teams really need is self-service with guardrails: the ability to move fast within a set of standardized parameters.

Govern Kafka or experience havoc

Thanks to its ability to ingest real-time data streams, decouple producer from consumer, and transmit data asynchronously, Apache Kafka is indispensable to most digital environments. That’s why Kafka feeds mission-critical applications across many verticals and companies: preventive maintenance for factories, personalized recommendations for online retailers, and fraud detection for financial institutions. 

Kafka is powerful, but it’s difficult to use at scale—and dangerous if misused. Without the right boundaries and workflows in place, teams will run into:

• Unexpected consequences. Once bad data gets into your application, it causes latency, outages, or something entirely unplanned, such as thousands of fake customers, which took many weeks (and plenty of visits with auditors) to undo. 

• Unclear ownership and accountability. Without centralized tools and standardized workflows, teams cannot audit, attribute costs, onboard users, manage infrastructure, or maintain consistency.

• Collaboration gaps. Team A can’t work with Team B without dragging an overworked platform team into the middle—and even then, policies may not be enforced consistently.

• Cloud waste. Lacking set limits, developers spun up partitions and topics at will. Zombie infrastructure lingered, quietly running up cloud bills. No one noticed until it was too late.

• Broken AI and ML outputs. Machine learning models trained on data lakes rarely matched the operational data flowing through Kafka, requiring a multi-week rework and stalling our project.

This leads to a vicious cycle: developers push for autonomy, platform teams scramble to clean up the aftermath, and the same problems reappear again and again. The root cause wasn’t Kafka itself—it was the absence of self-service with guardrails.

Why you need Conduktor Scale

Conduktor Scale was built for this exact need: helping platform teams give developers autonomy—while preventing them from (accidentally) destroying their Kafka environment. Through its self-service abilities, Scale:

• Standardizes ad hoc procedures, enforcing consistency in naming conventions, policies, and practices across teams.  

• Centralizes self-service workflows, permissions, and access in one place.

• Automates overly manual processes, so platform teams don’t become bottlenecks. 

Despite what it may seem, speed without safety brings problems in the long run, leading to delayed launches, forced re-engineering, and audit nightmares. Developers prefer to provision their own resources, and Conduktor provides a way for them to do so—without breaking anything or forcing the platform team to clean up after the fact.

If you’ve felt the same pains that I did, book a demo and see how Conduktor brings safe, efficient self-service abilities to Kafka.