As companies grow, their infrastructure becomes exponentially more complex. Early-stage DevOps, designed to bridge development and operations, eventually reach a breaking point.
Instead of enabling innovation, they become overwhelmed with incident response, toil, and endless configuration management. What started as a high-velocity approach to infrastructure turns into reactive firefighting, slowing down product development.
The problem isn’t tooling or automation; it’s a structural misalignment at scale. The answer lies in platform engineering.
Unlike traditional DevOps teams, which focus on CI/CD and infrastructure automation, platform teams build scalable, self-service developer platforms that eliminate bottlenecks. This shift is crucial for companies transitioning from startup agility to enterprise-grade reliability.
As an engineering leader and platform engineer, I’ve seen firsthand why platform engineering is the future of scalable, reliable infrastructure. In this piece, I’ll explain how this shift enables teams to move beyond firefighting and build a developer-friendly infrastructure.
The Difference between DevOps, SRE, and Platform Engineering
To understand why platform engineering is the answer, it’s crucial to distinguish it from DevOps and SRE. While these roles overlap, they serve distinct functions, each with different priorities when scaling infrastructure.
Infrastructure teams often fall into three overlapping but distinct categories:
| Function | Key Focus | Primary Responsibility |
| DevOps | Speeding up deployment cycles, CI/CD | Bridge dev and ops, automate pipelines |
| SRE (Site Reliability Engineering) | Reliability, uptime, incident response | Ensure system availability and reliability |
| Platform Engineering | Developer self-service, infrastructure as a product | Build internal tools, standardize workflows, optimize developer experience |
But what’s their key distinction? While DevOps and SRE focus on operations, platform engineering is about enablement.
Rather than constantly firefighting or fine-tuning individual pipelines, platform engineers create reusable internal platforms that developers can use seamlessly. This model allows teams to scale without exponentially increasing operational overhead.
Platform Engineering as a Strategic Function
At scale, developer bottlenecks are a bigger problem than system failures. Velocity slows if engineers wait for infrastructure teams to provision environments, debug configurations, or troubleshoot CI/CD pipelines.
Platform engineering solves this issue by treating internal infrastructure like a product, built with usability, self-service, and developer experience in mind.
But why does it matter at scale? Here’s why:
- It removes bottlenecks – Developers can ship features without infra dependencies.
- It reduces cognitive load – Engineers focus on code, not infrastructure.
- It standardizes best practices – Security, compliance, and observability are built-in, not bolted on.
By shifting infrastructure from an operational burden to an enabler, platform engineering ensures that scaling doesn’t come at the cost of developer productivity. Instead of fighting fires, teams can focus on innovation building and deploying confidently.
Structuring Platform Teams with Dedicated PMs
To ensure that platform engineering succeeds, teams need a structured approach that prioritizes adoption, usability, and developer enablement. A well-defined team structure is essential for achieving this.
One of the companies’ biggest mistakes when adopting platform engineering is treating it as another ops team.
A successful platform team doesn’t merely build internal tools; it drives adoption, ensures usability, and iterates based on developer needs.
And this requires a product mindset.
But why? It’s because traditional infrastructure teams often struggle with adoption. After all, a great internal platform is useless if no one uses it.
Dedicated PMs in platform teams must:
- Understand developer pain points to prioritize the right features.
- Drive adoption and ensure teams don’t build shadow infrastructure.
- Balance innovation vs. stability to avoid unnecessary complexity.
A well-structured engineering team includes key roles that balance developer enablement, operational efficiency, and scalability. Here’s a breakdown of those roles:
This structure balances developer enablement, operational efficiency, and scalability.
Let’s break down each role below:
- Head of Platform Engineering – Defines platform strategy and ensures alignment with business and engineering goals.
- Platform Product Manager (PPM) – Reports to the Chief Product Officer (CPO) to maintain a strong product focus while collaborating closely with engineering leadership to prioritize developer needs.
- Core Teams:
- Infrastructure and Cloud – Oversees Kubernetes, CI/CD, automation, and infrastructure scaling.
- Developer Experience (DevEx) – It builds self-service internal tools to streamline developer workflows.
- Security and Compliance (InfoSec Hands-on Team) – Works within the infrastructure org to enforce security best practices and governance but operates in close coordination with a dedicated security organization.
- SRE/Production Engineering – Ensures reliability, monitoring, and incident response.
- Embedded Platform Engineers (Optional) – Sit within product teams to align infrastructure with development needs, bridging platform and application engineering.
This structure helps platform teams operate as internal product organizations, delivering scalable infrastructure while improving developer productivity.
The Shift to Platform Engineering: A Product Mindset
At Bumble, we realized that scaling infrastructure successfully isn’t simply about technical automation; it requires treating internal platforms like products that engineers want to use.
Scaling a backend platform for 60M+ monthly active users (MAU) is a technological and organizational challenge. As infrastructure grows, the real bottleneck becomes how effectively developers can leverage their tools.
Here’s what we learned:
1. Self-Service Beats Automation
Early on, our infra team automated CI/CD and service provisioning, but velocity suffered if developers still had to wait on platform teams for exceptions or debugging.
In short, self-service infrastructure eliminates bottlenecks and keeps teams moving.
2. Developer Experience (DX) Matters as Much as Reliability
A powerful but complex platform slows engineers down. We can improve DX by doing the following:
- Opinionated Defaults: Pre-configured settings reduced cognitive load.
- Clear Observability: Teams debugged issues without platform intervention.
- Frictionless Onboarding: New engineers deployed code without digging through documents.
- Effortless Information Discovery: Engineers quickly found the right documentation, APIs, and best practices without unnecessary delays.
Implementing these strategies made the platform easy to use and stable for engineers of all experience.
3. Measure Adoption, Not Just Performance
Working on the project, we initially tracked uptime and latency but not developer adoption. This resulted in shadow infrastructure teams building their own solutions instead of using the platform.
To fix this, we measured instead:
- Time to First Deploy – How quickly can new engineers ship?
- Usage Metrics – Are teams actually adopting internal tools?
- Developer NPS – Would they recommend the platform?
With these measurements, we realized that platform success means more when more developers adopt them, not just when they’re reliable.
4. Iterate Like a Product Team
At first, we built features based on assumptions. Without continuous developer feedback, we solved the wrong problems.
So, we shifted to a product-driven approach:
- Regular User Research – Getting feedback from engineers using “Thrive Surveys” via CultureAmp every quarter.
- Metrics-driven Iteration – Shipping updates based on usage data.
- Platform PM Ownership – Ensuring usability, not just infrastructure.
This shift dramatically improved engineering velocity, cutting bottlenecks, reducing onboarding from weeks to days, and boosting platform adoption.
When supporting millions of users, scaling infrastructure isn’t just about handling traffic; it’s about helping engineers ship faster.
Pitfalls: Over-Indexing on Tooling without Product Vision
Many companies believe buying the latest Internal Developer Platform (IDP) will solve their scaling problems.
However, it won’t and here’s why:
- IDPs are useless without developer adoption.
- More automation doesn’t mean a better developer experience.
- Security, compliance, and observability must be native, not afterthoughts.
Avoiding these pitfalls is just as crucial as adopting platform engineering itself. By focusing on usability, adoption, and developer experience, organizations can unlock the benefits of a scalable infrastructure.
In our case at Bumble, we developed our own IDP, customized it, and migrated it to Cortex to overcome the challenges we previously faced and prevent them from happening again.
Conclusion: Evolving Infrastructure Teams for the Future
As organizations scale, traditional DevOps models often struggle to keep pace, leading to inefficiencies and operational bottlenecks. Platform engineering provides a structured, product-oriented approach that empowers developers and drives long-term stability.
The next step for CTOs and engineering leaders is to evolve your platform organization into a strategic enabler. Assess your current structure, invest in platform teams with a product mindset, and shift from ad-hoc solutions to a scalable, developer-first approach.
Scaling infrastructure is a challenge. How is your team handling it? Let’s connect and discuss how platform engineering can drive real results for your organization.
References:
Honeycomb. (2022). The future of ops: Platform engineering. Retrieved from https://www.honeycomb.io/blog/future-ops-platform-engineering
Humanitec. (2023). Platform engineering: A new paradigm for developer enablement. Retrieved from https://humanitec.com/blog
Thoughtworks. (2024). Technology Radar: Assessing emerging trends in software development. Retrieved from https://www.thoughtworks.com/radar
Avdyushkin, S. (2024). Lessons from scaling Bumble’s backend for 60M MAU. Internal case study, Bumble Inc.
Featured Image via Shutterstock)
