When tech companies hit hypergrowth, they face the challenge of evolving their software systems from minimally viable products (MVPs) to enterprise-grade platforms. These transformations extend beyond software development to affect entire organizations.

The primary engineering challenge is addressing technology debt. As companies experience hypergrowth, their platforms must scale up to match demand and volume. To succeed in the transition from start-up to scale-up, it’s crucial to identify and address troublesome patterns in MVP distributed systems.

During this transition, problems that impact scalability and reliability also degrade velocity (developer efficiency), developer experience, and testability. These impairments can obstruct business growth as severely as compromised production service levels.

The Urgency Pitfall

Shortcuts in design, implementation, deployment, and architecture cause most technology debt issues, known as “decision debt.” Technology shortcuts rarely work - and most don’t fulfil their promise and actually accelerate a launch. But if time is pressing, a “barbell” strategy works best: either take a fast shortcut for MVP purposes and intentionally accept the debt, or take the time and build for scale. Middle-ground approaches just cause delays and tech debt without any benefit.

Recurring Patterns in Technology Debt

1. Non-deterministic Behavior Under Load

These issues typically arise from timing-dependent distributed code that assumes remote work will complete within fixed intervals. Common problems include:

• Polling for success instead of implementing async callbacks
• Using sleep functions to rely on remote completion
• Untested timeout code paths that create timing-dependent behavior

2. Weak Completion Guarantees

Issues often involve delivery failures of RPCs and queued messages, alongside problems with stale delivery. Key concerns include:

• Enqueuing without asynchronous continuation handling
• Overlapped slow remote API calls or database queries without proper isolation
• Non-idempotent remote API calls and inefficient read-modify-write operations

3. Impaired Scalability

Common scalability issues include:

• Polling for results instead of event-driven mechanisms

  • Use webhooks, websockets or callback messaging instead
  • Prohibit synchronous “sleeping”

• Multiple sequential remote API calls that should be batched

  • Implement proper pagination capabilities

• Inefficient queue management

4. Impaired Agility

Key problems include:

• Services deployed as tightly-coupled bundles

  • these are monoliths masquerading as multiple services

• Lack of independent version control and deployment

• Insufficient attention to backwards-compatible internal APIs

• Monolithic structures without clean internal boundaries

5. Impaired Reliability

Common reliability issues include:

• Services sharing databases inappropriately
• Failure to implement and test graceful shutdown procedures
• Inadequate testing of alerting systems
• Lack of proper health checks and monitoring

Sensible Defaults

• Implement asynchronous completion mechanisms instead of polling
• Design for idempotence from the start
• Create clean service boundaries with independent deployment capabilities
• Ensure proper testing of all failure modes
• Implement comprehensive monitoring and alerting systems

While not exhaustive, these patterns represent common challenges faced by engineering teams transitioning from start-up to scale-up. Addressing these issues early can significantly smooth the path to successful scaling.

EPSD Can Help

EPSD helps companies assess, prioritize, and remediate tech debt before it impacts performance, security, and growth. If your organization is feeling the weight of technical debt, read about our approach to managing tech debt. Or, contact us today to take control.