Case Study: How DevZero Scaled Infrastructure for Developer Environments

When DevZero approached Smirnov Labs in early 2024, they were at a critical inflection point. Their platform for cloud development environments was gaining traction, but the infrastructure was struggling to keep up with demand. They needed to scale—and fast.

This case study walks through the technical and organizational challenges we tackled together, and the results we achieved.

The Challenge

Business Context

DevZero provides cloud-based development environments that allow developers to spin up fully configured workspaces in seconds. Think “Heroku for development”—each workspace is a complete development environment with all dependencies, tools, and configurations ready to go.

The problem: As they onboarded larger enterprise customers, several issues emerged:

Performance degradation under load (>100 concurrent environments)
Security concerns around multi-tenancy and data isolation
Cost inefficiency with their current AWS architecture
Team scaling challenges as engineering grew from 8 to 15 people

The stakes: They had major enterprise deals in the pipeline, but couldn’t sign them without proving their platform could handle enterprise scale and security requirements.

Technical Landscape

Existing architecture:

Kubernetes on AWS EKS
Monolithic application handling all concerns
Single PostgreSQL database
Ad-hoc monitoring and alerting
Manual scaling decisions

Pain points:

Environment provisioning took 60-90 seconds (target: <10 seconds)
No clear resource isolation between customers
Database becoming a bottleneck
No disaster recovery strategy
Difficult to debug production issues

The Engagement

Phase 1: Assessment (Week 1-2)

Before proposing solutions, I needed to understand the full picture. We conducted:

Technical Deep Dive:

Architecture review and documentation
Performance profiling under load
Security audit of multi-tenancy implementation
Cost analysis of AWS infrastructure
Code review of critical paths

Team Assessment:

Individual conversations with engineers
Understanding of team structure and communication patterns
Identification of knowledge gaps
Review of development processes

Key Findings:

The monolith was doing too much—provisioning, orchestration, billing, and user management all coupled together
No caching layer, causing repeated expensive operations
Lack of observability made debugging nearly impossible
Engineers were firefighting instead of building features
No clear ownership of infrastructure vs. product

Phase 2: Architecture Redesign (Week 3-4)

Based on the assessment, we designed a new architecture addressing the core issues:

High-Level Design:

┌─────────────────────────────────────────────────────┐
│                  API Gateway (Kong)                  │
└─────────┬──────────────────────────────┬────────────┘
          │                              │
          ▼                              ▼
┌──────────────────┐          ┌────────────────────┐
│  Auth Service    │          │  Billing Service   │
│  (JWT + RBAC)    │          │  (Stripe)          │
└──────────────────┘          └────────────────────┘
          │
          ▼
┌───────────────────────────────────────────────────┐
│         Provisioning Orchestrator                  │
│         (Event-driven, Queue-based)                │
└───────────┬───────────────────────────────────────┘
            │
            ├──────┬──────────┬──────────┬──────────┤
            ▼      ▼          ▼          ▼          ▼
        ┌─────┐ ┌─────┐  ┌─────┐  ┌─────┐   ┌─────┐
        │Pool │ │Pool │  │Pool │  │Pool │   │Pool │
        │ Mgr │ │ Mgr │  │ Mgr │  │ Mgr │   │ Mgr │
        └─────┘ └─────┘  └─────┘  └─────┘   └─────┘
            │
            ▼
  ┌────────────────────────────────┐
  │   Kubernetes Worker Nodes       │
  │   (Isolated Namespaces)         │
  └────────────────────────────────┘

Key Architectural Decisions:

1. Environment Pool Management

Instead of provisioning environments on-demand (60-90s), we implemented a pool system:

Pre-warmed environments sit idle in pools
User requests pull from pool (1-3 seconds)
Background workers replenish pools
Different pool sizes for different tiers

Impact: Provisioning time dropped from 60-90s to 3-5s (average).

2. Event-Driven Orchestration

Replaced synchronous provisioning with event-driven architecture:

User request → Event published → Workers process
Decoupled provisioning from API requests
Better error handling and retries
Easy to add new provisioning steps

3. Enhanced Security & Isolation

Implemented multiple layers of isolation:

Kubernetes namespaces per customer
Network policies for traffic isolation
Resource quotas and limits
Secrets management via AWS Secrets Manager
Audit logging for compliance

4. Observability Stack

Built comprehensive monitoring:

OpenTelemetry for distributed tracing
Prometheus + Grafana for metrics
ELK stack for centralized logging
PagerDuty integration for alerts

Phase 3: Implementation (Week 5-12)

Rather than a big-bang rewrite, we used the strangler fig pattern:

Sprint 1-2: Foundation

Set up observability stack
Implement API gateway
Add distributed tracing to existing system

Sprint 3-4: Pool System

Build environment pool manager
Implement background workers
Test under load

Sprint 5-6: Orchestration Refactor

Extract provisioning orchestrator
Migrate to event-driven model
Run both systems in parallel

Sprint 7-8: Security Hardening

Implement network policies
Add resource quotas
Security audit and penetration testing

Sprint 9-10: Optimization

Performance tuning
Cost optimization
Load testing

Sprint 11-12: Documentation & Handoff

Architecture documentation
Runbooks for operations
Team training

The Results

Technical Wins

Performance:

Environment provisioning: 60-90s → 3-5s (94% improvement)
Concurrent environments supported: 100 → 1000+
API response time: p95 of 2s → 200ms
System uptime: 99.2% → 99.9%

Cost Efficiency:

40% reduction in AWS compute costs (better resource utilization)
60% reduction in database costs (read replicas + caching)
Overall infrastructure cost per environment: 70% reduction

Security:

Passed SOC 2 Type II audit
Achieved tenant isolation standards for enterprise customers
Implemented comprehensive audit logging

Business Impact

Customer Success:

Signed 3 major enterprise deals (>$500K ARR each)
Customer-reported issues dropped by 80%
Net Promoter Score increased from 42 → 73

Team Velocity:

Deployment frequency: weekly → multiple times daily
Mean time to recovery: 4 hours → 20 minutes
Engineer satisfaction scores improved significantly

Company Growth:

Successfully raised Series A ($15M) with solid tech foundation
Hired full-time CTO (with my help in recruitment)
Engineering team grew to 25 engineers

Key Lessons

1. Observability First

The single most impactful change was implementing comprehensive observability. You can’t fix what you can’t see. We spent the first two weeks just making the system observable, which paid dividends throughout the project.

2. Incremental Migration

The temptation to rewrite everything is strong. Resist it. Strangler fig pattern let us de-risk the migration and keep shipping features.

3. Pool-Based Architecture

Pre-warming resources is a game-changer for perceived performance. The cost of idle resources was far less than the value of instant provisioning.

4. Team Ownership

We structured services around team ownership. Each service had a clear owner, reducing coordination overhead and increasing accountability.

5. Document Everything

Architecture decision records (ADRs) were crucial for the team to understand not just what decisions were made, but why. This helped new engineers ramp up quickly.

The Transition

As the technical foundation solidified, my role shifted from hands-on implementation to strategic advisory:

Months 1-3: Heavy hands-on architecture and implementation Months 4-6: Code reviews, architecture oversight, team mentoring Months 7-9: CTO recruitment and knowledge transfer Months 10-12: Advisory role, periodic check-ins

Today, DevZero has a full-time CTO and a strong engineering team. I maintain an advisory relationship, checking in quarterly and helping with major technical decisions.

This is exactly how fractional CTO engagements should work—build the foundation, empower the team, transition gracefully.

Conclusion

DevZero’s transformation wasn’t just about technology—it was about building a scalable foundation for the business. The architectural improvements enabled them to:

Close enterprise deals they couldn’t before
Scale their team effectively
Raise capital with confidence
Build a sustainable competitive advantage

If you’re facing similar scaling challenges—whether it’s performance, security, or team growth—you don’t have to figure it out alone.

Let’s talk about your specific situation and how fractional CTO services can help you scale successfully.

Note: All metrics and details shared with DevZero’s permission. Some technical specifics omitted for confidentiality.