ADR-06: PaaS-First Infrastructure Strategy
| Date | Author | Repos |
|---|---|---|
| 2024-12-17 | @KubrickCode | All |
Context
Problem Statement
Every software team faces a fundamental infrastructure decision: how much operational responsibility to take on versus delegating to managed platforms. This decision significantly impacts:
- Resource Allocation: Time spent on infrastructure vs. product development
- Skill Requirements: DevOps expertise needed within the team
- Cost Structure: Operational costs vs. platform fees
- Architectural Flexibility: Customization capabilities vs. platform constraints
The Infrastructure Spectrum
| Approach | Operational Burden | Flexibility | Cost at Scale |
|---|---|---|---|
| Bare Metal | Very High | Maximum | Lowest |
| IaaS (VMs) | High | High | Low |
| Kubernetes | Medium-High | High | Medium |
| PaaS (Managed) | Low | Limited | Higher |
| Serverless | Minimal | Most Limited | Variable |
Key Decision Factors
- Team Size and Composition: Available DevOps expertise
- Product Maturity: Stability of requirements and architecture
- Traffic Patterns: Predictability and scale of workload
- Time Constraints: Speed to market requirements
- Budget Model: CapEx vs. OpEx preferences
Core Question
For teams without dedicated infrastructure personnel, how should infrastructure strategy be approached to maximize product development velocity while maintaining operational reliability?
Decision
Adopt a PaaS-first infrastructure strategy, prioritizing managed services over self-managed infrastructure.
Core principles:
- Minimize Operational Overhead: Delegate infrastructure management to platform providers
- Focus on Product: Allocate engineering time to product features, not infrastructure
- Accept Trade-offs: Acknowledge higher per-unit costs in exchange for reduced complexity
- Maintain Portability: Use containerized workloads to preserve migration options
Options Considered
Option A: PaaS / Managed Services (Selected)
Deploy services on managed platforms that handle infrastructure operations.
Pros:
- Minimal Operations: No server patching, OS updates, or infrastructure maintenance
- Rapid Deployment: Git-push deployment workflows, instant rollbacks
- Built-in Features: Integrated logging, monitoring, SSL certificates, CDN
- Automatic Scaling: Platform handles traffic spikes without manual intervention
- Developer Experience: Simple mental model, low cognitive overhead
- Predictable Costs: Clear pricing models, no surprise infrastructure bills
Cons:
- Higher Per-Unit Cost: Premium for managed services vs. raw compute
- Platform Constraints: Limited customization, fixed runtime environments
- Vendor Lock-in Risk: Platform-specific features create switching costs
- Limited Control: Cannot optimize at infrastructure level
- Egress Costs: Data transfer fees can accumulate
- Feature Limitations: Advanced networking, custom kernels, etc. unavailable
Option B: IaaS Direct Management
Provision virtual machines on cloud providers and manage infrastructure directly.
Pros:
- Full Control: Complete customization of OS, networking, security
- Cost Efficiency: Lower per-unit costs, especially at scale
- No Platform Constraints: Any software stack, any configuration
- Optimization Potential: Fine-tune for specific workload characteristics
- Multi-Cloud Ready: Standard VMs portable across providers
Cons:
- Operational Burden: Patching, monitoring, security, backups, scaling
- Expertise Required: Requires dedicated DevOps knowledge
- Slower Iteration: More setup and maintenance overhead
- Security Responsibility: Full responsibility for hardening and compliance
- Capacity Planning: Must predict and provision resources in advance
Option C: Kubernetes (Managed or Self-Managed)
Deploy containerized workloads on Kubernetes clusters.
Pros:
- Orchestration Power: Advanced scheduling, self-healing, rolling updates
- Portability: Same manifests work across any Kubernetes cluster
- Ecosystem: Rich tooling for service mesh, observability, GitOps
- Scale Efficiency: Optimal bin-packing of workloads
- Industry Standard: Wide adoption, large talent pool
Cons:
- Complexity: Steep learning curve, many moving parts
- Operational Overhead: Even managed Kubernetes requires significant expertise
- Resource Overhead: Control plane costs, minimum cluster size requirements
- Overkill for Small Scale: Benefits don't materialize until significant scale
- YAML Sprawl: Configuration complexity grows with cluster size
Consequences
Positive
Maximized Product Focus
- Engineering time directed at features, not infrastructure
- Faster iteration cycles without operational distractions
- Reduced context switching between development and operations
Lower Barrier to Entry
- No DevOps expertise required to start
- New team members productive immediately
- Documentation and support readily available
Operational Reliability
- Platform provider handles availability and redundancy
- Automatic failover and recovery
- Professional-grade infrastructure without professional infrastructure team
Predictable Velocity
- Deployment complexity eliminated
- Consistent environments across staging and production
- Built-in CI/CD integration
Negative
Cost Premium
- Higher per-resource costs than raw infrastructure
- Costs scale linearly with usage (no volume discounts)
- Mitigation: Monitor usage, optimize application efficiency, evaluate at scale thresholds
Vendor Lock-in
- Platform-specific features create migration friction
- Pricing changes can impact budget unexpectedly
- Mitigation: Containerize workloads, avoid platform-specific abstractions, maintain exit strategy
Limited Customization
- Cannot tune infrastructure for specific needs
- Fixed runtime environments and constraints
- Mitigation: Choose platforms with sufficient flexibility, escalate blockers to platform providers
Architectural Constraints
- Some patterns impossible or expensive on PaaS
- Network topology limitations
- Mitigation: Design within platform constraints, evaluate alternatives for edge cases
Technical Implications
| Aspect | Implication |
|---|---|
| Deployment | Git-push workflows, platform-native CI/CD |
| Monitoring | Platform-provided observability, may need external APM |
| Scaling | Automatic horizontal scaling, vertical scaling may be limited |
| Networking | Platform-managed load balancing, limited custom networking |
| Data Persistence | Managed databases recommended, stateful workloads constrained |
| Cost Model | Usage-based pricing, predictable but potentially higher |
| Migration Path | Containerization preserves portability to other platforms |
When to Reconsider
- Infrastructure costs exceed equivalent dedicated DevOps headcount
- Platform constraints block critical feature implementation
- Traffic patterns make usage-based pricing inefficient
- Team grows to include dedicated infrastructure expertise
- Compliance requirements demand infrastructure-level control
- Performance requirements exceed platform capabilities