Context View
Actors, external systems, and primary value exchanges
- Clarifies boundaries and trust levels
- Surfaces regulatory/data residency edges
- Aligns API vs event responsibilities
software-development18 min read
Custom Application Architecture Planning
A practical, outcome-first guide to plan a custom application architecture—aligning business goals to technical constraints, selecting fit-for-purpose patterns, defining SLOs and budgets, modeling data and integration, building AI readiness and guardrails, and designing rollout and resilience without over-engineering.
By Solution Engineering Team
Summary
Define architecture from outcomes backward: clarify business goals and constraints, choose patterns that fit the use case and team, set SLOs and budgets (latency, error, cost), design the data model and integration contracts, prepare for AI use cases with evaluation and guardrails, and plan a reversible rollout.
Architectural Goals and Constraints
| Goal/Constraint | Signals | Design Implications |
|---|---|---|
| Business Outcome | Conversion ↑, cycle time ↓, compliance-ready | Align patterns to outcomes; instrument KPIs early |
| Latency Budget (P95) | UI interactions ≤ 200-400ms; API SLOs | Caching, async queues, back-pressure, read models |
| Error Budget | Monthly error budget ≤ X% | Retry policies, circuit breakers, idempotency keys |
| Cost/Unit Budget | $ per request/user/job | Right-sizing, autoscale, batching, cold-start mitigation |
| Compliance/Security | PII, residency, audit trails | Data zones, encryption, RBAC/ABAC, logging standards |
| Change Velocity | Daily deploys, feature flags | Trunk-based dev, progressive delivery, canaries |
Architecture Blueprint: Views That Matter
Logical View
Domains, services/modules, key flows
- Keeps modular monolith vs services honest
- Highlights cohesion/coupling trade-offs
- Supports clear ownership
Data View
Domains, canonical models, events, and contracts
- Prevents schema sprawl
- Enables change-friendly evolution
- Improves analytics and governance
Deployment/Runtime
Runtime topology, networking, scaling, storage
- Makes latency and failure modes visible
- Simplifies capacity planning
- Supports cost-aware design
Operational/Observability
Metrics, logs, traces, SLOs, runbooks, alerts
- Faster detection/MTTR
- SLO-driven decisions
- Audit-ready operations
Security/Trust Boundaries
AuthN/SSO, AuthZ model, secrets, data flows
- Limits blast radius
- Meets compliance requirements
- Supports zero-trust posture
Non-Functional Requirements (SLOs) and Budgets
| Dimension | Target/Budget | Notes |
|---|---|---|
| Availability | 99.9% monthly or higher | Define maintenance windows; multi-AZ; graceful degradation |
| Latency (API P95) | ≤ 300ms (read), ≤ 800ms (write) | Cache reads; queue long writes; avoid N+1 calls |
| Throughput | XX RPS peak with 2x headroom | Autoscale policy; connection pooling; back-pressure |
| Error Budget | ≤ 1% monthly | Error budget policy and rollback triggers |
| Cost/Unit | $X per 1k requests/jobs | Token/GPU if AI, egress, storage IOPS included |
| RPO/RTO | RPO ≤ 5m, RTO ≤ 15m | Backups, PITR, tested restore and failover |
Data Architecture and Integration
| Topic | Good Practice | Trade-Offs |
|---|---|---|
| Domain Modeling | Canonical models per domain; anti-corruption layers | More upfront modeling vs reduced coupling |
| Events | Outbox pattern; versioned events; idempotent consumers | Eventual consistency; consumer complexity |
| APIs | Stable contracts, pagination, filtering, retries, timeouts | Versioning overhead vs client stability |
| Migrations | Online schema changes, feature flags, double-write/reads | Temporary duplication; cleanup discipline |
| Multi-Tenancy | Row-level isolation or schema-per-tenant; keyed encryption | Ops overhead vs simpler isolation |
| Analytics | Event → warehouse/lakehouse; metrics layer | ETL/ELT ownership and freshness SLAs |
AI Readiness and Safety
Retrieval (RAG)
Vector store, embeddings, chunking, metadata access control
- Cited, auditable answers
- Reduced hallucinations
- Privacy-preserving retrieval
Evaluation
Task-specific evals, safety, toxicity, prompt-injection tests
- Quality over anecdotes
- Release gates with evidence
- Detection of drift
Cost/Latency Budgets
Tokens/GPU forecasts; caching/batching; model selection
- Predictable spend
- SLA-aligned prompts
- Fallbacks under load
Governance & Logging
Prompt/response logging, retention, RBAC, redaction
- Audit-ready usage
- Incident forensics
- Safer scale-up
Scalability and Performance Patterns
| Pattern | Signals It Helps | Trade-Offs |
|---|---|---|
| Caching (CDN/app/db) | High read latency; repeated queries | Stale data; cache invalidation complexity |
| CQRS/Read Models | Complex queries on hot path; reports | Sync complexity; eventual consistency |
| Async Work Queues | Spiky writes; slow IO; external calls | Ordering and idempotency concerns |
| Sharding/Partitioning | Single-node limits; data hotspots | Routing logic; rebalancing effort |
| Connection Pooling | DB saturation; high concurrency | Tuning required; pool starvation risks |
| Back-Pressure | Downstream saturation; timeouts | Delayed responses; shed load design |
Rollout, Change, and Resilience
From spike to safe production
Discovery Spike
Thin slice across FE/API/DB; document risks and budgets
- Spike repo/branch
- Risk notes
MVP Behind Flags
Feature flags; trunk-based; progressive delivery
- Flag strategy
- Rollback plan
Observability Baseline
Metrics/logs/traces; dashboards and SLOs
- Dashboards
- Runbooks
Failure Drills
Chaos testing; dependency timeouts; load and soak
- Drill reports
- Fix backlog
Canary & Ramp
Small cohort; watch error and latency budgets
- Canary results
- Go/No-Go
Learning & Hardening
Post-incident reviews; tune SLOs and autoscale
- PIRs
- SLO updates
Anti-Patterns to Avoid
Microservices-by-Default
Implementing microservices without scaling needs or strong boundaries
- Unnecessary complexity
- Increased operational overhead
- Slower development velocity
Big-Bang Schema Changes
Major schema changes without flags, dual-writes, or fallbacks
- High risk deployments
- Extended downtime risk
- Complex rollback scenarios
Chatty Service Patterns
N+1 remote calls on hot paths without optimization
- Poor performance
- Increased latency
- Resource inefficiency
Cloud Default Assumptions
Assuming vendor defaults meet specific SLOs and budgets
- Cost overruns
- Performance issues
- Security gaps
Late-Stage Security
Deferring threat modeling and observability until late in development
- Security vulnerabilities
- Compliance issues
- Expensive rework
Unbounded AI Usage
AI implementations without evals, guardrails, or token budgets
- Cost surprises
- Quality issues
- Security risks
Prerequisites
- Clear product outcomes, constraints, and success metrics (SLOs/SLAs)
- Data classification and basic compliance posture (incl. PII handling)
- Team capacity and ownership for domains, ops, and security
- Agreement on AI/data guardrails, logging, and retention
References & Sources
- C4 Model for Visualising Software Architecture— Simple and effective approach to software architecture documentation
- Google SRE: SLOs and Error Budgets— Comprehensive guide to service level objectives and reliability engineering
- OWASP Application Security Verification Standard— Security verification standards for application development
- NIST Secure Software Development Framework— Security guidelines for software development and architecture
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