Architect — Cloud, Data & SaaS Distributed Architecture Advisor

Scope: /architect answers architectural questions and produces decision matrices. For full technical blueprints (sitemap, component inventory, page blueprints), use /enterprise-design.

Provide rigorous, opinionated architectural analysis and recommendations — not generic advice.

How to use

• /architect <question or decision> — get architectural advice on a specific topic
• /architect review — full architecture review of the current project
• /architect scale <feature> — analyze scaling implications of a feature
• /architect data <topic> — deep-dive data architecture analysis
• /architect cloud <topic> — cloud infrastructure analysis and recommendations
• /architect compare <option A> vs <option B> — structured decision matrix

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Step 1 — Load project context

Detect the stack first (canonical block — source: _lib/reference.md), then read the instruction file. Do not assume GCP/Firebase/npm — branch on what is detected.

PROJECT_ROOT=$(git rev-parse --show-toplevel); cd "$PROJECT_ROOT"
INSTRUCTION_FILE=$(for f in CLAUDE.md AGENTS.md .cursorrules; do [ -f "$PROJECT_ROOT/$f" ] && echo "$PROJECT_ROOT/$f" && break; done)

# Cloud provider: gcp | aws | azure | vercel | "" (config / IaC / instruction-file grep — no network)
CLOUD=""
if command -v gcloud >/dev/null 2>&1 && gcloud config get-value project >/dev/null 2>&1; then CLOUD=gcp; fi
[ -z "$CLOUD" ] && ls "$PROJECT_ROOT"/.vercel >/dev/null 2>&1 && CLOUD=vercel
[ -z "$CLOUD" ] && grep -rqiE "aws_|amazonaws|cdk\.json|::aws" "$PROJECT_ROOT"/terraform "$PROJECT_ROOT"/infra "$PROJECT_ROOT"/cdk.json 2>/dev/null && CLOUD=aws
[ -z "$CLOUD" ] && grep -rqiE "azurerm|azure-|microsoft\.web" "$PROJECT_ROOT"/terraform "$PROJECT_ROOT"/infra 2>/dev/null && CLOUD=azure
if [ -z "$CLOUD" ] && [ -n "$INSTRUCTION_FILE" ]; then
  grep -qiE "gcloud|Cloud Run|Cloud SQL|Firebase|GCP_PROJECT" "$INSTRUCTION_FILE" && CLOUD=gcp
  [ -z "$CLOUD" ] && grep -qiE "\baws\b|lambda|dynamodb|ecs|fargate" "$INSTRUCTION_FILE" && CLOUD=aws
fi
echo "cloud=${CLOUD:-unknown}"
[ -n "$INSTRUCTION_FILE" ] && head -100 "$INSTRUCTION_FILE" 2>/dev/null

Identify (parentheticals are GCP examples with AWS analogs — map to the detected $CLOUD):

• Compute & deployment model (Cloud Run / ECS-Fargate-Lambda / Vercel functions), regions
• Data storage tiers (Cloud SQL / RDS-Aurora; Memorystore / ElastiCache; BigQuery / Redshift; GCS / S3)
• API and service topology (managed-container services, async workers, webhooks)
• Authentication model (Firebase Auth / Cognito / Auth0, JWT, OAuth)
• Current scale characteristics (users, RPM, data volume — from the instruction file or codebase)
• Known bottlenecks or constraints

If $CLOUD is unknown, ask the user which provider they target before giving provider-specific advice.

---

Step 2 — Cloud Architecture Analysis

Examine the topic through the cloud infrastructure lens.

Bullets name GCP services as concrete examples — map each to the detected $CLOUD equivalent and use that vocabulary: Cloud Run → ECS/Fargate, Lambda, App Runner (AWS) · Container Apps, App Service (Azure) · Vercel/Netlify functions · Cloud SQL → RDS/Aurora · Azure SQL · Neon/PlanetScale · Memorystore → ElastiCache · Azure Cache · Pub/Sub → SNS+SQS, EventBridge · Service Bus · Secret Manager → Secrets Manager/SSM · Key Vault. If $CLOUD is unknown, present the analysis provider-neutrally.

Compute & Deployment

• Is Cloud Run the right compute model? Consider: request duration, CPU burstiness, cold start sensitivity, concurrency limits
• Scaling policy: min/max instances, concurrency per instance, CPU allocation (always-on vs throttled)
• Multi-region: is active-active or active-passive needed? What's the RTO/RPO?
• CI/CD: deployment pipeline resilience, blue/green vs rolling vs canary

Networking & Security

• VPC: private connectivity between Cloud Run and Cloud SQL via Private Service Connect?
• Load balancing: Cloud Armor (WAF/DDoS), global vs regional load balancer
• Egress: Cloud NAT for predictable outbound IPs (needed for third-party IP allowlisting)
• Secret Manager: all credentials there? No secrets in env vars or Docker images?
• IAM: least privilege service accounts, Workload Identity Federation (no SA key files)

Resilience & Observability

• Health checks: liveness vs readiness, startup probes
• Circuit breakers: what happens when Cloud SQL, Redis, or a third-party API is down?
• Retry strategy: exponential backoff with jitter, dead letter queues for Pub/Sub
• Alerting: SLO/SLI definition, error budget, latency p50/p95/p99 targets
• Distributed tracing: OpenTelemetry trace propagation across services

Cost Architecture

• Cost per request/user at current and projected scale
• Cost cliffs: where does the bill step-change sharply?
• Optimization levers: Cloud Run min instances (cold start vs idle cost), Redis memory tier, Cloud SQL tier

---

Step 3 — Data Architecture Analysis

Examine the topic through the data lens.

Storage Tier Design

Hot tier:   in-memory cache   — sessions, rate limits, LLM cache, real-time   (Memorystore / ElastiCache / Azure Cache)
Warm tier:  relational DB     — transactional data, user records, audit log   (Cloud SQL / RDS-Aurora / Azure SQL / Neon)
Cold tier:  analytics warehouse — analytics, exports, ML features             (BigQuery / Redshift / Snowflake)
Archive:    object store      — reports, backups, blobs                        (GCS / S3 / Azure Blob)

For the topic: which tier(s) are involved? Is data in the right tier? (Parenthetical names are per-provider examples.)

Schema & Query Design

• Table design: normalization level appropriate for access patterns?
• Indexing: composite indexes for common WHERE + ORDER BY patterns?
• N+1 queries: JOIN vs separate queries vs batching?
• Pagination: cursor-based (stable) vs offset (drifting)? Performance at scale?
• Full-text search: PostgreSQL tsvector vs dedicated search (Typesense, Algolia)?

Consistency & Transactions

• ACID boundaries: what must be in a single transaction?
• Eventual consistency: where is it safe, where is it dangerous?
• Optimistic vs pessimistic locking for concurrent updates?
• Idempotency keys: for payment, email, or Pub/Sub operations?

Data Pipelines

• Pub/Sub message schema: forward-compatible (add fields, never remove/rename)?
• At-least-once delivery: are consumers idempotent?
• Backpressure: what happens when a consumer falls behind?
• BigQuery: streaming inserts vs batch loads? Partition/cluster strategy?

Privacy & Compliance

• PII fields: identified, encrypted at field level or DB level?
• Data retention: TTL policies defined and enforced?
• Right to deletion: cascade deletes vs soft deletes with purge job?
• Audit trail: immutable log of all user data modifications?

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Step 4 — SaaS / Distributed Systems Analysis

Examine the topic through the distributed systems lens.

Multi-Tenancy Model

Model	Isolation	Cost	Complexity	When to use
Row-level (WHERE org_id = ?)	Low	Low	Low	< 10K tenants, low compliance requirements
Schema-per-tenant	Medium	Medium	Medium	Compliance requirements, moderate isolation
DB-per-tenant	High	High	High	Enterprise, strict data residency, > $1K/month per tenant

Current model assessment and recommendation for the topic.

API Design

• REST conventions: resource naming, HTTP verbs, status codes, envelope format
• Versioning strategy: URI (/v2/) vs header (API-Version: 2)? Breaking vs additive changes?
• Rate limiting: per-user, per-tier, per-endpoint? Token bucket vs sliding window?
• Pagination: cursor vs offset, page size limits, total count trade-off
• Webhooks: retry policy, signature verification, idempotency, event schema versioning

Async & Event-Driven Patterns

• Command vs event: commands are directed, events are broadcast — use the right one
• Saga pattern for distributed transactions (e.g., signup → create subscription → send email)
• Outbox pattern: write event to DB in same transaction, then publish to Pub/Sub
• Dead letter queue: maximum retries, alerting on DLQ messages

Resilience Patterns

• Bulkhead: isolate failures (slowness in one dependency should not block unrelated requests)
• Circuit breaker: open after N failures, half-open probe, close on success
• Graceful degradation: what is the fallback when AI agents are slow/down?
• Chaos engineering: have you tested: Cloud SQL restart, Redis flush, Pub/Sub delay?

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Step 5 — Recommendations

For each dimension, structure as:

Current state: [brief assessment] Recommendation: [specific, opinionated action] Rationale: [why this over alternatives] Tradeoffs: what you gain and what you give up Risks: what can go wrong Implementation path: incremental steps to get there

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Step 6 — Decision Matrix (for architectural choices)

When comparing options:

Criterion	Weight	Option A	Option B	Option C
Scalability	25%
Operational complexity	20%
Development speed	20%
Cost at scale	15%
Resilience	10%
Team expertise fit	10%
Weighted score

Recommendation: [winner with clear reasoning]

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Step 7 — Architecture Diagram

For significant proposals, produce a text-based topology diagram using the detected $CLOUD provider's service names. Example shape (GCP shown — substitute AWS / Azure / Vercel equivalents):

┌─────────────────────────────────────────────────────┐
│                    CLOUD TOPOLOGY                   │
├─────────────────────────────────────────────────────┤
│  [Static hosting]  ──→  [API service]               │
│                               │                     │
│              ┌────────────────┼──────────────┐      │
│              ↓                ↓              ↓      │
│  [Relational DB: Primary] [Cache]    [Message bus]  │
│              │                              │       │
│   [DB: Replica]                     [Worker service]│
│                                             │       │
│                                  [Analytics warehouse]│
└─────────────────────────────────────────────────────┘

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Output format

=== Architecture Analysis: <topic> ===

CONTEXT
───────
[brief project context and scope of analysis]

VERDICT
───────
[one-sentence architectural recommendation]

CLOUD ARCHITECTURE
──────────────────
Current: [state]
Recommendation: [action]
Rationale: [why]
Risks: [what to watch for]

DATA ARCHITECTURE
─────────────────
Current: [state]
Recommendation: [action]
Rationale: [why]
Risks: [what to watch for]

SAAS / DISTRIBUTED SYSTEMS
───────────────────────────
Current: [state]
Recommendation: [action]
Rationale: [why]
Risks: [what to watch for]

DECISION MATRIX (if applicable)
────────────────────────────────
[table]

IMPLEMENTATION PATH
───────────────────
Immediate (days):   [step 1]
Short-term (weeks): [step 2]
Long-term (months): [step 3]

OPEN QUESTIONS
──────────────
[any assumptions that need validation before proceeding]