Serverless Architecture Review Playbook for Melbourne SaaS Teams

If you run a SaaS product from Melbourne (or anywhere in Australia), chances are your backend is:

• on Firebase, GCP, AWS, or a mix of the three
• described as “serverless”
• somewhere between “it works” and “I’m scared to touch it”

This playbook is how I review serverless architectures as a serverless consultant for growth‑stage SaaS teams:

• before big launches
• before major investment in new features
• when cost or reliability start to hurt

Use it as a structured checklist with your team, or as a brief for an external review.

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1. Clarify what “serverless” actually means in your stack

“Serverless” can hide a lot of complexity.

Start by listing what you’re actually running:

• Firebase:
  • Authentication
  • Firestore / Realtime Database
  • Cloud Functions
  • Hosting
• GCP:
  • Cloud Run
  • Pub/Sub
  • Cloud Tasks
  • BigQuery
• AWS:
  • Lambda
  • API Gateway
  • SQS / SNS

For each component, answer:

• What critical workloads depend on this?
• Who owns it?
• How is it deployed (scripts, CI/CD, “click‑ops”)?

If you cannot draw this on one page, your first action item is to produce a current-state diagram.

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2. Trace three core flows end‑to‑end

Trying to review the entire system at once is overwhelming.
Instead, pick three critical flows and trace them end‑to‑end:

• New user signup → first value moment
• Core revenue event (e.g. checkout, donation, upgrade)
• A key automation or integration (e.g. CRM sync, webhook processing)

For each flow:

1. List every component touched:
   • client → API / Cloud Function → queue / Pub/Sub → workers → DBs → third‑party APIs
2. Note:
   • timeouts
   • retries
   • error handling
   • observability (logs, metrics, traces)

You are looking for silent failure points:

• webhooks that can fail without retry
• queues without dead‑letter handling
• functions that depend on global state

These flows will also anchor the rest of the review.

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3. Evaluate scalability and cost together

Serverless promises:

• scale to zero
• pay only for what you use

In reality, many SaaS teams hit the opposite:

• higher‑than‑expected baseline costs
• unpredictable spikes from naive design

Key questions:

• Do you have any N+1 patterns (e.g. a function per row, per user, per record)?
• Are there centralised “God functions” doing too much work synchronously?
• Are hot code paths hitting external APIs inside tight loops?

On Firebase and GCP, good signs include:

• heavy work moved to background functions (Pub/Sub, Cloud Tasks)
• clear separation between read paths and write paths
• use of caching where appropriate (e.g. configs, small reference data)

On AWS, similar ideas apply with:

• SQS / SNS and Lambda
• step functions or workflow engines for long‑running jobs

Your goal is to identify where a 10x increase in traffic would:

• double your bill (good)
• or multiply it by 100 (bad)

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4. Review data model and tenancy

Serverless backends can quietly accumulate tenancy and data modelling debt:

• all tenants in a single Firestore collection with no clear boundaries
• ad‑hoc security rules
• per‑tenant performance that’s hard to reason about

For multi‑tenant SaaS, review:

• How do you separate tenant data?
  • Collections / tables
  • Project / instance boundaries if needed
• How is access enforced?
  • Firestore rules
  • Application code
  • Both
• Can you:
  • export data for a single tenant?
  • delete data for a single tenant?
  • move a tenant to a different region or project if required?

These questions matter for compliance, performance, and future migrations.

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5. Check reliability patterns: retries, idempotency, DLQs

Most outages in serverless systems come from unhandled partial failure:

• a webhook endpoint went down for 5 minutes
• a third‑party API rate‑limited you
• a single message poisoned a queue

Your architecture review should answer:

• Where do we use retries?
  • with what backoff?
  • with what max attempts?
• Where are operations idempotent?
  • what keys or constraints guarantee it?
• Do our queues / topics have dead‑letter queues?
  • how are they monitored?

If the answer to “how do we detect and fix stuck messages?” is “we don’t know”, you have an immediate reliability priority.

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6. Look for observability and runbooks

Serverless spreads logic across:

• functions
• queues
• scheduled jobs
• third‑party APIs

Without observability, debugging is slow and brittle.

During a review, check:

• Do we have structured logging with correlation IDs?
• Can we trace a single user action across:
  • frontend → backend → queues → workers → third‑party APIs?
• Do we have dashboards for:
  • error rates
  • latency
  • queue depth
  • function cold starts?
• Are there runbooks for:
  • common incidents
  • backfills
  • data corrections?

If not, your team is paying an invisible tax in every incident and on‑call shift.

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7. Review security and configuration management

Serverless often hides security issues in:

• permissive IAM roles
• ad‑hoc environment variables
• untracked configuration in consoles

For each environment (dev, staging, prod):

• Are secrets stored in:
  • Secret Manager / SSM / similar?
  • or .env files and console fields?
• Are IAM roles least privilege, or “admin everywhere”?
• Is there a clear change path for:
  • environment variables
  • configuration
  • feature flags?

If any configuration is changed only via the cloud console with no audit trail, mark that as risk.

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8. Turn findings into a 3–6 month roadmap

An architecture review without a roadmap is just homework.

Convert findings into:

• Quick wins (1–2 weeks)
  • add DLQs for critical queues
  • tighten timeouts and retries
  • add basic dashboards and alerts
• Medium projects (4–8 weeks)
  • refactor a hot path into background jobs
  • fix tenancy model for one major feature
  • introduce CI/CD for infrastructure
• Strategic projects (3–6 months)
  • multi‑region or multi‑cloud strategy
  • large data migrations
  • deeper LLM / AI integration

Prioritise by:

• business impact (revenue, user trust)
• risk reduction
• effort / complexity

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9. When to bring in outside help

There are good reasons to get an external architecture review, especially if:

• you inherited a complex Firebase or serverless stack
• production incidents are increasing
• cost is unpredictable
• you are about to invest heavily in new features or AI automation

An external review should:

• give you a clear, written assessment
• include diagrams and flow descriptions
• prioritise work into phases
• be scoped so your team can act on it, not just file it away

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Need a serverless architecture review for your SaaS?

If your product runs on Firebase, GCP, AWS, or a mix, and you want a second set of eyes before the next growth phase, I can help.

I work with Melbourne and Australia‑based teams on:

• serverless architecture reviews
• cost and reliability improvements
• deeper AI and automation integration on top of existing backends

Request an architecture review →