30/60/90 Startup Scaling Plan
30/60/90 Startup Scaling Plan
This is a sample deliverable format showing the structure and specificity of a typical 30/60/90 engagement. It is NDA-safe by design.
Client profile: [Redacted] C‑Corp
Seed to Series A product team with early traction, aiming to scale from prototype to production while controlling infra costs and reducing security risk.
Primary objective: Ship a production-grade system and weekly execution cadence that scales cleanly from 0 → 100K MAU without emergency migrations.
Inputs needed (minimal)
- Access to staging logs/metrics (or sanitized exports)
- DB schema snapshot or ERD (even rough)
- Top endpoints by traffic
- Monthly spend breakdown by category
- Threat model constraints (payments, uploads, OAuth, webhooks, admin)
Executive summary
The constraint we are solving
- Hot-path latency (no instrumentation)
- Cost spikes (runaway queries, egress, invocations)
- Data model debt (analytics and discovery become guesswork)
- Auth/session risk (OAuth edge cases, cookie flags, CSRF)
- Shipping stalls (no repeatable weekly process)
90-day outcomes (what done looks like)
- Stable foundation: Postgres system of record, clear boundaries, predictable deploys
- Cost governance loop: drivers identified, budgets, optimizations with measured ROI
- Discovery inputs: event schema plus aggregates for ranking and experimentation
- Security baseline: hardened auth/session surfaces with regression checks
- Operator cadence: weekly plan → ship → review loop with measurable progress
What you actually get
- Constraint map plus 30-day action plan (top levers with evidence)
- System map (current → target) (services, boundaries, data flow)
- Backlog plus milestones (scoped phases and sequencing)
- Cost driver report (top spend categories plus mitigation plan)
- Security baseline (auth, OAuth, sessions, abuse controls)
- Delivery estimate ranges (so leadership can plan)
01
Days 0 to 7: Intake and baseline
Goal: establish reality (what is fragile, expensive, and risky) and define success metrics.
- Architecture inventory and hot paths
- Baseline p95 latency, error rates, spend drivers, security surfaces
- Decide guardrails (speed vs cost vs quality)
02
Days 1 to 30: Stabilize and make it measurable
- Observability foundation (logs, metrics, alerts)
- Data model sanity and migration discipline
- Cost governance loop and quick wins
- Security baseline hardening (auth, redirects, rate limits)
- Weekly shipping cadence with decision log
03
Days 31 to 60: Build the growth-ready core
- Events and analytics pipeline (lightweight and real)
- Discovery v1 (heuristics first, measurable outcomes)
- Release process, rollback flow, runbooks
04
Days 61 to 90: Scale, de-risk, automate
- Load testing and capacity planning
- Budgets and cost per active user model
- Security regression workflow
- Documentation that gets used (debug map, runbooks)