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Mesura — AI Cost & Risk Snapshot

We evaluated a representative AI SaaS architecture using GPT-4-class models under moderate growth assumptions.

Scenario Assumptions

  • • AI SaaS product with chat-based interface
  • • Average prompt size: 1,200 tokens
  • • Daily active users: 5,000 → 30,000 (6 months)
  • • Request frequency: 3–5 per user per day
  • • Model: GPT-4-class API

(All numbers are representative of a typical mid-stage AI product)

Executive Summary

  • –Total cost increases ~6.2× as usage scales 10×
  • –Break-even vs open-weight/self-hosted models occurs at ~6–8M tokens/day
  • –Vendor lock-in risk becomes structurally high after API-level coupling

Recommendation:

Short-term:Continue with API-based model for speed and reliabilityMid-term:Introduce optional multi-provider routingLong-term:Evaluate partial self-hosting to control cost exposure

Cost Projection

Volume Scaling

  • Current usage: ~500K tokens/day
  • Projected (6 mo): ~3M tokens/day
  • Projected (12 mo): ~8M tokens/day

Estimated Monthly Cost

  • Current: $1,200
  • 6 months: $7,400
  • 12 months: $19,000+

Key cost drivers: Non-linear pricing tiers, increased prompt/response size, retry overhead due to latency constraints.

Risk Analysis

Vendor Lock-in
HIGH
  • Deep API integration
  • Prompt & workflow coupling
Cost Volatility
MEDIUM
  • Pricing change exposure
  • High usage sensitivity
Performance Stability
HIGH
  • Mature infrastructure
  • Predictable latency
Overall Risk Score: 7.4 / 10

Alternatives Evaluated

1. Anthropic Claude
Comparable performance
Slightly lower volatility
2. Open-weight (self-hosted)
Lower long-term cost
Higher infra + ops burden
3. Multi-provider routing
Reduced lock-in
Increased system complexity

What This Means

At current scale, API-based models are cost-efficient and operationally simple.

However, as usage grows:

  • Cost efficiency declines non-linearly
  • Switching cost increases significantly
  • Lock-in risk compounds over time

Architecture decisions made today will directly impact cost flexibility at scale.

This is a representative scenario.

Mesura can generate a customized report based on your actual usage, traffic, and architecture.

Typical inputs:
  • Monthly active users
  • Token usage / request
  • Model selection
  • Latency / SLA requirements
Generated by Mesura Decision Engine
Measure before you commit architecture