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2.6 KiB

AI Cost Analysis

Date: 2026-02-23
Project: Ghostfolio Finance Agent MVP
Scope: development, testing, and monthly production projections

Pricing Inputs

Primary model routing in MVP:

  • 80% glm-5 (Z.AI)
  • 20% MiniMax-M2.5 (MiniMax)

Reference pricing:

Blended effective rates used for projections:

  • Input: $0.86 / 1M (0.8 * 1.00 + 0.2 * 0.30)
  • Output: $2.80 / 1M (0.8 * 3.20 + 0.2 * 1.20)

Development and Testing Costs

Measured local verification for this MVP used mocked model calls in Jest suites:

  • npm run test:ai uses mocked generateText
  • npm run test:mvp-eval uses mocked generateText

Direct external LLM calls from automated tests:

  • API calls: 0
  • Input tokens: 0
  • Output tokens: 0
  • Cost: $0.00

Manual smoke estimate for development sessions:

  • Assumed live calls: 40
  • Average tokens per call: 2400 input, 700 output
  • Total estimated tokens: 96,000 input, 28,000 output
  • Estimated model cost:
    96,000/1,000,000 * 0.86 + 28,000/1,000,000 * 2.80 = $0.16

Observability cost:

  • LangSmith tracing integration: implemented (optional env-gated), current spend in this repository phase: $0.00

Production Cost Projections

Assumptions:

  • Average AI queries per user per month: 30 (about 1/day)
  • Average tokens per query: 2400 input, 700 output
  • Tool call frequency: 1.5 tool calls/query (portfolio + risk/market mix)
  • Verification overhead and retry buffer: 25%
  • Effective cost/query before buffer: $0.004024

Monthly projection:

Users Queries / Month Model Cost / Month With 25% Buffer
100 3,000 $12.07 $15.09
1,000 30,000 $120.72 $150.90
10,000 300,000 $1,207.20 $1,509.00
100,000 3,000,000 $12,072.00 $15,090.00

Sensitivity Range (Model Mix)

Same token assumptions, model-only monthly cost (without 25% buffer):

  • 100 users:
    • all MiniMax-M2.5: $4.68
    • all glm-5: $13.92
  • 100,000 users:
    • all MiniMax-M2.5: $4,680
    • all glm-5: $13,920

Instrumentation Plan for Exact Tracking

  1. Add per-request token usage logging at provider response level.
  2. Keep LangSmith traces enabled in staging for request, tool-call, and verification spans.
  3. Export weekly token and cost aggregates into a versioned cost ledger.
  4. Set alert thresholds for cost/query drift and high retry rates.