AI Cost Analysis — Ghostfolio AI Financial Agent
Development & Testing Costs
LLM API Costs (Anthropic Claude Sonnet)
| Category |
Estimated API Calls |
Estimated Cost |
| Agent development & manual testing |
~200 queries |
~$4.00 |
| Eval suite runs (58 tests × ~8 runs) |
~464 queries |
~$8.50 |
| LLM-as-judge eval runs |
~58 queries |
~$1.00 |
| Claude Code (development assistant) |
— |
~$20.00 (Anthropic Max subscription) |
| Total development LLM spend |
~695 queries |
~$33.00 |
Token Consumption
Based on Langfuse telemetry data from production traces:
| Metric |
Per Query (avg) |
Total Development (est.) |
| Input tokens |
~2,000 |
~1,390,000 |
| Output tokens |
~200 |
~139,000 |
| Total tokens |
~2,200 |
~1,529,000 |
Typical single-tool query: ~1,800 input + 50 output (tool selection) → tool executes → ~2,300 input + 340 output (synthesis). Total: ~4,490 tokens across 2 LLM calls.
Observability Tool Costs
| Tool |
Cost |
| Langfuse Cloud (free tier) |
$0.00 |
| Railway hosting (Hobby plan) |
~$5.00/month |
| Railway PostgreSQL |
Included |
| Railway Redis |
Included |
| Total infrastructure |
~$5.00/month |
Total Development Cost
| Item |
Cost |
| LLM API (Anthropic) |
~$33.00 |
| Infrastructure (Railway, 1 week) |
~$1.25 |
| Observability (Langfuse free tier) |
$0.00 |
| Total |
~$34.25 |
Production Cost Projections
Assumptions
- Queries per user per day: 5 (portfolio check, performance review, a few follow-ups)
- Average tokens per query: 4,490 (2 LLM calls: tool selection + synthesis)
- Input: ~4,100 tokens (system prompt + tools + conversation + tool results)
- Output: ~390 tokens (tool call + response text)
- Average tool calls per query: 1.5
- LLM model: Claude Haiku 3.5 ($0.80/M input tokens, $4/M output tokens)
- Average cost per query: ~$0.005 (validated by Langfuse production traces after model switch)
- Verification overhead: Negligible (string matching, no additional LLM calls)
- Days per month: 30
Cost Per Query Breakdown
| Component |
Tokens |
Cost |
| LLM Call 1 (tool selection) |
1,758 in + 53 out |
$0.0016 |
| Tool execution |
0 (database queries only) |
$0.000 |
| LLM Call 2 (synthesis) |
2,289 in + 339 out |
$0.0032 |
| Total per query |
~4,490 |
~$0.005 |
Monthly Projections
| Scale |
Users |
Queries/day |
Queries/month |
Monthly LLM Cost |
Infrastructure |
Total/month |
| Small |
100 |
500 |
15,000 |
$75 |
$20 |
$95 |
| Medium |
1,000 |
5,000 |
150,000 |
$750 |
$50 |
$800 |
| Large |
10,000 |
50,000 |
1,500,000 |
$7,500 |
$200 |
$7,700 |
| Enterprise |
100,000 |
500,000 |
15,000,000 |
$75,000 |
$2,000 |
$77,000 |
Cost per User per Month
| Scale |
Cost/user/month |
| 100 users |
$0.95 |
| 1,000 users |
$0.80 |
| 10,000 users |
$0.77 |
| 100,000 users |
$0.77 |
Cost per user is nearly flat because LLM API costs dominate and scale linearly. Infrastructure becomes negligible at scale. The switch from Sonnet to Haiku reduced per-query costs by ~70% while maintaining 100% eval pass rate.
Cost Optimization Strategies
Implemented:
- Switched from Sonnet to Haiku 3.5 — 70% cost reduction with no eval quality loss
- Tool results are structured and minimal (only relevant fields returned to LLM, not raw API responses)
- System prompt is concise (~500 tokens) to minimize per-query overhead
- Single-agent architecture avoids multi-agent token multiplication
- Streaming responses reduce perceived latency
Recommended for production:
| Strategy |
Estimated Savings |
Complexity |
| Response caching (same portfolio, same question within 5 min) |
20-40% |
Low |
| Prompt compression (shorter tool descriptions) |
10-15% |
Low |
| Batch token optimization (combine related tool results) |
5-10% |
Medium |
| Switch to open-source model (Llama 3 via OpenRouter) |
50-70% |
Low (provider swap) |
Most impactful: Adding response caching could reduce costs by 20-40%, bringing the 10,000-user scenario from $7,700 to ~$4,500-6,000/month.
Key Insight
At $0.005 per query and 5 queries/user/day, the per-user cost of under $1/month is extremely affordable for a premium feature. For Ghostfolio's self-hosted model where users provide their own API keys, this cost is negligible — roughly the price of a single coffee every three months for conversational access to portfolio analytics.
