# 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.