Your CEO saw a ChatGPT demo. Now you’re building “AI features.” Sound familiar?
I’ve helped multiple companies integrate LLMs into their products. The technology works. But the cost model is unlike anything most engineering teams have dealt with. Here’s what I wish someone had told me before we shipped our first LLM-powered feature.
The Real Cost Breakdown
1. Token Costs Are Just the Beginning
Yes, you’ll pay per token. At current rates, that might look cheap—a few dollars per million tokens. But watch what happens at scale:
- 1,000 users × 10 queries/day × 2,000 tokens/query = 20M tokens/day
- At $0.01/1K tokens (a middle-tier model), that’s $200/day or $6,000/month
And that’s just input tokens. Output tokens often cost 2-3x more.
2. Latency Has a Cost
LLM calls are slow—200ms to 2 seconds for a typical response. That latency affects user experience and system architecture:
- Users wait longer, reducing engagement
- You need more concurrent connections
- Timeouts and retries complicate error handling
- Background jobs pile up during peak load
3. Quality Assurance Is Expensive
Unlike deterministic code, LLMs are probabilistic. The same input might produce different outputs. Testing is hard:
- You can’t just assert on outputs
- You need human review for quality
- Edge cases are harder to catch
- Regression testing is more complex
Budget significant engineering time for evaluation frameworks.
Cost Control Strategies That Work
Caching Aggressively
Many LLM queries are repetitive. Cache at multiple levels:
User Query → Semantic Hash → Cache Lookup → LLM (if miss)A well-tuned cache can reduce LLM calls by 40-60%. That’s a 40-60% cost reduction.
Right-Size Your Models
You don’t need GPT-4 for everything. A hierarchy of models:
| Use Case | Model Choice | Why |
|---|---|---|
| Classification | Small fine-tuned model | 10x cheaper, faster |
| Summarization | Medium model (GPT-3.5 tier) | Good enough quality |
| Complex reasoning | Large model (GPT-4 tier) | Worth the cost |
| Simple Q&A | Cached responses or retrieval | Avoid LLM entirely |
Set Hard Limits
Implement per-user and per-request limits:
- Maximum tokens per request (truncate inputs if needed)
- Rate limits per user per hour
- Cost alerts when spending spikes
- Automatic fallbacks when approaching budgets
Batch When Possible
Real-time isn’t always necessary. Batch processing can:
- Use cheaper off-peak pricing (some providers offer this)
- Reduce total calls through deduplication
- Improve throughput efficiency
Monitoring You Actually Need
Standard APM won’t cut it. Track:
- Cost per user action - Not just total spend, but unit economics
- Token efficiency - Output quality per token spent
- Cache hit rate - Your best lever for cost control
- Latency percentiles - P50 and P99 both matter
- Error rates by type - Rate limits, timeouts, content filters
Build dashboards that let you correlate cost with product metrics. If a feature costs $0.50 per use but generates $5 in revenue, that’s healthy. If it costs $0.50 and users churn, that’s a problem.
The Hidden Staffing Cost
Don’t forget: someone needs to manage this. LLM systems require:
- Prompt engineering (a real skill, not just writing)
- Model evaluation and selection
- Cost optimization ongoing
- Vendor relationship management
- Keeping up with rapid model changes
Budget for at least 0.5 FTE dedicated to AI ops for any serious production deployment.
My Recommendation
Start small. Pick one feature, one model, one use case. Build your cost monitoring and control systems first—before you scale. Learn your unit economics early.
The technology is genuinely useful. But “it works in a demo” and “it works profitably at scale” are very different things.
Planning an AI integration? I help companies build realistic AI adoption strategies that account for real costs. Let’s talk.