AI demos are cheap. AI in production is not.

That prototype feature that worked beautifully in development? It's about to start costing real money, adding real latency, and failing in ways you didn't anticipate.

Here are the real costs of AI in production—the things nobody tells you until the bill arrives.

The API Bill

Let's talk numbers.

Token pricing (approximate, varies by model):

  • GPT-4: ~$30 per million input tokens, ~$60 per million output tokens
  • Claude: Similar range for comparable models
  • GPT-3.5/smaller models: ~$0.50-2 per million tokens

Seems cheap until you do the math.

Example: AI-powered search

  • Query: 100 tokens
  • Context: 2,000 tokens
  • Response: 500 tokens
  • Cost per query: ~$0.008

At 10,000 queries/day = $80/day = $2,400/month

At 100,000 queries/day = $24,000/month

Example: Document summarization

  • 10-page document: ~10,000 tokens
  • Summary output: 500 tokens
  • Cost per document: ~$0.35

Process 1,000 documents/month = $350

These add up fast. And users don't see (or pay for) this cost directly.

The Latency Tax

AI is slow compared to traditional code.

Typical API response times:

  • Simple query: 500ms - 2 seconds
  • Complex reasoning: 2-10 seconds
  • Long generation: 10+ seconds

What this means:

  • Users wait. They don't like waiting.
  • Timeouts in sync operations. You need async patterns.
  • Rate limits compound delays.

Mitigation strategies:

  • Streaming responses (show progress)
  • Async processing with notifications
  • Caching where possible
  • Smaller, faster models for latency-sensitive features

Design around latency, not despite it.

Reliability Realities

AI services go down. They rate limit. They change behavior.

What happens in production:

  • OpenAI has outages. Multiple per month.
  • Rate limits hit unexpectedly during usage spikes.
  • Model updates change output without warning.
  • Token limits get exceeded on edge cases.

Defensive practices:

  • Graceful degradation. What happens when AI is unavailable?
  • Fallback models or providers. Can you switch?
  • Error handling for every AI call. Never trust availability.
  • Timeout policies. Don't let slow calls hang indefinitely.
  • Retry logic with backoff. Transient failures are common.

Your users shouldn't know when OpenAI is having a bad day.

Quality Variance

AI output isn't consistent.

The same prompt can produce:

  • Perfect results
  • Subtly wrong results
  • Completely wrong results
  • Unexpectedly formatted results
  • Refusals or off-topic responses

Production implications:

Design your system to handle this variance. Never assume the happy path.

Hidden Infrastructure Costs

Beyond API bills:

Logging and monitoring: Every AI call should be logged. That's storage cost.

Prompt management: As you iterate, you need version control for prompts. That's tooling.

Evaluation and testing: Testing AI features is harder than testing traditional code. That's time.

Support burden: Users will have questions about AI behavior. That's support time.

Iteration cycles: AI features need continuous refinement. That's ongoing development.

The API call is the visible cost. The iceberg below is larger.

Pricing Your AI Features

How to not lose money:

Calculate cost per user action. Know exactly what each AI-powered interaction costs you.

Build margin in. If a feature costs $0.05 to run, don't charge $0.05. Build in buffer for variance and overhead.

Consider usage-based pricing. Heavy AI users should pay more. Unlimited plans can kill margins.

Gate expensive features. Don't give everyone the most expensive AI capabilities.

Monitor constantly. Usage patterns change. Costs surprise you. Watch the metrics.

Cost Optimization

Strategies that work:

Cache aggressively. Same question, same answer. Don't re-compute.

Use appropriate models. GPT-4 for everything is expensive. Match model to task.

Truncate intelligently. Don't send more context than needed.

Batch operations. Multiple small requests cost more than one larger one.

Process async. If it doesn't need to be real-time, don't make it real-time.

Consider self-hosting. At scale, local models can be cheaper.

Optimization is ongoing. What's affordable at 100 users might not be at 10,000.

The Unit Economics Reality

Before shipping AI features:

  1. Calculate cost per user per month
  2. Compare to what users pay you
  3. Build in margin for growth
  4. Plan for cost optimization
  5. Have a kill switch if costs explode

AI features are investments. They need returns.

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