Building AI Products on a Budget

You want to build with AI. GPT-4o, Claude Opus, Gemini — the names are everywhere. But the bills? They add up fast. What if you could ship quality AI products without the sticker shock?

The Reality Check

A customer-support chatbot running on GPT-4 costs $15/1M output tokens. At 200 tokens per response and 10K users/month, that's $300/month just for the model. Add infrastructure, retries, and context overhead? You're at $500+.

But here's the secret: 90% of your use cases don't need flagship models. A well-tuned Haiku or Gemini Flash handles most tasks for 1/10th the cost.

Rule 1: Right-Size Your Model

Not every task needs GPT-4. Here's a practical breakdown:

• Simple classification, sentiment, FAQs: Haiku ($0.25/1M) or Gemini Flash ($0.075/1M)
• Code generation, summarization: GPT-4o-mini ($0.15/1M) or Claude Sonnet ($3/1M)
• Complex reasoning, creative writing: GPT-4o ($2.50/1M) or Claude Opus ($15/1M)

Rule 2: Optimize Your Prompts

Every token you send costs money. Bloated prompts = wasted budget.

Before: 1,200 tokens

You are a helpful assistant. Your job is to classify customer
support tickets into one of the following categories: billing,
technical, account, feature request, or other. Please analyze
the ticket carefully and provide your best classification.

Ticket: "I can't log in to my account"

Please respond with just the category name.

After: 180 tokens (85% reduction)

Classify: billing, technical, account, feature, other

"I can't log in to my account"

Category:

Result: Same accuracy, 85% lower cost. Multiply that across 10K requests and you've saved real money.

Rule 3: Cache Aggressively

30% of API requests are duplicates. If you're not caching, you're paying 30% more than you should.

FAQs, documentation lookups, repeated translations — all cacheable. Set a 24-hour TTL and watch your API calls drop.

Rule 4: Batch When Possible

Sending 100 one-sentence requests costs more than one 100-sentence batch. OpenAI and Anthropic both support batch processing at 50% off.

• Real-time: Use standard API ($15/1M for GPT-4)
• Batch (24h turnaround): Use batch API ($7.50/1M)

If your use case tolerates delay (overnight reports, bulk classification), batch = instant 50% savings.

Rule 5: Track Everything

You can't optimize what you don't measure. Break down costs by:

• User
• Feature
• Model
• Endpoint

When you see "sentiment classifier burning $200/month," you know where to optimize.

Real-World Budget Breakdown

$500/month: 10K users, Haiku for classification, 24h cache TTL
$5K/month: 100K users, mix of Haiku + Sonnet, smart routing
$20K/month: 500K users, multi-model routing, aggressive caching

Key Takeaways

• 90% of use cases don't need flagship models — right-size to save 10x
• Optimize prompts: trim bloat, save 50-85% per request
• Cache aggressively: 30% of requests are duplicates
• Batch when delay is acceptable: instant 50% discount
• Track by user, feature, model — you can't optimize blind

Start Building Smart

We built OriginStart because we needed it. If you're frustrated with opaque API bills and zero cost controls, you're not alone. Start a free trial — see your first savings in 24 hours.