The Real Cost of LLMs in Production: A Model Comparison for Finished Products

Adam Dugan • February 09, 2026

Building an AI product is easy. Making it profitable is hard.

I've shipped multiple AI products: BalancingIQ (financial advisory platform), Handyman AI (image-based repair planning), and AI Administrative Assistant (voice-enabled phone system). Every single one hit the same wall: LLM costs can destroy your margins if you're not careful.

Here's what AI actually costs in production, broken down by real usage patterns, and how to make it economically viable.

The Models: Pricing Breakdown (January 2026)

First, let's establish the baseline costs. All prices are per 1 million tokens.

ModelInputOutputBest For
GPT-4o$2.50$10.00Complex reasoning, multi-step tasks
GPT-4o-mini$0.15$0.60Simple tasks, high volume
GPT-4 Turbo$10.00$30.00Highest quality, low latency
Claude 3.5 Sonnet$3.00$15.00Long context, code generation
Claude 3.5 Haiku$0.25$1.25Fast responses, simple queries
GPT-3.5 Turbo$0.50$1.50Legacy, being phased out

Key insight: GPT-4o-mini is 17x cheaper than GPT-4 Turbo for input, and 50x cheaper for output. That difference compounds fast.

Real-World Cost Examples from Production Systems

Example 1: BalancingIQ Financial Advisory

BalancingIQ analyzes financial data from Xero/QuickBooks and generates actionable insights for SMBs.

Typical workflow:

Token usage per analysis:

Cost Per User Session:

ModelInitialFollow-upsTotal
GPT-4 Turbo$0.125$0.162$0.287
GPT-4o$0.035$0.046$0.081
GPT-4o-mini$0.002$0.003$0.005

Reality check: If a user pays $50/month and uses the product 10 times, GPT-4 Turbo costs $2.87 (5.7% of revenue). GPT-4o-mini costs $0.05 (0.1% of revenue).

What I actually use: GPT-4o for complex analyses, GPT-4o-mini for simple queries and follow-ups. Average cost per session: ~$0.03.

Example 2: Handyman AI (Image Analysis)

Handyman AI analyzes photos of home repairs and generates detailed repair plans, material lists, and cost estimates.

Typical workflow:

Token usage per request:

Cost Per Image Analysis:

Reality check: If users pay $5 per analysis, GPT-4 Turbo takes 3.6% of revenue. GPT-4o takes 1%. At scale (1,000 requests/month), that's $180 vs $50.

What I actually use: GPT-4o for most analyses. The quality difference from GPT-4 Turbo is minimal for this use case, and the cost savings are significant.

Example 3: AI Administrative Assistant (Voice)

Voice AI is expensive. You're paying for TTS, STT, and LLM inference, all in real-time.

Typical 5-minute call:

Total Cost Per 5-Minute Call:

Reality check: If you're running a customer support line with 500 calls/day, that's $158/day with GPT-4o-mini or $264/day with GPT-4 Turbo. Over a month: $4,740 vs $7,920.

What I actually use: GPT-4o-mini for most calls, GPT-4o for complex routing decisions or multi-step workflows. I also cache common responses (FAQs) to avoid LLM calls entirely.

Hidden Costs: Embeddings, Fine-Tuning, and Storage

LLM inference isn't the only cost. Here are the hidden expenses:

Embeddings

If you're using RAG (retrieval-augmented generation), you need embeddings for semantic search.

Example: Embedding a 100-page document (≈75K tokens) costs $0.0015 (small) or $0.01 (large). If you're indexing thousands of documents, this adds up.

Vector Database Storage

Storing embeddings requires a vector database (Pinecone, Weaviate, pgvector).

Fine-Tuning

Fine-tuning models for domain-specific tasks has upfront costs:

When it's worth it: If you need very specific behavior and prompt engineering isn't enough. But usually, better prompts + RAG is cheaper than fine-tuning.

Cost Optimization Strategies That Actually Work

1. Aggressive Caching

The cheapest LLM call is the one you don't make.

What I cache:

Impact: Caching reduces LLM calls by 40-60% in production. Use Redis with TTL for dynamic content, S3 for static responses.

2. Model Tiering

Route queries to the cheapest model that can handle them.

My Routing Strategy:

How to decide: Start with a classifier (even a simple keyword check). Route 80% of queries to the cheap model, 20% to the expensive one. Monitor quality and adjust.

3. Shorter Context Windows

Input tokens cost money. Don't send more context than you need.

Bad: Sending 50K tokens of conversation history on every request.

Good: Summarize older messages, keep only the last 5-10 turns, use embeddings to retrieve relevant context.

Impact: In BalancingIQ, I reduced input tokens by 60% by summarizing financial data instead of sending raw transactions. Quality stayed the same, costs dropped dramatically.

4. Batch Processing

For non-urgent tasks, use OpenAI's Batch API: 50% discount, 24-hour turnaround.

Good use cases:

5. Prompt Optimization

Shorter, clearer prompts = fewer tokens = lower costs.

Example: I rewrote a 1,500-token system prompt down to 600 tokens by removing redundancy and being more concise. Quality improved (clearer instructions) and costs dropped 40%.

6. Usage Limits and Guardrails

Prevent abuse and runaway costs:

When to Use Which Model: Decision Framework

Use GPT-4o-mini When:

  • Simple queries with clear inputs
  • High-volume, low-complexity tasks
  • Following instructions (not reasoning)
  • Summarization, formatting, extraction
  • You're cost-sensitive and quality is "good enough"

Use GPT-4o When:

  • Complex reasoning and multi-step logic
  • Vision tasks (image analysis)
  • Technical content generation
  • Code generation and debugging
  • Balance between cost and quality matters

Use Claude 3.5 Sonnet When:

  • Very long context windows (200K tokens)
  • Code generation (often better than GPT-4)
  • Research and analysis tasks
  • You need detailed, thoughtful responses
  • JSON parsing reliability (better adherence)

Use GPT-4 Turbo When:

  • You need the absolute best quality
  • Low latency is critical (faster than GPT-4o)
  • High-stakes decisions (legal, medical, financial)
  • Cost is not a primary concern

Real Cost Analysis: A $50/Month SaaS Product

Let's say you're building a SaaS product with a $50/month subscription. How much can you afford to spend on LLM costs?

Typical SaaS Margins:

Available for LLM costs: ~$10/month (20% of revenue)

If users make 20 requests per month at $0.05 each (GPT-4o), that's $1/month. Comfortable margin.

If users make 100 requests per month at $0.30 each (GPT-4 Turbo), that's $30/month.You're losing money on every customer.

The fix: Tier pricing based on usage, use cheaper models, or implement aggressive caching and rate limits.

Monitoring and Cost Tracking

You can't optimize what you don't measure. Here's what I track:

Tools I use:

Key Takeaways

Building an AI product and worried about costs? I'd love to hear about your cost optimization challenges, model selection, or pricing strategy. Reach out at adamdugan6@gmail.com or connect with me on LinkedIn.

LLMs were used to help with research and article structure.