What Running LLMs in Production Costs | Soamee Skip to main content
Back to blog<br>LLM AI Costs Production Infrastructure<br>What Running LLMs in Production Actually Costs (With Numbers)<br>A real breakdown of LLM production costs: tokens, infrastructure, latency, and optimization strategies with current 2025-2026 pricing data.<br>JM<br>Javier Manzano<br>CEO & Co-founder • July 30, 2026
Everyone talks about integrating AI into their products. Few talk about the bill that arrives at the end of the month.
At Soamee, we have spent two years building AI features for clients across different industries: from customer support chatbots to RAG systems for legal document processing. In that time, we have learned that the real cost of running LLMs in production has very little to do with what you imagine at first. The surprises almost always go in the wrong direction.
This is the article I wish I had when we started. Real numbers, concrete scenarios, and strategies that actually work.
Token Pricing in 2025-2026: The Table You Need
Before discussing scenarios, we need to be clear on base prices. The relevant models in production today are these:
ModelInput (per 1M tokens)Output (per 1M tokens)Max ContextNotesGPT-4o $2.50$10.00128KOpenAI’s main modelGPT-4o mini $0.15$0.60128KIdeal for simple taskso3-mini $1.10$4.40200KReasoning, slowerClaude Sonnet 4 $3.00$15.00200KBest quality/price ratioClaude Haiku 3.5 $0.80$4.00200KEconomical and fastClaude Opus 4 $15.00$75.00200KMaximum capabilityGemini 2.5 Pro $1.25$10.001MAggressive context cachingGemini 2.5 Flash $0.075$0.301MUltra-economicalLlama 3.3 70B ~$0.20-0.60~$0.20-0.60128KSelf-hosted or via Bedrock/Together<br>One important clarification: the price per token is the cost of the tokens themselves. In real production, the system prompt (which repeats on every call), conversation history, and RAG context massively inflate the input token count. This is the most common mistake in cost estimates.
Real Scenarios: What Does It Actually Cost?
Let us run the numbers for the four most common use cases.
Scenario 1: Customer Support Chatbot (1,000 queries/day)
Typical parameters:
System prompt: 500 tokens (instructions, tone, FAQ base)
Average conversation history: 800 tokens (4-5 turns)
User query: 100 tokens
Model response: 300 tokens
Total per call: ~1,400 input tokens + 300 output tokens
ModelCost/callCost/day (1K calls)Cost/monthGPT-4o$0.0064$6.40~$192GPT-4o mini$0.00039$0.39~$12Claude Sonnet 4$0.0087$8.70~$261Claude Haiku 3.5$0.0023$2.30~$69Gemini 2.5 Flash$0.000195$0.20~$6<br>For a simple chatbot at low-to-medium volume, GPT-4o mini, Claude Haiku, or Gemini Flash are perfectly valid options at minimal cost. The key question is whether the response quality is sufficient for your use case.
Scenario 2: Document Processing (500 docs/day, ~5 pages each)
Documents change the equation dramatically. An A4 page has roughly 400-500 words, which translates to around 500-600 tokens.
Parameters:
5-page document: ~2,500 input tokens
Instructions prompt: 300 tokens
Generated summary/extraction: 500 tokens
Total per document: ~2,800 input tokens + 500 output tokens
ModelCost/documentCost/day (500 docs)Cost/monthGPT-4o$0.0120$6.00~$180Claude Sonnet 4$0.0165$8.25~$248Gemini 2.5 Pro$0.0085$4.25~$128Gemini 2.5 Flash$0.00059$0.30~$9<br>For document processing where precision is critical (contracts, medical, financial documents), Claude Sonnet or GPT-4o justify their premium. For structured data extraction from simpler documents, Gemini Flash offers an unbeatable ROI.
Scenario 3: Code Assistant (200 active developers/day)
A code assistant has a very different token profile: code snippets are token-dense (code with its syntax consumes more tokens per character than natural text).
Estimated parameters per session:
Code context: 3,000 tokens
Conversation history: 1,500 tokens
System prompt: 400 tokens
Response with code: 800 tokens
Average: 4 interactions/session
Total per session: ~23,600 input tokens + 3,200 output tokens
ModelCost/sessionCost/day (200 devs)Cost/monthGPT-4o$0.091$18.20~$546Claude Sonnet 4$0.119$23.80~$714Gemini 2.5 Pro$0.062$12.40~$372<br>For code assistants in mid-size teams, costs range from $370 to $714/month depending on the model. Not outrageous, but there is significant optimization headroom with project context caching.
Scenario 4: RAG System (document base + 2,000 queries/day)
RAG systems add a layer: retrieval brings relevant chunks from a vector database that get inserted into every call’s prompt.
Parameters:
Retrieved chunks: 3 chunks × 400 tokens = 1,200 tokens
System prompt: 600 tokens
User query: 150 tokens
Generated response: 400 tokens
Total per query: ~1,950 input tokens + 400 output tokens
ModelCost/queryCost/day (2K queries)Cost/monthGPT-4o$0.0089$17.80~$534Claude Sonnet 4$0.0122$24.40~$732Gemini 2.5 Flash$0.000267$0.53~$16Llama 3.3 70B (self-hosted)~$0.0005~$1.00~$30<br>In high-volume RAG, the gap between premium and economy models starts to...