Integrating LLMs into applications: costs from MVP to enterprise

Integrating a large language model (LLM) into an application has become more accessible than ever, but the question that troubles many SME owners is not 'can it be done' but 'how much does it cost'. LLM integration costs can range from a few million VND per month for a simple MVP chatbot to hundreds of millions of VND when operating at enterprise scale with millions of queries.

This gap does not come from the model price alone, but from how you design the architecture, choose the model and control token consumption. This article breaks down the cost components, analyzes the differences between MVP and enterprise, and points out common mistakes that make bills balloon many times over the initial estimate.

The components that make up LLM integration cost

LLM integration cost is not just the money paid for the API each month. It is the sum of many layers, and each layer can swell if you don't control it from the start. Understanding each component helps you estimate a budget closer to reality.

• API token cost: charged by the number of input and output tokens per million tokens, with a very large gap between models. Cheap models like Gemini Flash fall around 0.5 USD input and 3 USD output, or DeepSeek at only about 0.14 USD input and 0.28 USD output. Mid-tier models like Claude Sonnet run around 3 USD input and 15 USD output, and Gemini Pro around 2 USD input and 12 USD output. High-end models like GPT-4 or Opus can reach around 5 to 30 USD per million tokens.
• Operating infrastructure: servers, vector databases, processing queues and monitoring, typically from 3 to 30 million VND per month.
• RAG or fine-tuning techniques: the cost of embeddings, storage and retraining the model on your own data.
• Evaluation and guardrails: systems for moderating output, preventing data leakage and measuring answer quality.

Cost at the MVP stage

At the MVP stage, the goal is to prove value quickly with a minimal budget. You usually use the API of an existing model, add a layer of prompt engineering and perhaps a lightweight RAG over a few hundred documents. The actual total operating cost for an internal chatbot or a question-answering assistant typically falls around 5 to 20 million VND per month.

This figure assumes traffic of a few thousand to a few tens of thousands of queries. At that scale, most of the cost comes from API tokens, while the infrastructure can leverage serverless services to keep it minimal. The mistake to avoid is investing in fine-tuning too early, before you have enough real data to know what the model needs to improve.

Cost at the enterprise stage

When scaling to enterprise, the problem changes completely. Query volume can reach millions per month, along with requirements for low latency, high availability and security compliance. At this point cost is no longer linear with tokens but is dominated by redundant infrastructure, the operations team and quality control layers.

A serious enterprise system typically costs from 100 to 500 million VND per month, not counting MLOps personnel costs. The key point is that at this scale, using a small self-hosted model for simple tasks and only calling the large model when truly needed can save up to half the token budget.

Choose a model just big enough rather than the largest

A costly misconception is always choosing the most powerful model for every task. In reality, most needs such as classification, information extraction or answering simple questions are handled well by small models at a fraction of the price.

An effective strategy is tiering: use a small model as the default processing layer, and only switch to the large model when a complex query exceeds a threshold. This difficulty-based routing approach typically cuts token costs by 40 to 60 percent while still keeping the quality users perceive.

Optimizing token cost and latency

Tokens are the billing unit, so every token optimization directly reduces the bill. Trimming the system prompt, compressing context and removing irrelevant documents in RAG are simple but effective levers.

• Caching answers for repeated queries to avoid calling the model multiple times for the same question.
• Prompt caching for the repeated system prompt portion can save around 50 to 90 percent of the cost for the fixed context sent each time.
• Compressing context and including only the document passages that are truly relevant instead of stuffing in everything.
• Setting a reasonable output token limit to avoid unnecessarily long-winded answers.
• Using streaming to improve perceived latency without incurring additional cost.

Mistakes that make costs balloon

Many projects start smoothly but quickly go over budget because of poorly calculated decisions. Recognizing these mistakes early helps you keep costs under control as the application scales.

• Calling the large model for every task, even things a small model can easily handle.
• Stuffing all documents into the context instead of selective retrieval, causing input tokens to spike.
• Skipping caching and monitoring, letting duplicate queries silently burn the budget.
• Fine-tuning too early when prompt engineering and RAG already meet the need.

The roadmap from prototype to production

A reasonable roadmap starts with a prototype using an existing model API to validate value, then adds RAG when your own data is needed, and only then considers model routing and infrastructure optimization as traffic grows. Jumping straight to a complex architecture before you have real users almost always leads to waste.

At Tekmium, we usually advise SME clients to go step by step: measure the actual cost at each stage, then only invest more when the numbers prove the need. This approach keeps the AI budget tied to business value rather than chasing technology.

Conclusion

LLM integration cost spans from a few million VND for an MVP to hundreds of millions of VND for enterprise, and most of that gap is determined by design choices rather than the model itself. Choosing a model just big enough, controlling tokens, caching and measuring continuously are the principles that help you scale without blowing your budget. If your business is considering bringing an LLM into your product, Tekmium's AI development services are ready to accompany you in outlining a roadmap that fits your scale and budget. Get in touch with our team to map out the right approach for your product.