How scaling enterprise AI with the wrong LLM could cost you

In AI text generation, a token is a small unit of text that a language model uses to read and generate language. AI services charge based on how many tokens are processed during an interaction.

 

Providers calculate these charges based on the number of input tokens you include in your prompts, but also the number of output tokens included in the model’s responses.

 

Token pricing might seem straightforward: count the tokens processed and multiply by the per-token price. But enterprise-scale LLM deployments reveal a more complex reality where tokenizer efficiency dramatically impacts total cost of ownership.

 

For example, our study found up to a 450% increase in token usage between the least and most efficient model tokenizers. That equates to a 450% increase in costs.

   

 

Consider a financial services company processing 100,000 customer inquiries daily through an AI assistant. If each interaction averages 1,000 tokens and the company pays $0.001 per token, daily costs appear manageable at $100. However, a less efficient model requiring 4.5x more tokens transforms that $100 daily expense into $450

 

That’s $36,500 annually with the efficient model versus $164,250 with the inefficient one – an extra $127,750 for the same workload.

 

 

Poor tokenizer performance becomes even more costly in multilingual environments. For languages with complex writing systems like Tamil, our study revealed that token usage could be up to 450% higher depending on the model.

 

This means an enterprise operating globally could face dramatically different costs per interaction depending on language. That’s a hidden variable that can shatter budget projections.

 

Don't evaluate models based solely on per-token pricing. Instead, benchmark actual token consumption across your specific use cases and languages before committing to any LLM provider.

 

Token consumption is reported with every API interaction, and so measuring it is easy. Some companies even provide online tools to calculate token consumption, such as OpenAI’s Tokenizer.

 

 

Imagine a global ecommerce company processing 50,000 daily customer inquiries: 25,000 in English, 20,000 in Spanish, and 5,000 in Tamil. Now, imagine that with an efficient tokenizer at $0.001 per token, each English interaction uses 800 tokens, each Spanish interaction uses 900 tokens, and each Tamil interaction uses 1,000 tokens.

 

At these rates, daily costs total $43 or $15,695 per year.

 

With a less efficient model, English and Spanish token usage increases by 70% to 1,360 and 1,530 tokens per interaction, respectively. However, Tamil spikes by 450% due to its complex writing system.

 

Daily costs jump to $87.10—doubling the budget. Instead of $15,695 annually, the company is paying $31,791.50. That’s an extra $16,096.50 for the same workload.

 

The Tamil interactions alone, despite representing just 10% of volume, drive most of this cost increase.

While accuracy metrics dominate LLM evaluation discussions, generation speed directly impacts user experience, operational efficiency, and ultimately, business outcomes. According to TrainAI’s research, Meta Llama models generated responses 3x to 10x slower than competitors, creating a hidden productivity tax that accumulates across thousands of daily interactions.

 

The business impact extends far beyond user frustration. In customer service applications, slower response times drive higher abandonment and lower satisfaction scores.

 

For internal enterprise tools, delays disrupt workflows and force employees to switch context, destroying the productivity gains AI was meant to deliver. Amazon famously discovered that every 100ms of web latency cost them 1% in sales, and the same principle applies to AI-powered business applications.

 

 

Consider a software development team using an AI coding assistant. If their chosen model takes 30 seconds to generate code while a competitor delivers results in 3 seconds, developers face constant micro-interruptions that fragment their focus.

 

Although these tools can enhance throughput, slower models create critical trade-offs that reduce overall effectiveness.

 

For example, as part of our study, we measured how fast sentences were generated by different LLMs, as well as different versions of those LLMs. We found that some models were 3x to 10x slower at producing sentences than the fastest models.

 

The speed advantage compounds daily across hundreds or thousands of users, making generation velocity a critical selection criterion alongside accuracy and cost. It also compounds when generating data at scale, which is often the case with synthetic data.

Enterprise expansion requires AI systems that perform consistently across markets and languages. However, most LLMs have significant performance degradation outside English-dominant scenarios.

 

Our study found that some models scored low in less-represented languages, making them unsuitable for use in those languages. This makes them unsuitable for enterprise deployment. All tested models became predominantly unusable for languages like Kinyarwanda, severely limiting global scalability.

 

Some models even underperformed on more complex tasks in English.

 

 

The business implications can be significant. Poor multilingual performance can lead to compliance failures in regulated industries, customer service breakdowns in international markets, and brand damage from culturally insensitive or inaccurate responses.

 

A multinational corporation deploying a single LLM across regions might find its AI assistant excelling in New York while completely failing customers in Mumbai or São Paulo.

 

In our study, Claude Sonnet emerged as the top performer for language proficiency. It particularly excelled in instruction adherence across multiple languages and was the only model that provided high-quality data in Tamil.

 

However, the study revealed that no single model dominated all languages and tasks. Even with high-performing models, multilingual testing is critical before deployment. LLM candidates must use realistic multilingual scenarios that mirror their actual business requirements to effectively evaluate LLMs.

Effective LLM selection demands moving beyond public, closed-ended benchmarks to focus on real-world performance across your specific use cases, geographic markets, and scaling requirements. Our research demonstrates that LLM performance varies significantly based on task complexity, language requirements, and organizational priorities.

 

Overall, success requires evaluating LLM fit for your specific AI use case over raw performance scores.

 

An LLM might achieve 95% accuracy on English-language benchmarks. However, if it struggles to understand your industry's terminology or your international customer base, it could create more problems than it solves. Similarly, a model with impressive capabilities that consumes 4x more tokens or responds 10x slower than alternatives will drain budgets and frustrate users.

 

Before committing to an LLM, enterprises must adopt systematic evaluation strategies that model the following:

This includes conducting pilot programs with representative workloads, benchmarking actual token consumption patterns, and testing multilingual performance with native speakers rather than relying on automated metrics.

TrainAI by RWS specializes in this comprehensive evaluation and AI data consulting, helping organizations navigate the complex LLM landscape with data-driven insights. Our expertise combines multilingual AI assessment and a deep understanding of the challenges of scaling AI models. We help organizations make informed decisions that optimize for long-term success rather than short-term gains.

 

Download the full LLM synthetic data generation report to learn more about how 9 leading LLMs stack up against each other across 6 data generation tasks and 8 languages.

 

Ready to make the right LLM choice for your enterprise? Contact TrainAI to discuss your specific model selection requirements and avoid the hidden costs that derail AI initiatives.