The Translation Revolution: How LLMs Are Cutting 90% of Translation Costs

The Surprising Discovery

Here is a fact: if you’re using any translation services for your business, school, or personal life, you should probably move to translating with LLMs — it will save around 90%* of the costs with fewer mistakes and more accurate nuance!

How is that possible? We’ve had translation services perfected over centuries, so how is it that a generic LLM service can outpace specialized translation systems? When I first encountered these numbers in my previous role leading the R&D efforts, I was skeptical. But after implementing LLM-based translation and seeing the results firsthand, the evidence was undeniable.

It comes down to a couple of things: Technology and Business!

The Old World of Translation

Traditional translation services (TTS) use an old architecture of statistical models and are at a state of “it’s good enough and profitable” (although they do improve a little) with a very stable customer base that don’t complain about translating “bricklayer” to “שכבת לבנים” (Hebrew translation for “layer of bricks”) or the older example of “מאלף כלבים” (“dog trainer”) translated to “1000 dogs” (“אלף” is 1000 and ״מאלף״ is trainer).

Why do they accept these errors? Because it’s probably less than 20% of the cases that they encounter, and until recently — what was their alternative?

The above translation errors were some highlights we had to handle in my previous work. I remember sitting in a meeting with one of the engineers and a domain expert, reviewing yet another batch of mistranslated product content. We were paying premium rates for “professional” translations that still required internal reviews. There had to be a better way.

The Breakthrough Moment

There has been a lot of buzz around LLMs, and yes, they’re still statistical models at the end of the day, but there has been a framework and architecture breakthrough in neural networks that’s really interesting.

The turning point came when we ran a side-by-side test: the same content processed through our traditional translation service versus an LLM-based approach. The results weren’t just marginally better — they were transformative (for Semitic languages). The LLM translations captured nuances our previous service missed entirely at a fraction of the cost.

What makes this possible? Three revolutionary changes:

1. Unified language understanding: Today’s LLMs are trained on all data in different languages all at once! Traditional translation models are usually trained language-specific, but LLMs understand the nuances between different locales by mapping all languages to the same computational space.
2. Self-attention mechanisms: The way LLMs process language allows them to consider the entire context of what’s being said, not just word-by-word replacements. For more, check this article.
3. Scale economics: The infrastructure for deploying LLM-based translation is fundamentally different from traditional services.

Why LLMs Are Dramatically Less Expensive

The economics behind LLM-based translation reveal several reasons for the dramatic cost difference:

• Bundled overhead costs: Traditional translation services include certification, standards compliance, and human review processes in their pricing structure. These administrative layers add significant costs that LLMs simply don’t require for most use cases.
• Architectural efficiency: TTS deploys complex architecture to support a vast pool of languages, often requiring multiple specialized models. LLMs support multiple languages at once with a single model, dramatically reducing infrastructure and maintenance costs.
• Research momentum: LLMs have received massive R&D focus because of their wide usability. There is extensive active research on making them “smaller” and smarter through techniques like quantization and partial parameter activation (such as DeepSeekMoE). This continuous improvement cycle benefits all LLM applications, including translation.
• Current pricing dynamics: It’s worth noting that current LLM pricing is arguably too low for long-term profitability and might increase by 10–15% if there aren’t further developments in making models more efficient. Even with such an increase, the cost advantage over traditional services would remain substantial.

Most popular translation services will offer you 10$ per million characters, on the other hand, LLM pricing goes by the token, if you take Claude Haiku costs ($0.80/MTok for input and $4/MTok for output), on average, you’ll be paying around 1.26$ for the same job (translating Hebrew to English, with average 3–4 char/token).

The Business Reality Check

It’s not all that sunshine! Going back to the business side, TTS are “guaranteed”, which limits some businesses from just moving on (although they 100% make more mistakes than LLMs, but it’s business, not logic).

Another business aspect that keeps fueling TTS is the legacy/big businesses that can’t easily move from one service to another. I’ve seen procurement teams cling to outdated translation contracts simply because “that’s how we’ve always done it.”

There’s also a language coverage gap; LLMs, in the near future, won’t support all languages that are supported by TTS since there are languages with small footprints on the internet (LLMs learn from the internet), like Yiddish or Quechua, that still need traditional translation services. This creates a genuine market need that LLMs currently can’t address.

The Implementation Journey

From my experience, using LLMs as translation services in live production products has its own challenges. We had to develop new quality assurance processes and handle edge cases differently than before.

For teams looking to implement this approach, I’ll be writing a technical article discussing key considerations for using LLMs in production.

The Future Is Already Here

Looking ahead, several trends are becoming apparent:

• Consumer-led transition: Private users are already starting to abandon TTS because LLMs are communicating in their native languages without requiring explicit translation. This shift will gradually erode a portion of the traditional translation market.
• Hybrid solutions are emerging: Leading TTS providers will inevitably start incorporating LLM capabilities and develop hybrid solutions that leverage the strengths of both approaches.
• Specialization and persistence: TTS will remain with us for the long term, becoming more specialized in areas where LLMs fall short. They won’t be fully replaced, primarily due to certification requirements, standards compliance, and business policy limitations.
• Pricing evolution: There’s a strong possibility that LLMs’ competitive pricing will drive down the cost of traditional translation services over the next five years. Alternatively, increased specialization might create a unique pricing opportunity for TTS providers (higher price points but with more profit from a smaller market share).

Conclusion

The translation landscape is undergoing a fundamental shift as LLMs deliver superior results at a fraction of the price. While business considerations and specialized needs will keep traditional translation services relevant in certain contexts, the economic and quality advantages of LLM-based translation are simply too compelling to ignore.

For most organizations, the question isn’t whether to transition to LLM-based translation but when and how to implement it effectively. The companies that embrace this shift now will gain both competitive advantage and significant cost savings in their global communications.

*Based on comparing enterprise translation service average costs with equivalent API-based LLM solutions at current market rates (2025) on the most popular languages on the internet