Best AI for Translation: Should Enterprises Build or Buy?

Generative AI has made translation easy to prototype. Scaling it across an enterprise is a different problem. The question is no longer whether an LLM can produce a fluent translation, but whether AI translation can remain reliable across millions of words, multiple content types, dozens of languages, evolving models, brand requirements, security controls and ongoing human feedback.

That leads to a more fundamental question: should enterprises build their own translation AI infrastructure, connect general-purpose LLMs to existing workflows, or buy a specialized AI translation platform?

For enterprises, the answer depends on more than translation quality. Context, terminology, workflow, human feedback, security, scalability and the ongoing cost of maintaining the technology all matter. Specialist providers such as Translated offer an alternative to building this infrastructure internally, combining translation AI such as Lara with the orchestration and workflow capabilities of TranslationOS.

Key takeaways

• Scaling AI translation requires more than a capable model: Enterprise deployment depends on context, terminology, human feedback, governance and workflow infrastructure that make translation reliable at scale.
• Build-versus-buy decisions should reflect the full lifecycle cost: Enterprises need to account for engineering, data, talent, integrations, maintenance, governance and model changes rather than comparing only API or licence costs.
• Internal development makes sense when translation is strategically differentiating: Building is most relevant when proprietary data or language technology creates a defensible advantage and the organization can sustain the required capabilities over time.
• AI translation should be measured by operational and business outcomes: Automation, time to market, workload reduction and cost savings provide a more meaningful assessment of enterprise value than model performance alone.

The translation AI question has changed

Traditionally, enterprise translation focused on selecting specific machine translation engines based on target language pairs, domains, or content types.LLMs have changed that: a fluent AI translation can now be produced with a simple API call, making prototypes possible in days.

But production translation requires more: consistent terminology and brand voice, document context, content-specific constraints, previous translation decisions, human review, data protection and resilience as models evolve. 

That creates three broad approaches: build the infrastructure internally, bolt-on a general-purpose LLM onto existing translation workflows, or buy a purpose-built translation AI platform.

Why fluent output isn’t the same as production-ready translation

An LLM can produce natural-sounding text while still altering meaning, mishandling terminology, or missing cultural nuance. A 2025 Alibaba study of 17 LLMs across 11 translation directions identified hallucination patterns linked to source length, linguistic bias and language mixing. Similarly, Appen’s research across 20+ languages similarly found gaps between literal accuracy and localization quality, particularly with idioms, puns and culturally nuanced language. For enterprise use, the implication is that quality cannot be treated as a property of the model alone. It has to be managed through a wider translation operation.

The hidden costs of building

A build proposal can look inexpensive when the first budget line is an API and a few engineering sprints. The longer-term economics are different.

Model-change overhead

LLMs are not static software libraries. Changes to an underlying model can alter prompts, output formats, token usage and behavior, requiring regression testing, prompt adjustments, quality validation and downstream integration work. This makes model changes a recurring production risk: seemingly subtle quality regressions can go unnoticed, while format changes can break dependent systems. McKinsey and Oxford found that large IT projects averaged 45% over budget and delivered 56% less value than forecast. The finding illustrates why initial build estimates should not be mistaken for lifecycle costs.

Data and infrastructure

AI systems are only as effective as the data and context behind them. Gartner (2025) reported that 63% of organizations either lacked or were unsure they had the right data-management practices for AI, and predicted that 60% of AI projects unsupported by AI-ready data would be abandoned through 2026. For translation, that data includes approved terminology, previous translations, documents, style rules, metadata, market context and human corrections. 

Specialized talent

Building production translation AI requires more than ML engineers. It combines model evaluation, data engineering, computational linguistics, QA and MLOps. That talent is scarce and expensive. PwC (2025) found a 56% wage premium for workers with AI skills. IDC projects that more than 90% of enterprises will face critical skills shortages by 2026. KPMG (2026) found 82% of technology leaders cite technical skills gaps as a challenge to AI deployment. Translation narrows the talent pool further: enterprises need both AI expertise and deep linguistic and market knowledge. The cost is therefore not simply hiring a team to build the system, but maintaining that capability as the technology evolves.

Governance and risk

At enterprise scale, translation quality becomes a risk-management issue, particularly for legal, financial, regulatory, safety and customer-facing content. An error can have consequences from reputational damage to regulatory exposure. CSA’s 2025 research highlights the growing importance of language risk management, with governance shaped by factors such as content type, audience, regulatory requirements and language pair.  For an internal build, the enterprise takes responsibility for implementing, auditing and continuously monitoring these controls.

The build-vs-buy decision: What does AI translation really cost to scale?

Across enterprise AI, organizations are finding that demonstrating what a model can do is easier than turning that capability into sustained business value. BCG (2025) found that only 5% of 1,250+ companies were achieving AI value at scale, while 60% reported minimal or no material value. McKinsey (2026) similarly found that 80% reported individual productivity gains, but only 37% saw a positive impact on organizational EBIT.

For translation, this distinction matters because the cost and value of AI are determined not just by the model’s output, but by the system required to operate it reliably at scale. The real comparison is not API cost versus software subscription. It is the lifecycle cost of operating the capability. Initial build estimates often overlook integration, internal staffing, migration, maintenance, security and infrastructure. The true cost of AI translation is therefore determined not just by what it takes to build, but by what it takes to operate and adapt over time. That leads to the the build-vs-buy dilemma.

Build, Bolt On or Buy: Three Approaches to AI Translation

An internal build typically has three cost phases:

• Phase 1: Build involves engineering, data preparation, model integration, quality infrastructure, security and compliance, as well as the opportunity cost of diverting engineering capacity from other priorities. 
• Phase 2: Maintain adds ongoing costs for model updates, monitoring, evaluation, governance, data maintenance and workflow changes as models, content and language requirements evolve. 
• Phase 3: Scale introduces another question: does increasing translation volume make the operation more efficient, or simply increase the infrastructure required to run it?

This is also where the three strategic options come into focus:

• Build when translation technology is genuinely differentiated, proprietary data creates a meaningful advantage, and the organization can sustain the capability long term.
• Bolt-on when connecting a general-purpose LLM to an existing TMS is sufficient for the use case, while recognizing that additional context, quality and governance infrastructure may still be required.
• Buy when translation is important to the business but the underlying language infrastructure is not itself a source of competitive differentiation, or when the business requires enterprise-scale translation but cannot own or maintain the underlying translation infrastructure.

Enterprise AI is increasingly moving toward the third model. Menlo Venture’s 2025 research found that 76% of enterprise AI solutions were purchased rather than built internally, up from 53% the previous year. 

The hybrid model: Build what differentiates you, buy what doesn’t

Building may make sense when translation is a genuine source of competitive advantage, but enterprises do not have to choose between owning the entire technology stack and giving up control. A third option is to retain ownership of the applications, workflows and business logic that differentiate the business, while relying on a specialist platform for the translation AI and infrastructure underneath.

Tripadvisor provides a useful example of this approach. Its internal applications connect to a language technology platform, which in turn connects to AI models. This allows them to retain control over its product experience and business logic while the specialist platform manages the rapidly changing language-technology layer.

For enterprises considering this model, the next question is what a credible translation AI platform should actually provide. It needs to go beyond access to an LLM, with the context, translation memory, terminology, human feedback, security and operational infrastructure required for production-scale translation. This is the model Translated has built around Lara, TranslationOS and AI+human approach: combining translation AI with the context, orchestration, workflow and human expertise needed to scale localization.

How should enterprises choose an AI translation platform?

Choosing an AI translation platform requires more than comparing LLM benchmarks. Buyers should assess context, quality, scalability, security and total cost. The following questions can help determine whether a platform is equipped to support enterprise-scale translation:

1. Does the platform understand full document context? It should use the full document and relevant context rather than translating isolated segments.
2. How does it enforce terminology and brand voice? It should apply brand and domain terminology consistently across languages, markets and content types.
3. How does human feedback improve AI translation? Ask how linguist corrections affect subsequent output and how quickly they are incorporated.
4. How does it use translation memory? Relevant previous translations should be dynamically retrieved and used as context rather than treated as a static repository.
5. What happens when the underlying AI model changes? The provider should have a clear process for regression testing, validation and migration.
6. How does it handle security and AI governance? Evaluate data isolation, access controls, auditability, residency and relevant certifications.
7. What is the total cost over 36 months? Include implementation, integrations, maintenance, review, governance, model changes and scaling, not just the initial licence or API cost.

Translated: A scalable AI translation solution

Translated’s approach combines Lara, its proprietary translation AI, with TranslationOS, the platform that orchestrates AI, content, linguistic assets and human expertise.

TranslationOS uses a context-centric architecture that gives Lara access to seven sources of context: complete documents, translation memories, glossaries, style guides, custom instructions, metadata and human feedback. Linguist corrections can also feed back into the workflow and influence subsequent output within the same project. 

Beyond translation, TranslationOS acts as a centralized hub for integrating content systems and automating content ingestion and delivery, connecting AI translation with the wider localization workflow. It also provides real-time visibility into project progress, quality and spend, giving localization teams a centralized view of what is being translated, where work stands and where intervention may be required. As translation volumes and the number of markets grow, this centralized visibility can help teams monitor projects, identify bottlenecks and track performance across the localization operation.

Translated’s security controls also include role-based access controls (RBAC), end-to-end encryption and immutable audit trails, providing visibility into who accessed what and when.

Together, these capabilities illustrate what enterprise-scale AI translation requires beyond the underlying model: context, organizational knowledge, automation and human expertise.

The enterprise evidence from Asana

A useful test of any AI translation platform is how it performs in a real enterprise workflow. Translated and Asana implemented an AI-first localization workflow powered by TranslationOS, combining content integrations, AI, human feedback, translator selection and human supervision.. The results included:

• 70% of the localization workflow automated
• 30% faster time to market
• 268 manual workload days saved annually
• $1.4 million in annual time, license and operational cost savings. 

A framework for evaluating an enterprise AI translation platform

Conclusion

Scaling AI translation is less about choosing the right model than building the right system around it. Enterprises need to weigh quality, context, data, governance, talent and total cost before deciding what to build, what to buy and what to own.

For organizations where translation is not a core differentiator, a specialist platform can provide the infrastructure needed to scale without making translation technology a permanent internal engineering responsibility. The goal is not simply to translate more, but to make AI translation reliable, scalable and measurable as global content grows.

Frequently asked questions

What does it mean to scale AI translation?

Scaling AI translation means moving from isolated LLM translation experiments to a production system that can reliably handle increasing volumes of multilingual content, more languages, different content types, enterprise terminology, brand requirements, human review, security and governance.

Is an LLM API enough for enterprise translation?

An LLM API can be an important component, but an enterprise translation operation typically needs additional infrastructure for context, translation memory, terminology, quality management, workflow automation, security and human feedback. The challenge is therefore not simply model access but how the model is integrated into the translation operation.

Is it cheaper to build or buy AI translation?

The answer depends on the organization’s requirements and what it already owns. A meaningful comparison should use total cost of ownership rather than initial development or API cost alone. Engineering, data, talent, model changes, governance, quality management, integrations and opportunity cost all belong in the calculation.

When should an enterprise build its own translation AI?

A build may be more relevant when language technology is strategically differentiating, the company has genuinely proprietary language data, and it has the technical, linguistic and governance resources required to maintain the system over the long term.

What should enterprises look for in an AI translation platform?

Enterprise buyers should examine context handling, translation memory, terminology management, human feedback, model-update processes, integrations, security, governance, quality measurement and the platform’s ability to demonstrate business outcomes.

Who provides scalable AI translation?

Specialist AI translation providers offer platforms designed to manage translation at enterprise scale. Translated, for example, combines Lara translation AI with TranslationOS to provide translation, context management, workflow orchestration and human-in-the-loop capabilities.

Is human review still necessary with AI translation?

Human review remains common in enterprise AI-assisted translation. Slator’s 2025 research reports that 90–98% of respondents using MT and/or LLMs perform some level of post-editing, while 84% of language service integrators reported clients specifically requesting human editing of AI-generated content.