Localization pricing is one of the most debated topics in our industry, yet it’s often the one that feels the most confusing to buyers. While most procurement conversations focus on per-word rates and match discounts, those metrics don’t always tell you what you’re actually paying for.
In our latest Field Notes episode, Global Marketing Director Stephanie Harris-Yee sat down with industry consultant Erik Vogt to discuss why the traditional effort-based pricing model is becoming obsolete in an AI-driven world. They explored a fundamental trade-off in how we value language services: moving away from paying for the process of translation and toward paying for the certainty of the results.
Here’s one scenario we see often: A client receives two quotes. One is significantly lower because it relies on a “black box” of automated output with minimal oversight. The other is higher, reflecting a rigorous human-in-the-loop validation process. On a spreadsheet, they look like the same service. But in the real world—especially in industries like life sciences or manufacturing—they represent two completely different levels of risk.
The problem is that traditional pricing models are context-blind. They treat a UI button and a surgical manual as the same unit of work. As Erik points out in the episode, this disconnect creates a massive gap between what a client thinks they are buying and the actual protection they need.
“Quality isn’t a single thing. It’s a verifiable behavior,” Erik says. “When we talk about pricing quality, we’re really talking about the probability of an error and the consequence of that error.”
In practice, delivery teams often see clients equate quality with linguistic perfection, even when the real risk lies in whether an error actually changes how the content is used or understood.
The Three Dimensions of Quality
Understanding why traditional pricing falls short requires deconstructing what quality means in a modern workflow.
In many programs, teams move forward without agreeing on what the content needs to achieve or how much risk is acceptable. Buyers then assume levels of validation and protection that were never part of the workflow or the price.
Erik points out in the Field Notes episode that most procurement conversations fail because they conflate three very different things.
First, there’s output quality. This is the baseline linguistic check. Is the grammar correct? Does it follow the glossary? While essential, this is now a commodity. In an AI-first world, getting strings to be linguistically accurate is the starting line, not the finish line.
Then we have process quality, which focuses on the operations of the project. This covers infrastructure: selecting the right subject matter linguist, managing translation memory hygiene, and ensuring the workflow is scalable. This part is about consistency, making sure the system can produce the same high standard regardless of volume.
But the most critical dimension—and the one often missing from a quote—is outcome quality. This is fitness for purpose. It’s the difference between a translation that is merely correct and one that actually achieves its goal, whether it’s passing a regulatory audit or avoiding a costly product recall.
Building a Risk-Based Framework
If we accept that quality is about results, then pricing needs to follow the risk profile of the content. The ability to categorize content by risk, purpose, or regulatory exposure is the way to unlock this efficiency. When you know exactly what a string is, you can determine how it should be translated, reviewed, and released across the workflow.
On one end of the spectrum is low-risk content. This includes high-volume support tickets or community forum threads. In these cases, the priority is throughput. Because the impact of a minor linguistic error is negligible, the pricing should reflect a lean, highly automated workflow.
That distinction often only becomes visible once content is in use. Jan Nohovec, who leads enterprise localization quality teams at Argos, notes that English-first or lightly reviewed content may be sufficient in markets with high English knowledge, but introduces real usability risk in markets like Japan, where users depend on fully localized content.
“Quality isn’t about perfection,” Nohovec says. “Language is subjective, and there’s always a margin for error. The important question is whether an error actually matters for the content, the client, and the end user.”
On the other end is high-risk content. This involves content like surgical instructions, legal contracts, or global campaigns. In these scenarios, an error carries significant consequences for compliance or brand integrity. The price for these projects reflects the human intervention and validation required to provide certainty.
This is where the “as-is” model becomes a problem. Choosing raw machine translation or a workflow with no documented QA process might reduce the invoice, but it leaves the buyer responsible for any errors that surface later. For a partner to take ownership of that risk and guarantee the content is safe to publish, the pricing must account for the extra validation required. In the end, the cost reflects whether the responsibility for a mistake stays with the buyer or is handled by the provider.
Aligning Price with Liability
The most difficult part of the pricing conversation is the liability gap. Many organizations require unlimited liability as a standard contract clause, but there’s often a disconnect between that legal requirement and the budget allocated for the risk management necessary to support it. If a provider is taking on the accountability for high-stakes content, the pricing needs to account for the extra steps required to guarantee the result.
The solution is to tie the price to how decisions are made in the workflow—which content is escalated, which is automated, and where validation is mandatory before release. Rather than a one-size-fits-all rate, pricing should vary based on how many layers of validation are built into the workflow. For critical content, this means paying for additional tasks like back-translation or subject-matter expert reviews. By including these steps in the price, companies ensure the protection they receive covers the risk they are asking the provider to assume.
Using Data to Verify Quality
Outcome-based pricing relies on a clear way to prove the translations are fit for use. A traditional invoice only proves that a translation was completed. Providers use quality metrics—such as error-density reports or compliance scores—to verify that content meets project requirements. If a client is paying for a manual to be audit-ready, the data serves as the proof that it will pass that audit.
This logic also changes how a localization budget is distributed. Companies often over-spend on human review for low-impact content like internal communications. By defining the necessary result first, organizations can use automation for low-risk tasks and reserve their budget for projects where a mistake has real consequences. The goal is to align the spend to the degree of certainty each project requires.
Partnering for Results
The choice between pricing models determines who owns the consequences of the work. When a company pays for volume, they are buying a process and accepting the risk themselves. When a company pays for an outcome, they are buying a guarantee.
As AI commoditizes baseline translation, the value of an LSP is no longer tied up in moving words between languages.The value is in providing the verification and the infrastructure necessary to stand behind the result. A vendor sells a process; a partner sells the certainty that the content will perform exactly as intended.
Is your localization budget buying you words, or is it buying you peace of mind? To learn how to align your spend with your actual risk profile, contact us.
Liz Dunn Marsi
6 min. read
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