We work with software teams at every software localization planning stage. Some come to us with a ChatGPT integration and a spreadsheet. Others have enterprise translation platforms they invested in years ago. Both are trying to solve the same thing. Both run into problems they didn’t see coming.
This post maps out the common patterns we see, where teams get stuck at each stage, and what software localization planning actually looks like when real products and real budgets are involved.
Two Halves of the Same Setup
Localization infrastructure works a lot like the rest of your tech stack. The software localization planning decisions you make early feel small. A translation plugin here, a manual workflow there. Everything works fine at one or two languages. By the time you’re at five or eight, those early choices have hardened into cost structures that are hard to change. It’s the same pattern the Startup Genome project found across 3,200 startups. Scaling before the foundation is ready is the number one reason things break.
Every software product that goes international goes through some version of this. It’s not a failure. It’s growing pains. But some teams navigate it much faster than others because they understood what the setup needed to look like before the problems started compounding.
A working localization setup has two parts. Most teams start with one and realize too late they need the other.
A TMS (Translation Management System) handles the plumbing. File exchange between your codebase and your translators. Translation memory that stores everything already translated so you never pay for the same sentence twice. Version control so nobody overwrites work in progress. Integrations with GitHub, Figma, your CMS. The TMS is the pipe.
An LSP (Language Service Provider) handles what goes through the pipe. Translation, review, cultural adaptation, localization QA. The people who actually know whether your German checkout flow reads naturally or sounds like it was run through a filter.
You need both. The TMS without an LSP gives you unsupervised AI output going straight to production, nobody catches it until a customer in Stockholm emails support. That’s the localization quality gap in action.
The LSP without a TMS gives you people doing great work inside a workflow held together by email threads and shared drives.
The pattern we see most often with smaller teams is that someone picks one and assumes it covers everything.
A team signs up for a TMS platform, connects it to their repo, toggles on machine translation, and publishes. Fast. Cheap. No human in the loop. The translations are technically in the right language. But “technically correct” and “something your customers trust” are not the same thing. Unsupervised AI translation will flatten your product voice. Terminology drifts. Context errors pile up. And because nobody is reviewing, nobody catches it until a customer in Munich emails support asking why the confirmation screen says something weird.
Going the other direction, a team hires an LSP but has no TMS connecting the workflow. Now every translation request means exporting files manually, emailing them, tracking versions in a spreadsheet, importing the finished work back into the product. That’s workable at one language. At three, someone is spending a full day a week just on file logistics.
Neither approach is wrong for where the team is at that moment. The problem is staying there too long.
What Software Localization Planning Looks Like at Three Different Stages
Every product that goes international grows through roughly the same stages. The infrastructure questions are different at each one.
First International Client
This is how it usually starts. Not a grand expansion strategy. No real software localization planning. A client asks your sales team whether the product is available in their language. Or a new enterprise deal requires German and French as part of the contract. The expansion is client-driven, not roadmap-driven.
The team does what makes sense in the moment. Someone integrates a translation API. DeepL, ChatGPT, Google Translate. The product has a language switcher within a week. It works. Kind of.
We saw this with Vault. They started with ChatGPT integrations to move fast. The output covered the basics, but quality gaps started showing up in customer-facing flows. They brought us in as an LSP to handle the language work properly. But there was no TMS connecting the two sides. So the workflow was manual on both ends. Their team exports files, sends them to us, we translate, send them back, they import.
That’s a normal starting point. Nothing went wrong. They solved the immediate need and kept the client happy. The question is what comes next.
Multiple Markets, Split Platforms
Your product is in five or six languages. The core product strings live in a TMS. Translations flow in and out of your codebase automatically. That part works.
But your content isn’t all in one place. Product strings are in the TMS. Your marketing site is on a different platform. Help docs are somewhere else. And every platform has a different answer to the question “how do we get translated content in and out?”
For teams running a product-led model, the planning question shifts from “what do we translate” to “what quality level does each content type need.” We cover that framework in detail in our post on product-led growth localization
We work with a SaaS company where the product localization runs smoothly. Strings are connected to a TMS, updates flow with the development cycle. But their website is built on Framer. Framer has limited integrations with translation tools and no clean export/import path. So every time website content needs translating, someone on their design team manually copies text out, sends it to us, gets translations back, and manually pastes them in. Language by language. Page by page.
At two languages, this is a small annoyance. At five, it’s a recurring cost item. At ten, it’s a significant line in the localization budget that exists purely because of a platform choice made before localization was on anyone’s radar.
The reality is that sometimes you live with it. For us it wasn’t exactly the software localization planning dream come true. Migrating a website platform to fix an integrated localization workflow is rarely the right call. You have to weigh the ongoing manual cost against the migration cost and make a practical decision. We get that. We work within those constraints regularly. But you should know the cost is there, and you should factor it into your localization budget honestly rather than being surprised every quarter.
Enterprise Platform Lock-In
Your company invested properly in localization infrastructure years ago. A full TMS integration. Proper workflows. Trained teams. Everything was set up the right way for the time.
The problem is that localization tooling has moved fast. AI translation quality improved significantly. Workflow automation tools like Zapier and Blackbird opened up new ways to route content. Newer TMS platforms offer features that didn’t exist when you signed your contract. But your setup is rigid. It was designed to be reliable, and it is. It’s just not designed to evolve.
We see this with Reputation. They invested heavily in a TMS platform integration. It works. But the platform is now considered a generation behind. AI-assisted translation workflows, automated routing based on content type, newer quality estimation tools. None of that is available without a migration. So we translate everything with full human workflows, even content that could safely go through AI with human review at a fraction of the cost.
The migration math usually makes sense on paper. McKinsey estimates that technical debt accounts for 20 to 40 percent of a company’s entire technology estate, and localization infrastructure is no exception. New platform, lower per-word costs, faster turnaround, payback within a year. But enterprise teams have competing priorities. The current system works. It’s just not optimized. And the engineering hours to migrate are the same engineering hours that could go toward product features.
This isn’t a criticism. It’s a reality of how enterprise software decisions work. The point is that if you’re building your localization infrastructure now, build it to be modular. Pick tools that let you swap components without rebuilding the whole pipeline. Because whatever “best practice” looks like today will look different in three years.
The Software Localization Planning Scope Is Always Bigger Than the Request
Here’s something that catches teams off guard. A client asks for one feature in 28 languages. AI voice, for example. Sounds contained. One deliverable, clear spec, predictable cost.
But then you follow the user journey. The voice lives inside a platform. That platform has UI, settings screens, onboarding flows, error messages. If the AI voice is in Portuguese but every button around it is in English, you haven’t localized a feature. You’ve created a jarring experience where half the product speaks one language and half speaks another.
One feature request just became a scoping exercise for the entire user journey around that feature. This happens constantly. And it’s the moment where the right localization partner earns their fee, not by saying yes to the full scope, but by helping you figure out how to build localization references gradually.
Making Every Euro Count
This is where most software localization planning advice gets it wrong. The standard checklist tells you to prepare everything, translate everything, test everything. That’s not a plan. That’s a wish list with no budget attached.
The better question isn’t “are we ready?” It’s “where does each euro of localization investment create the most value?” Narrow and deep beats wide and shallow almost every time. HBR made the same case for market expansion strategy overall. Bigger isn’t always better.
The Honest Conversation Before You Start
Most teams come to us expecting us to say yes to everything. Translate the full product. All 28 languages. Ship it all at once. That’s revenue for us on paper. But it’s not advice we’d give.
Our service position is simple. Every euro you spend on software localization planning and localization itself should make your product better for the people using it. If we think you’re about to spend money in the wrong place, we’ll say so.
Sometimes that means telling a team to start with 4 languages instead of 12. Sometimes it means pointing out that the marketing site can wait but the checkout flow can’t. Sometimes it means flagging that a quick engineering fix on string externalization will save more money in three months than it costs in a week.
We’ve worked with products at every level of readiness. Startups that hardcoded everything. Enterprise platforms locked into rigid setups. Products that work perfectly in English because nobody ever tested them in anything else. All of them got to a working localization workflow. The ones that got there fastest were the ones where both sides were honest about what mattered and what didn’t.
That’s what our project managers do. They look at what you have, figure out the smartest path forward, and tell you the truth about where your budget should go. You don’t need to become an internationalization expert. You need someone who can bridge the gap between your product team, your engineering team, and the language work, and make sure the money goes where it counts. And if you’re comparing partners right now, we wrote a separate guide on what to watch for when evaluating vendors and the questions most companies forget to ask.
Let’s Figure Out the Right Software Localization Planning Approach
Tell us what you’re working with. Your product, your target markets, your timeline. We’ll tell you what we’d prioritize, what can wait, and where the setup decisions will save or cost you money over the next twelve months.
Not a sales call. A scoping conversation. If it makes sense to work together, we’ll get specific about what that looks like. If the honest answer is “you’re not ready yet,” we’ll tell you that too, and what to fix first.
Related Content
The full process from internationalization to launch, including how translation fits into your development workflow.
A breakdown of what drives the price, from per-word rates to engineering hours and ongoing maintenance.
What localization QA covers, how much it costs, and what to ask your partner before testing starts.
FAQ
Not necessarily. Most LSPs either have their own TMS or can recommend one based on your stack. What matters is that a TMS enters the picture early so translation memory starts building from project one. If you wait until language five to set one up, you’ve already paid to translate the same content multiple times.
Yes, and for some content types that’s the smart approach. AI gives you speed and coverage. Human review adds accuracy, brand voice, and contextual understanding. The key is not publishing AI output without any oversight. Set up the review process before you go live, even if it’s lightweight at first.
Follow the user journey. If a client asks you to localize one feature, trace every screen and message the user sees before, during, and after interacting with that feature. If any of it stays in English, the experience breaks. The scope is the journey, not the feature. A good LSP will help you map this and prioritize what to invest in first.
Almost always. Six languages with full coverage of the user journey will perform better than twenty with patchy translations. Users in those six markets get a product that feels native. You build the infrastructure, test the workflow, and scale from a position that works. Rushing to launch in every market at once usually means doing none of them well enough to matter.
Most TMS platforms range from free tiers for small volumes to a few hundred per month for mid-size SaaS. The LSP side is project-based. For a detailed breakdown of what drives the price, see our software localization cost guide.
Ask which parts of the localized product your users actually interact with. A translated checkout flow that drives conversions is money well spent. A translated admin panel that three people see is not the priority. Your localization partner should be able to tell you where the investment creates the most value and where it can wait.
