Software localization: process, tools, and best practices

What is software localization?

Software localization (l10n) is the process of adapting a software product for a specific locale or regional market. A locale is a combination of language and regional conventions — for example, en-US (American English) and en-AU (Australian English) are two distinct locales despite sharing the same base language.

Localization goes well beyond word-for-word translation. A fully localized product adapts:

• UI strings, menus, tooltips, and error messages.
• Date, time, and number formatting — DD/MM/YYYY vs MM/DD/YYYY, commas vs. periods as decimal separators.
• Currency symbols and payment conventions.
• Images, icons, and color palettes that may carry different cultural meanings across regions.
• Legal disclaimers and compliance language required by local regulations.
• Right-to-left (RTL) text rendering for Arabic, Hebrew, Farsi, and Urdu.
• Keyboard shortcuts and input method compatibility.
• Plural forms, gendered nouns, and grammatical structures that do not map directly to English.

What is the difference between software localization and translation?

Short answer: Translation converts text from one language to another. Localization adapts the entire user experience — text, layout, logic, and cultural context — for a specific target market.

The distinction becomes clear in the details. A US-based SaaS product might label an address field “ZIP code.” A translated German version might carry the same label — but a properly localized version replaces it with “Postleitzahl” and updates the validation logic to accept the correct five-digit German format.

Localization failures are rarely about missing words — they are about missing context. A product that feels “translated” rather than “localized” signals to users that they were an afterthought, not a primary audience. That signal is expensive: it erodes trust and increases churn in markets where users have local-language alternatives.

Why does software localization matter?

Short answer: Localization improves revenue potential, user satisfaction, and product adoption in non-English markets. The data consistently shows that users prefer — and are more likely to pay for — products in their native language.

According to CSA Research’s “Can’t Read, Won’t Buy — B2C” study, 76% of online shoppers prefer to buy products with information in their native language, and 40% will never buy from websites in other languages (CSA Research, 2020). For software teams, this means an unlocalized product is functionally invisible to a significant portion of the global addressable market — not because users cannot find it, but because they will not use it.

The market-level opportunity reflects this. The global language services industry — which includes software localization — was valued at USD 75.5 billion in 2024 (IMARC Group, 2024), with the strongest growth in markets where English is not the primary language.

Beyond revenue, localization affects:

• User retention — users who interact with a product in their native language engage more deeply, complete onboarding more successfully, and churn less.
• Support costs — a poorly localized UI generates confusion and increases support ticket volume per user.
• App store discoverability — localized app store listings — title, description, keywords — improve organic ranking in non-English app store searches.

Localization improves conversion because it reduces friction at the exact moment a user is deciding whether to trust your product.

The software localization process: step by step

Software localization follows a defined sequence of steps, each building on the last. Skipping or rushing any stage — particularly the content preparation at the start or the quality assurance at the end — creates problems that compound across every locale you add.

Step 1: Define and prioritize your target locales

Start by identifying which markets represent the strongest opportunity and align with your product roadmap. Consider:

• Existing user geography — where is organic, unlocalized usage already coming from?
• Market size and language coverage — Spanish covers 20+ countries; Portuguese covers Brazil and Portugal as distinct locales.
• Competitive presence — are strong local-language alternatives already established?
• Localization complexity — language distance from English, script type, RTL support requirements, and plural form complexity.

Common first-wave locales for SaaS and software products include German, French, Spanish (ES and LATAM as separate locales), Brazilian Portuguese, Japanese, Korean, and Simplified Chinese. Each represents a distinct locale with its own formatting conventions, not just a translated version of another.

Step 2: Extract and organize localizable content

Extract all localizable strings from your codebase into resource files in the format appropriate for your platform. Before sending anything to translation, complete a full content inventory — UI strings, error messages, onboarding flows, email templates, push notifications, and in-app tooltips. Missing strings discovered mid-project are disruptive and expensive to reconcile.

At this stage, context is the most important asset you can give a translator.

Translators need to know:

• Where the string appears in the UI (button label, page title, error message, tooltip).
• What it refers to or what action it triggers.
• Any character limits imposed by the UI container.
• Whether it is standalone or part of a sentence containing other variables.

Strings without context produce inconsistent, low-quality translations — particularly for short UI labels that are ambiguous in isolation. “Edit,” for example, may be a verb or a noun, and in German those forms are different words. At scale, missing context compounds quickly: Haufe X360, a German business software provider, managed 380,000+ strings with no context or metadata — translators had no way to distinguish “account” as a bank account versus a customer account, and localization cycles ran for weeks as a result.

Learn more about string localization

The format of your resource files will depend on your platform and framework — common options include JSON, XLIFF, PO, STRINGS, XML, RESX, ARB, and YAML. Most localization platforms support import and export across these formats, so translators and engineers can work in their native environment while content stays synchronized in a central system. For a hands-on walkthrough using React and i18next, our React app localization guide covers the full setup from string extraction to automatic sync.

Step 3: Translate and localize content

This stage involves human translators, translation technology, or a combination of both, depending on content type and quality requirements. An effective translation workflow typically uses:

• Professional translators or LSPs for high-visibility content including product UI, onboarding, and marketing copy.
• Translation memory (TM) to automatically reuse approved translations for repeated or similar strings, reducing cost and improving consistency across releases.
• Glossaries and termbase to maintain consistent product terminology across languages and across translators working on the same locale.
• Machine translation with human post-editing (MTPE) for high-volume, lower-criticality content where speed and cost are priorities — system notifications, error messages, administrative settings.

Not all content deserves the same level of investment. Tier content by visibility and consequence: premium human translation for anything a user encounters during critical moments — onboarding, purchase flows, error states that affect decisions; MTPE for functional but lower-stakes content such as settings labels, tooltips, and system messages; and raw MT only for internal or transient content.

Step 4: Integrate translations and build

Translated resource files need to be integrated back into the codebase and built into the product. This step surfaces issues that cannot be caught in isolation: truncation caused by layout constraints, font rendering problems for specific scripts, functional bugs tied to locale-specific behavior, and missing strings that were not captured during extraction.

A CI/CD integration that automatically pulls translated files from a localization platform — and triggers builds when new translations are approved — reduces the gap between translation completion and production release from weeks to hours. Educational software company Enuma implemented this model by connecting Gridly to their Jenkins pipeline via API, completing the full integration in two weeks and eliminating the manual overhead of synchronizing production and localization updates.

Step 5: Conduct linguistic quality assurance

Linguistic quality assurance (LQA) is the process of reviewing translated content in the live product or a staging environment — not in a spreadsheet or translation memory tool. In-context review catches issues that are invisible in isolation:

• Text truncated or overflowing due to UI constraints.
• Untranslated strings that passed through extraction.
• Terminology inconsistencies between features or screens.
• Cultural or contextual errors that only become apparent when the string is read in its UI context.
• Placeholder or variable formatting issues that break at runtime.

Step 6: Release and iterate with continuous localization

Software localization is not a one-time project — it is an ongoing discipline. A release-based approach creates backlogs that grow with every sprint.

Continuous localization treats translation as a parallel workstream — new strings flow to translators as soon as they are committed, and approved translations return to the codebase automatically. This approach eliminates waterfall translation bottlenecks and keeps international releases in step with the English version. In practice, it means every new string committed by a developer becomes immediately available for translation in the localization platform — and once approved, flows back to the codebase ready for the next build.

What are the most common software localization challenges?

Software teams running localization for the first time tend to encounter the same problems — regardless of team size or tech stack.

Text expansion breaks fixed-width UI layouts

Text expansion is one of the most technically disruptive challenges in software localization. European languages such as German, Finnish, and Polish typically produce strings that are 25-35% longer than their English source (W3C, 2007). In fixed-width UI containers — buttons, menu items, mobile nav labels — this causes overflow, truncation, or wrapping that breaks the intended layout.

Fast-moving codebases push localization out of sync

In teams committing new strings regularly across multiple feature branches, keeping localization in sync with development becomes a coordination problem. Strings are added without context, resource files diverge between branches, and translators receive batches without knowing which release they belong to.

The root cause: localization is treated as a downstream step rather than a parallel workstream integrated into the development pipeline. Teams often try to solve this with better communication — more detailed handoff documents, more Slack messages between developers and translators — without addressing the underlying problem of disconnected systems. The structural answer is multilingual content management — treating source strings and translations as a single, continuously synchronized system rather than separate files passed between teams.

Translation consistency degrades as products and teams grow

As a product scales, the same concept may be described differently across features — especially when multiple translators work on different areas without a shared reference. Terminology inconsistencies compound silently. A user who encounters different names for the same feature across screens loses confidence in the product without being able to articulate why.

Plurals, genders, and dynamic strings resist static translation

Dynamic strings — content that changes based on user data or runtime variables — cannot be translated as static text. Languages such as German, Russian, and Arabic have more than two plural forms; French and Spanish nouns change based on the gender of the subject; and variable-embedded strings must remain grammatically correct regardless of the inserted value.

Waterfall localization creates a permanent lag behind your English release

Many teams run localization as a waterfall process: English ships first, then translation happens, then a localized release follows weeks or months later. This creates a permanent lag between the English product and international versions, and grows with every sprint.

Software localization best practices

The following practices apply across team sizes and tech stacks. They are ordered to reflect the sequence in which they have the most impact — front-loading the content and workflow decisions that make everything downstream cheaper and faster.

1. Audit all localizable content before translation begins. Map every user-facing asset — UI strings, error messages, onboarding flows, email templates — before sending anything to translation. Missing strings discovered mid-project are expensive to catch up on and disruptive to release schedules.
2. Provide context for every string. Translators working from strings without location, character limits, or UI context produce inconsistent, low-quality output. Include screenshots, UI location, character limits, and variable descriptions as metadata on each string.
3. Build localization into your CI/CD pipeline. Treat translated content as code — version-controlled, reviewed, and deployed through the same pipeline as every other asset. Manual file transfers between developers and translators create delays, version drift, and errors.
4. Use translation memory from day one. Even on a small product, TM pays dividends as the codebase grows. Repeated or similar strings are auto-populated with approved translations, reducing both cost and turnaround time per release.
5. Test in-context, not in spreadsheets. LQA must happen in the live product or a staging environment. Reviewing translations in isolation misses truncation, layout breakage, and functional bugs that only surface in context.
6. Localize for the locale, not just the language. Spanish for Spain (es-ES) and Spanish for Mexico (es-MX) are different locales with different vocabulary, idiomatic conventions, and date formatting. Treating them as the same locale produces content that feels generic to both audiences.
7. Define quality tiers by content type. Premium human translation for UI and onboarding, MTPE for notifications and admin settings, and raw MT for internal or transient content. Matching quality to content type optimizes both cost and output.

For a broader look at how these practices come together — covering team structure, language ROI, and content consolidation — see our guide to streamlining app localization. SaaS teams can go further with our SaaS localization guide, which covers platform-specific workflow and release considerations.

How do you choose a software localization tool?

Short answer: The right software localization platform for your team depends on tech stack, workflow maturity, and integration depth. The most important factors are format coverage, real-time collaboration between developers, translators, and project managers, and whether the platform connects directly to your development pipeline without requiring manual file transfers.

When evaluating localization platforms, the most common mistake is comparing features as a checklist — translation memory, API support, format coverage — without considering how those features behave under the conditions of continuous development. A platform that handles a static translation project well may break down when strings are being added, changed, and branched across multiple sprints simultaneously. The real test is how the platform behaves when your product is moving, not when it is standing still.

Consider the following criteria:

Localization platforms suit teams that need translation workflow management. Content management platforms suit teams that also need real-time sync between development and localization — where strings and translations must stay in continuous alignment across every release.

Gridly is a localization content management platform built for the structural complexity of software and game localization — multiple content types, parallel development branches, and the requirement that source strings and translations always remain synchronized. It is designed for teams where localization is a live, continuous part of the development workflow, not a quarterly batch process.

Frequently asked questions about software localization

What is software localization?

Software localization is the process of adapting a software product for a specific locale or regional market. It goes beyond translating text — it covers every element a user encounters, including date and number formatting, currency conventions, right-to-left text rendering, culturally appropriate imagery, legal compliance language, and grammatical structures such as plurals and gendered nouns. A locale is a combination of language and region, so en-US and en-GB are treated as distinct locales even though both use English. The goal of software localization is for a product to feel as though it was built for a target market, not simply translated for it.

How long does software localization take?

Timelines vary based on word count, language complexity, team capacity, and how thoroughly the product is prepared for localization. A well-prepared SaaS product adding a single European language might complete localization in two to four weeks. A large-scale product adding ten languages simultaneously can take three to six months. Teams using continuous localization workflows eliminate this timeline entirely — there is no discrete localization phase because translation runs parallel to development.

How much does software localization cost?

Software localization costs depend on word count, language pair, content type, and quality tier. Professional human translation typically ranges from USD 0.08 to USD 0.30 per source word depending on language pair and specialization. Machine translation with human post-editing (MTPE) costs less per word while maintaining acceptable quality for most UI content. Total localization costs also include localization platform fees, engineering time for integration and QA, and linguistic QA effort — typically the most underestimated line item in a localization budget.

What is pseudo-localization?

Pseudo-localization is a testing technique where source strings are replaced with modified characters and artificially lengthened text to simulate a localized product before real translations exist. It allows developers and QA teams to find layout breakage, encoding errors, and hardcoded strings early — without waiting for translators. Running pseudo-localization as part of the CI pipeline is one of the highest-value, lowest-cost steps in a software localization workflow.

When should you use machine translation for software localization?

Machine translation (MT) is most effective as an acceleration tool, not a complete replacement. For high-visibility product surfaces — onboarding, UI labels, error messages that affect user decisions — human review or full human translation remains the standard. MT with human post-editing (MTPE) is effective for high-volume, less critical content such as system notifications, tooltips, and administrative settings.

Which languages should you localize your software into first?

Language prioritization depends on your existing user base, market opportunity, and localization complexity. A common starting framework: identify where you already have unlocalized users adopting the product (they are signaling intent despite the language barrier), then layer on market size and revenue potential. European languages — German, French, Spanish, Italian — tend to offer strong ROI because of market size and a well-established translation supply chain. Japanese and Korean require more engineering effort (font rendering, input method support, UI spacing) but represent large, high-intent software markets.

Conclusion

Software localization is not a feature you add when a product reaches scale — it is the infrastructure that makes reaching scale in international markets possible in the first place. The teams that integrate localization into their development workflow from the beginning spend less per language, release faster internationally, and deliver a consistently better experience to non-English-speaking users.

The process starts with a complete content audit, continues with structured translation workflows and in-context quality assurance, and scales through the right platform to keep source strings and translations synchronized across every release. Getting those foundations right is what separates a product that is “available” in ten languages from one that actually works in ten languages.

If you are looking for a platform that brings together content management, translation workflow, and developer integration into a single system — Gridly is built for exactly that. Get started with a 14-day free trial or schedule a demo to see Gridly in action.

Key takeaways

• Software localization is not the same as translation. Translation converts text. Localization adapts the full user experience — layout, formatting, cultural context, and logic — for a specific locale.
• Context is the most important asset you give a translator. Strings without location, character limits, or UI context produce inconsistent output.
• Text expansion is a technical problem, not a translation problem. European languages can run 25-35% longer than English. Design flexible layouts and run pseudo-localization tests before real translations begin.
• Continuous localization eliminates release lag. Treating translation as a waterfall step after each sprint creates growing backlogs. Integrating localization into your CI/CD pipeline keeps international releases in step with English.
• Localize for the locale, not just the language. Spanish for Spain and Spanish for Mexico are different locales with different vocabulary, date formats, and cultural conventions. Treating them as one produces content that feels generic to both.
• 76% of online shoppers prefer products in their native language — and 40% will never buy from websites in other languages ([CSA Research, 2020](https://csa-research.com/l/media/Consumers-Prefer-their-Own-Language)). An unlocalized product is not just a UX gap; it is a revenue gap.

Author

Quang Pham

Quang has spent the last 5 years as a UX and technical writer, working across both B2C and B2B applications in global markets. His experience translating complex features into clear, user-friendly content has given him a deep appreciation for how localization impacts product success.

When he's not writing, you'll likely find him watching Arsenal matches or cooking.