150+ LANGUAGES · ISO 17100 CERTIFIED · SAME-DAY TURNAROUND
Translation Services for SaaS Companies
Translation services for SaaS companies: ISO 17100 UI, docs and marketing localization in 150+ languages, from £30 per page, integrated with your CI/CD.
- 150+ Languages
- ISO 17100 Certified
- Same-day Turnaround
Get a free, no-obligation quote
What we do
What are translation services for SaaS companies?
Translation services for SaaS companies are ISO 17100-certified localization of UI strings, documentation, marketing pages and support content in 150+ languages, delivered through a TMS/API workflow with translation memory and termbase reuse, priced in GBP from £30 per page.
How it works
What is the SaaS translation and localization process step by step?
The SaaS translation and localization process runs in eight steps: scope and content-class mapping, i18n audit, TMS or repo integration, string extraction, TM pretranslation, ISO 17100 translate-edit-proofread, in-context QA and pseudolocalization, then automated re-injection into the build via API or Git.
What’s included
What features and tools should SaaS translation services provide?
SaaS translation services should provide seven core capabilities: a translation management system, translation memory, a version-controlled termbase, connectors for GitHub/GitLab and headless CMS, machine translation pretranslation, pseudolocalization QA, and in-context preview for UI review.
Complete guide
Everything you need to know
UK SaaS companies buy translation as a continuous operations layer, not a document project — priced per word against release velocity, split across UI strings, documentation, marketing and support content, and only viable when the language partner integrates with the repository rather than the inbox.
Why should a SaaS company invest in professional translation and localization?
Professional translation unlocks non-English markets, lifts signup conversion on localized landing pages, satisfies GDPR and market-specific compliance, and prevents retrofit costs that hit teams who add i18n after launch. CSA Research found that 76% of online shoppers prefer to buy in their native language and 40% will not buy from English-only sites. That single data point sets the revenue case for localizing SaaS websites, UI and support. Compliance, brand consistency and engineering economics compound on top.
How much revenue does localizing a SaaS product unlock?
Localizing a SaaS product opens addressable markets where English-only signup pages under-convert. French, German, Spanish and Japanese buyers complete checkout at higher rates when pricing, UI and support appear in the local language. A localized landing page pairs the translated copy with market-specific keyword research, so paid and organic acquisition costs fall alongside conversion rising.
What compliance obligations force SaaS localization in the UK and EU?
Compliance obligations include UK GDPR privacy notices in the user’s language, EU consumer law on pre-contract information, and sector rules for fintech and health SaaS. Under the UK GDPR and the Data Protection Act 2018, information provided to data subjects must be intelligible. Terms of service must be understandable to bind the end user. Our Professional Business Translation Services page covers the wider contract and compliance workflow that sits alongside SaaS.
What does day-one internationalization save versus retrofitting later?
Day-one internationalization saves engineering rework, string audits and codebase-wide refactors. Teams that added i18n after launch have reported approximately $100K per year in translation alone at 18 languages, plus significant engineering effort to externalize hardcoded strings. Day-one architecture puts strings in resource files from the first commit, so incremental per-sprint spend replaces a lump-sum retrofit. Internationalization is the technical prerequisite for every downstream translation activity.
What content should a SaaS company prioritize for localization?
A SaaS company prioritizes four content classes for translation in order: product UI and in-app messages first, then technical documentation and help centre, then marketing landing pages and lifecycle emails, and finally legal, privacy and support macros. Each class has a different TEP intensity, reviewer profile and turnaround band.
| Content class | Reviewer profile | Typical turnaround | GBP band |
|---|---|---|---|
| UI strings, in-app messages | Product-domain linguist + pseudolocalization QA | Same-day for hotfixes; 24–48h standard | £0.10–£0.14 per word |
| Technical documentation | Technical writer as second linguist | 2–3 working days | £0.12–£0.18 per word |
| Marketing pages | Transcreator + local SEO reviewer | 3–5 working days | £0.16–£0.20 per word |
| Legal, privacy, support macros | Legal specialist under ISO 17100 | 2–3 working days | £0.14–£0.20 per word |
How is product UI and in-app string translation handled?
Product UI translation is handled by extracting resource files, running translation memory pretranslation, translating with domain-specialist linguists, applying pseudolocalization QA and re-injecting via API. The full extract–TM–TEP–reinject sequence is documented on our Software and App Localization in Manchester page. Supported files include JSON, XLIFF, .po and .resx.
How are SaaS documentation and knowledge-base articles localized?
SaaS documentation is localized under an ISO 17100 TEP workflow with a technical writer as reviewer, screenshots re-shot in the target locale, and a version-controlled termbase. API names, product features and error messages read identically across releases. Release notes and help centre articles ride the same translation memory, so recurring terms compound TM leverage over time.
How does marketing translation for SaaS websites differ from product localization?
Marketing translation prioritizes conversion copy, brand voice and local SEO over literal accuracy. Linguists transcreate landing pages, ads and lifecycle emails, then localize hreflang tags, sitemaps and keyword targets for indexation in new markets. Direct keyword translation loses local search volume — SaaS categories where the vocabulary has not solidified in a market need native keyword research, not word-for-word conversion. Our Website Localization in Leeds page covers the SEO deliverables in depth.
How is legal, privacy and support content translated for SaaS?
Legal, privacy and support content is translated by ISO 17100 legal specialists with jurisdiction awareness. Privacy policies map to UK GDPR and local data-protection rules. Terms of service are reviewed for enforceability. Support macros stay in a locked termbase so agent responses remain consistent across locales. The broader contract workflow lives on the Business Translation Services in London and the UK page.
How it works
What are the essential features and tools for SaaS localization?
SaaS translation services provide seven core capabilities: a translation management system, translation memory, a version-controlled termbase, connectors for GitHub, GitLab and headless CMS, machine translation pretranslation, pseudolocalization QA, and in-context preview. Together they form the translation platform behind continuous localization.
| Capability | Function | Typical tools |
|---|---|---|
| Translation management system | Central hub for files, linguists, QA | memoQ, Trados, Phrase |
| Translation memory | Reuse of prior segments | Built into the TMS |
| Termbase | Approved terminology enforcement | Version-controlled glossary |
| Repo connectors | Sync with product code | GitHub, GitLab, Bitbucket |
| Machine translation | On-commit pretranslation | Google Cloud Translation, DeepL API |
| Pseudolocalization | Surface layout and CJK/RTL bugs | TMS-native |
What role does a translation management system play?
A translation management system centralises string files, linguist assignments, TM leverage and QA in one platform. Our CAT stack covers memoQ, Trados and Phrase, with connectors into Lokalise and Crowdin where the client already runs a TMS. The best translation management setups treat the TMS as the single source of truth for every locale file. File format detail for XLIFF, .po and JSON lives on the Software and App Localization in Bristol page.
How do translation memory and termbase drive cost down release over release?
Translation memory charges fuzzy or repeated segments at a discount, and the termbase locks approved terminology into every future project. A SaaS shipping weekly typically sees 40–70% TM leverage after twelve months. Savings compound on recurring UI strings, release notes and marketing lifecycle content. Approved translations propagate across UI, docs and marketing without manual cross-checking.
What translation APIs and connectors are used?
Translation APIs include REST endpoints for headless CMS, Git connectors for repo-based string files, and webhook triggers for lifecycle content. Google Cloud Translation and DeepL API seed the machine translation layer where speed matters. TMS-native connectors handle push and pull between code and translation without CLI scripts on the client side.
How do AI translation and human translators work together for SaaS?
AI translation and human translators work together in a hybrid workflow: machine translation pretranslates strings on commit, human linguists post-edit under ISO 17100 TEP, translation memory captures the approved output, and the model improves against the domain termbase. The workflow follows ISO 18587, the standard for full human post-editing of machine translation output. Confidentiality controls, termbase enforcement and TEP traceability sit on top of the AI translator layer.
Is AI translation good enough for SaaS UI without human review?
AI translation suits internal tooling and low-visibility strings but not customer-facing UI. Brand voice, feature naming and legal terms need human review. Raw machine output produces awkward product terms, mistranslated CTAs and inconsistent terminology across screens. Ai-powered translation earns its place as the pretranslation layer, not the final layer.
When does full post-editing beat light post-editing?
Full post-editing beats light post-editing whenever the string is customer-facing, marketing-critical or legally binding. Full post-editing produces quality indistinguishable from human translation, per ISO 18587. Light post-editing suits internal dashboards, log messages and admin panels where speed matters more than polish. Full post-editing typically costs 15–30% less than full human translation on high-volume UI content.
Can SaaS teams use ChatGPT or general LLMs for translation?
SaaS teams can use general LLMs for first-pass drafts, glossary building and internal content, but not as the production translation layer. General LLMs lack termbase enforcement, ISO 17100 traceability and confidentiality controls that regulated SaaS content requires. AI tools sit inside the TMS under human-in-the-loop review, not as a standalone translation service.
What are the common challenges in SaaS localization?
Common challenges in SaaS localization are string sprawl across JSON files, terminology drift across product and marketing, right-to-left and Asian-language UI breakage, release cadence outpacing translation, and hidden retrofit costs. Each is solved by TMS centralization, termbase governance, pseudolocalization and CI/CD-connected continuous localization.
How is translation file management (JSON, XLIFF, .po) kept sane?
File management is kept sane by holding every locale file in the TMS as the single source of truth, diffing on commit, and blocking merges when required keys are missing. Nested JSON objects are parsed structurally so translators see logical groupings, not flat key lists. Version tags trace every string to a release.
How is terminology kept consistent across UI, docs and marketing?
Terminology is kept consistent through a shared, version-controlled termbase. A term approved on the English dashboard appears in its approved translated form in every language, every release. Product, docs and marketing draw from the same glossary. Enterprise SaaS termbase propagation across multi-tenant dashboards is detailed on our Software and App Localization in Cardiff page.
How are right-to-left and Asian-language interfaces handled?
RTL and CJK interfaces are handled by pseudolocalization QA before translation begins, then in-context review on the live UI. Arabic and Hebrew flip layouts. Japanese and Chinese compress character length. CJK line-height rules are validated by native reviewers on target-locale builds. Pseudolocalization surfaces truncation and layout bugs before a single word is translated.
How is scale handled when a five-language stack grows to eighteen?
Scale is handled by moving from spreadsheet-and-inbox workflows to a TMS with API connectors. What works at five languages breaks at eighteen. TM leverage, automated file diffing and per-language reviewer assignment become mandatory to keep unit cost flat. Manual handoffs stop scaling above ten languages.
Pricing
How does pricing work for SaaS translation services in GBP?
SaaS translation services cost from £30 per page under ISO 17100, priced per word for UI strings and per project for marketing pages, with a standard band of £0.10–£0.20 per word. Translation memory leverage discounts repeated segments by 30–70%. Rush surcharge of 30–50% applies to same-day and weekend delivery.
| Content type | Per-word band (GBP) | TM leverage discount |
|---|---|---|
| UI strings | £0.10–£0.14 | Up to 70% on repeats |
| Technical docs | £0.12–£0.18 | Up to 50% |
| Marketing transcreation | £0.16–£0.20 | Up to 30% |
| Legal and privacy | £0.14–£0.20 | Up to 40% |
How is per-word, per-project and retainer pricing structured?
Per-word pricing suits recurring UI strings, per-project suits fixed-scope marketing pages, and monthly retainers suit continuous localization. Retainers cap unit cost, guarantee linguist availability against release cycles, and cover TMS access, termbase management and QA. Standard turnaround is 2–3 working days at the base rate.
What does professional SaaS translation actually cost per year?
Annual cost varies sharply with language count and content velocity. A SaaS translating a full product plus marketing into 18 languages has reported approximately $100K per year. A five-language stack limited to UI and docs typically runs £15,000–£40,000 annually once TM leverage compounds after the first release.
How do purchase orders, framework agreements and retainers work?
Purchase orders cover one-off projects, framework agreements set locked rates across a fiscal year, and retainers reserve linguist capacity per sprint. All sit under NDA with UK GDPR data-processing terms. Volume pricing applies on annual commitments. Invoicing runs monthly on retainer engagements.
How do I choose the right translation partner or tool for my SaaS company?
Choose the right translation partner by scoring three axes: workflow fit (CI/CD and file-format support), quality controls (ISO 17100 TEP, termbase, native linguists), and commercial terms (GBP per-word bands, retainers, NDAs). The buyer-side decision is between an LSP retainer, a self-serve TMS or in-house AI tooling.
LSP with ISO 17100 vs self-serve TMS vs in-house AI — which model fits?
An ISO 17100 LSP fits regulated SaaS and marketing content where quality liability sits with the vendor. A self-serve TMS fits product-led teams comfortable managing linguists directly. In-house AI tooling fits high-volume internal content but carries hidden engineering, model-tuning and QA cost. Team size and language count decide the model.
| Model | Best for | Typical cost signal |
|---|---|---|
| LSP retainer | Regulated content, marketing, 5+ languages | Per-word + monthly fee |
| Self-serve TMS | Product-led teams, 2–8 languages | Platform subscription + freelance |
| In-house AI | Internal-only, high-volume | Engineering + LLM inference cost |
Which SaaS translation tools compete with a managed service?
SaaS translation tools competing with a managed service include Lokalise, Crowdin, Phrase, DeepL API and Google Cloud Translation. Each solves platform mechanics but still needs qualified linguists, terminology governance and QA for customer-facing content. A translation tool without a linguist layer produces machine output, not release-ready localization.
What ISO 17100, security and data-residency questions should procurement ask?
Procurement should ask for the ISO 17100 certificate number, evidence of native-linguist qualification, the TEP audit trail, UK GDPR data-processing terms with UK or EEA data residency, an NDA for source files, and penetration-test posture for the TMS. Our certifications are ISO 17100, ISO 9001, ISO 27001 and ISO 4043.
How is non-English SEO handled when localizing SaaS websites?
Non-English SEO is handled by pairing marketing translation with hreflang tags, per-locale sitemaps, market-specific keyword research and localized metadata. Translation alone does not get pages indexed. Crawlability, canonicalization and local link signals do. Deliverables include hreflang mapping across locales, per-market keyword research replacing direct keyword translation, localized titles and meta descriptions, and localized URLs. SaaS categories with unsettled local vocabulary need native keyword research from the linguist, not a lookup in Google Cloud Translation.
What are the future trends in SaaS translation and localization in 2025?
Future trends in SaaS translation in 2025 include LLM-based translation replacing statistical MT, on-commit machine translation with human post-editing, TMS-native AI translators, tighter GitOps integration, and richer terminology-and-translation reasoning. The direction of travel points at localization workflows where the AI translator explains why a term was chosen against the brand voice, the termbase auto-updates from approved edits, and continuous localization runs on every commit rather than every sprint. Successful SaaS teams treat translation as a release-blocking check, not a post-launch task.
How do you engage translation services for a SaaS company in the UK?
Engage translation services for a SaaS company in the UK by scoping the four content classes, sharing sample resource files for a TM analysis, agreeing a per-word or retainer rate under a framework agreement, and connecting the TMS to your repo. Same-day turnaround is available on urgent updates from £30 per page. Standard turnaround is 2–3 working days at the base rate.
Which languages are covered for SaaS localization?
SaaS localization covers 150+ languages including French, German, Spanish, Italian, Polish, Arabic, Simplified and Traditional Chinese, Japanese, Korean and Portuguese. Native linguists are based in-market. CJK and RTL layout QA runs on target-locale builds. Every language pair follows the ISO 17100 TEP workflow with a second linguist.
What turnaround can a SaaS team expect?
A SaaS team expects same-day turnaround on urgent UI hotfixes, 24–48 hours for standard sprint volumes, and milestone delivery for projects above 10,000 words. The 1,500–2,000 finished words per linguist per day benchmark makes sprint planning predictable. Rush surcharge of 30–50% applies to same-day and weekend delivery.
Translation services for SaaS companies start from £30 per page under ISO 17100, with a per-word band of £0.10–£0.20 depending on content class. Translation memory leverage discounts repeats by up to 70% after the first release cycle.
Frequently asked questions
How do I handle localization/translation efficiently for a SaaS app without it becoming a maintenance nightmare?
The key is connecting your codebase directly to a translation management system (TMS) via API or webhook so new and updated strings flow automatically to translators without manual file handling. Adopt a structured file format (JSON, YAML, or XLIFF) from the start, enforce string freeze policies before each release, and use translation memory to avoid re-translating unchanged segments — typically cutting per-release costs by 30–60% on mature products. Pair this with a termbase so product-specific terminology stays consistent across every language and every update cycle.
How much does professional SaaS translation actually cost per year?
Annual spend varies enormously depending on word volume, language count, and content type, but a mid-size SaaS localising UI strings, help-centre articles, and marketing copy into 5–10 languages typically budgets £30,000–£150,000 per year with a professional LSP. Per-word rates for technical SaaS content run roughly £0.10–£0.18 per word for an ISO 17100 translate-edit-proofread workflow; translation memory leverage and volume agreements can reduce effective rates significantly. Many vendors offer retainer or framework agreements that provide cost predictability and priority capacity.
Should I think about internationalization from day one or can I add it later?
Internationalising from day one is strongly recommended — retrofitting i18n into an existing codebase is significantly more expensive and disruptive than building it in at the start. Hardcoded strings, date/number formats, right-to-left layout assumptions, and database character encoding are all much cheaper to handle upfront. Even if you only launch in English initially, externalising strings into resource files (e.g. JSON with i18next) costs little extra during development and removes a major bottleneck when you’re ready to enter new markets.
Is AI translation good enough for SaaS products or do you still need human review?
AI/machine translation (MT) output is not yet reliable enough to publish without human post-editing for customer-facing SaaS content. Raw MT can handle high-level comprehension but routinely fails on UI microcopy, error messages, legal terms, and brand voice — errors that directly affect user trust and compliance. ISO 17100 mandates human translation and editing as the quality baseline for professional work; light post-editing of MT is a valid cost-reduction step, but zero human review is a reputational risk. For internal or low-stakes content, MT-only may be acceptable with clear sign-off.
How do I get my translated pages indexed in Google Search for non-English markets?
Use hreflang annotations (in the HTML head, HTTP headers, or sitemap) to signal to Google which language/region each URL targets, and serve translated content on dedicated URLs — subdirectories (/fr/), subdomains (fr.example.com), or ccTLDs — rather than via JavaScript that crawlers may not render. Each localised page must contain genuinely translated content, not just machine-translated placeholder text, as thin or duplicate content can suppress indexing. Submit locale-specific sitemaps in Google Search Console and ensure server response times are acceptable in target regions.
Can I use Google Translate for initial strings and then have humans review?
Yes — using machine translation as a first pass followed by human post-editing (MTPE) is a recognised and cost-effective workflow, provided the human edit is substantive rather than superficial. Google Translate and neural MT engines like DeepL can produce usable drafts, but SaaS UI strings, onboarding flows, and help content typically require significant correction for tone, terminology consistency, and platform conventions. A professional LSP can integrate your preferred MT engine into a post-editing workflow with quality assurance checks, translation memory, and termbase alignment to maintain consistency at scale.
How much do translators charge per 1000 words?
Professional translators working into European languages typically charge £90–£180 per 1,000 words for technical or SaaS-related content under an ISO 17100 workflow (translate + edit). Rates vary by language pair — rare or complex languages such as Japanese, Korean, or Arabic sit toward the higher end — and by specialism; legal, medical, and financial content commands a premium. Volume discounts, translation memory leverage, and framework agreements with an LSP can reduce effective per-1,000-word costs materially on ongoing programmes.
How do I manage translation files (JSON, etc.) across a large codebase without going crazy?
Centralise all string files in a dedicated /locales directory and integrate them with a TMS (such as Phrase, Lokalise, or Crowdin) that syncs via API or CLI on each build. Enforce key naming conventions and a linting step in CI/CD to catch missing or duplicate keys before they reach translators. Use translation memory and locking to prevent already-approved strings from being re-submitted unnecessarily. A professional LSP with TMS/API integration can receive and return files automatically, removing the manual handoff bottleneck that causes version drift in large codebases.