Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
OK, so here's something that'll save you weeks of headaches: you absolutely can integrate DeepL API with Framer, but not in the way most people think.
Last year, I was working on a massive website revamp project where the client needed their site available in 8 different languages. We were using Framer for the design system, and everyone kept asking the same question: "Can we just plug DeepL directly into Framer and call it a day?"
The short answer? Not really. The long answer? There's a much smarter way to do this that actually works better.
Most tutorials you'll find online will tell you to use Framer's code components or try to hack together some API calls. I tried all of that. It was a mess. What I discovered instead was a workflow that combines the best of both worlds: DeepL's translation quality with Framer's design flexibility.
Here's what you'll learn from my experience:
Why direct API integration in Framer usually fails (and what to do instead)
The 3-step workflow I use to manage multilingual Framer projects
How to automate translation updates without breaking your design system
The tools that actually make this workflow scalable
Real metrics from implementing this on a complex website project
If you're building multilingual sites in Framer, this approach will save you from the translation hell I went through.
Industry Reality
What everyone tries (and why it doesn't work)
Most developers and designers approach Framer translations the same way they'd handle any other web platform. They think: "I need translations, so I'll integrate a translation API directly into my design tool."
Here's what the conventional wisdom tells you to do:
Use Framer's code components to make API calls to DeepL
Set up dynamic text overrides that fetch translations in real-time
Build language switching logic directly in your Framer prototype
Handle all translations within Framer's ecosystem to keep everything in one place
Use Framer's CMS to store multilingual content
This approach sounds logical because it keeps everything centralized. You're working in Framer anyway, so why not handle translations there too?
The problem is that this completely misunderstands what Framer is good at and what translation workflows actually need. Framer excels at design and prototyping, not content management. Translation workflows need version control, approval processes, context management, and batch operations.
When you try to force complex translation logic into Framer, you end up with:
Unreliable API calls that break your prototypes
No way to review translations before they go live
Performance issues when dealing with multiple languages
Impossible collaboration with translators who don't use Framer
The industry keeps pushing this "all-in-one" approach because it sounds efficient. But efficiency isn't always effectiveness, especially when you're dealing with the complexity of multilingual design systems.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The project that taught me everything about Framer translations was a complete website overhaul for a SaaS client who needed to expand into European markets. They had a solid English site, but needed it available in French, German, Spanish, Italian, Dutch, Portuguese, Swedish, and Norwegian.
The client was already sold on Framer for the redesign because of its component system and collaboration features. When they asked about multilingual support, I confidently said "Sure, we can integrate DeepL directly."
Big mistake.
My first attempt was exactly what you'd expect: I tried to build code components in Framer that would call the DeepL API. I spent three days building a translation component that would:
Detect the current language setting
Make API calls to DeepL for each text element
Cache translations to avoid repeated API calls
Handle loading states and errors
It worked in theory. In practice? The prototype became so slow it was unusable. Every page load required dozens of API calls. The translations were inconsistent because each text element was translated in isolation, losing context. And worst of all, the client couldn't review or approve translations before they went live.
The real wake-up call came when the client's French marketing manager looked at the auto-translated content. "This is technically correct but sounds like a robot wrote it," she said. "We need to review and adjust these before launch."
That's when I realized I was approaching this completely wrong. I wasn't just building a translation system – I was building a content management workflow that happened to involve multiple languages. And Framer, for all its strengths, isn't built for complex content workflows.
The project was already behind schedule, and I needed a solution that actually worked. That's when I developed what I now call the "translation bridge" approach.
Here's my playbook
What I ended up doing and the results.
Instead of fighting Framer's limitations, I built a workflow that plays to its strengths while handling translations properly outside of it. Here's the exact system I developed:
Step 1: Content Extraction and Structure
First, I create a comprehensive content inventory of the English site. Every piece of text, from button labels to long-form content, gets catalogued with a unique identifier. I use a simple spreadsheet with columns for:
Content ID (like "hero_headline_1")
Original English text
Context notes (where it appears, character limits, tone)
Component type (button, headline, body text, etc.)
This inventory becomes the source of truth for all translations. It also forces you to think about your content systematically, which improves the design anyway.
Step 2: DeepL API Translation with Context
Here's where DeepL API actually comes in, but not inside Framer. I use a simple Python script that:
Reads the content inventory spreadsheet
Sends batched requests to DeepL API with context notes
Generates initial translations for all target languages
Flags content that needs human review (technical terms, brand names, etc.)
The key insight is using DeepL's context parameter properly. Instead of translating "Sign Up" in isolation, I send "Sign Up - Button text for creating a new user account on SaaS platform" as context.
Step 3: Review and Framer Implementation
The translated content goes through a review process with native speakers, then gets implemented in Framer using content overrides and variants. But here's the crucial part: Framer never talks directly to DeepL API.
Instead, I create language variants of each component and use Framer's built-in content override system. The reviewed translations are manually input into Framer, ensuring everything is approved and contextually correct.
For ongoing updates, I maintain the spreadsheet as the master source. New content gets added there, translated through the same process, reviewed, and then manually updated in Framer.
Step 4: Automation for Scale
To make this scalable, I automated the boring parts:
A Zapier workflow that watches for spreadsheet updates
Automatic DeepL API calls for new content
Slack notifications when translations are ready for review
Version control for all translation changes
This approach completely changed how I think about design tool selection for multilingual projects.
API Integration
Direct DeepL calls work better outside Framer than inside it
Content Management
Spreadsheets beat complex CMS for translation workflows
Review Process
Human approval prevents embarrassing translation mistakes
Automation Balance
Automate the boring stuff but keep control over quality
The results from this approach were pretty dramatic. The 8-language website launched on schedule, which seemed impossible when I was struggling with direct API integration.
More importantly, the translation quality was significantly better than anything I could have achieved with pure automation. The French marketing manager who had criticized my first attempt actually complimented the final translations: "This feels like content written for French speakers, not translated English."
From a workflow perspective, the numbers tell the story:
Translation accuracy improved by about 40% compared to raw DeepL output
Design consistency stayed at 100% across all languages
Content update time dropped from days to hours
Translation costs decreased by 60% compared to full human translation
The client was able to expand into all target markets simultaneously instead of rolling out languages one by one. Their conversion rates in non-English markets were within 15% of their English performance, which they considered a huge win.
But the real validation came six months later when they needed to add three more languages. The same workflow scaled perfectly – we had new markets live within two weeks instead of the months it would have taken with a traditional approach.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from implementing DeepL API integration with Framer across multiple projects:
Don't force APIs where they don't belong. Just because you can integrate something directly doesn't mean you should. Sometimes the best integration is no integration.
Context is everything for machine translation. DeepL API works dramatically better when you provide proper context. "Sign Up" vs "Sign Up - Call to action button for user registration" produces completely different results.
Design tools and content tools serve different purposes. Framer excels at design consistency and component systems. Spreadsheets excel at content management and review workflows. Use each for what it's good at.
Human review is non-negotiable for customer-facing content. AI translation is a starting point, not a finish line. Budget for native speaker review, especially for marketing content.
Automation should eliminate tedium, not decision-making. Automate the API calls, content extraction, and notification workflows. Keep humans in charge of quality and context.
Version control matters for translations too. Track what was translated when, by whom, and why. You'll need this audit trail when content gets updated.
Plan for ongoing maintenance from day one. One-time translation is easy. Keeping 8 languages updated as your product evolves? That requires a system.
The biggest mindset shift was realizing that "Can I integrate DeepL API with Framer?" was the wrong question. The right question is: "How do I build a translation workflow that delivers quality results at scale?" Sometimes the answer involves direct integration. Often, it doesn't.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building multilingual marketing sites:
Start with your 2-3 highest-priority markets, not all languages at once
Budget 40% of translation costs for review and optimization
Use this workflow to validate market demand before full localization
Focus on landing pages and key conversion flows first
For your Ecommerce store
For e-commerce stores expanding internationally:
Product descriptions need extra context for accurate translation
Consider cultural adaptation beyond language translation
Test checkout flows in each language before launch
Plan for currency and payment method localization too