If you're using the Google Translate API or DeepL API in production, you're probably paying $20 per million characters. For a SaaS product that translates user-generated content, product descriptions, or documentation, that adds up fast.
A mid-size e-commerce platform translating 50 million characters per month? That's $1,000/month just for translation. A localization pipeline processing 200 million characters? $4,000/month. And that's before you factor in the engineering time to handle rate limits, retries, and API quirks.
We built Langbly specifically to solve this problem: higher-quality translation at a dramatically lower price point. Here's a transparent breakdown of how translation API pricing actually works, why the incumbents charge what they do, and how we deliver better results for less.
The Current Translation API Pricing Landscape
Let's start with what the major providers charge in 2026:
| Provider | Price per 1M Characters | Free Tier | Minimum Commitment |
|---|---|---|---|
| Google Translate API v2 | $20.00 | 500K chars/month | Pay-as-you-go |
| Google Translate API v3 (Advanced) | $20.00 (base) + model fees | 500K chars/month | Pay-as-you-go |
| DeepL API Free | Free (limited) | 500K chars/month | None |
| DeepL API Pro | $20.00 | None | $5.49/month base |
| Amazon Translate | $15.00 | 2M chars/month (12 months) | Pay-as-you-go |
| Microsoft Translator | $10.00 | 2M chars/month | Pay-as-you-go |
| Langbly | $1.99–$3.80 | 500K chars/month | Free tier or $19/month |
The price difference is stark. At scale, Langbly costs 81–90% less than Google Translate or DeepL, and 73–87% less than Amazon Translate. See our full pricing page for current plans and volume tiers.
Why Are the Incumbents So Expensive?
Google, DeepL, and Amazon all built their translation engines using Neural Machine Translation (NMT), custom-trained models that require massive infrastructure to serve. (For a deeper look at how NMT compares to modern translation approaches, read our translation technology deep dive.) These models are served on dedicated GPU clusters that run 24/7, regardless of traffic.
The pricing reflects several cost layers:
- Model training costs: NMT models are trained on billions of parallel sentence pairs. Acquiring, cleaning, and aligning this data is expensive, often involving human translators for quality validation.
- Infrastructure costs: Dedicated inference clusters with low-latency requirements. Google and DeepL operate their own ML infrastructure at global scale.
- R&D amortization: These companies have invested hundreds of millions in translation technology over the past decade. The per-character pricing reflects that investment.
- Enterprise positioning: At $20/1M chars, translation APIs are positioned as enterprise tools. The pricing acts as a signal of quality and reliability.
But here's the key insight: the cost of delivering a translation has dropped dramatically, while the pricing hasn't kept up.
How Next-Generation Translation Changes the Cost Equation
Langbly uses advanced AI models instead of custom-trained NMT models. This fundamentally changes the cost structure:
1. No Custom Model Training
We don't train our own translation model from scratch. Instead, we use frontier AI models that have already been trained on trillions of tokens across hundreds of languages. The translation capability is a byproduct of general language understanding, so we don't pay to build it from scratch.
2. Shared Infrastructure
AI inference providers operate at massive scale across all use cases (coding, writing, analysis, translation). We benefit from infrastructure that's already been built and optimized for millions of users, rather than maintaining dedicated translation-only servers.
3. Competition Drives Down Inference Costs
The AI inference market is intensely competitive. Major providers are all competing to offer the lowest cost per token. This competition directly benefits us, and our customers.
Our current cost per million translated characters: approximately $0.10. That gives us enormous margin even at $1.99/1M characters, while still being 90% cheaper than Google Translate.
Real Cost Comparison: Monthly Scenarios
Let's look at what this means for actual businesses:
| Monthly Volume | Google Translate | DeepL API Pro | Langbly (Growth) | Savings vs Google |
|---|---|---|---|---|
| 5M characters | $100 | $105.49 | $19 | 81% |
| 25M characters | $500 | $505.49 | $69 | 86% |
| 100M characters | $2,000 | $2,005.49 | $199 | 90% |
| 200M characters | $4,000 | $4,005.49 | $199 + $400 overage | 85% |
At 100 million characters per month (a volume that many localization platforms, e-commerce sites, and content management systems easily reach), you'd save $1,800 per month ($21,600 per year) by switching from Google Translate to Langbly.
But What About Quality?
Cost savings mean nothing if translation quality drops. The usual expectation is that cheaper = worse. But with advanced context-aware translation, the opposite is true.
Modern AI translation consistently outperforms NMT engines in several areas:
- Natural phrasing: Langbly translations read like they were written by a native speaker, not translated by a machine. They handle idioms, colloquialisms, and cultural references that NMT engines translate literally.
- Register awareness: Langbly understands when to use formal vs. informal language. This matters especially for languages like Dutch, German, and French where the wrong register sounds unnatural.
- Context handling: Langbly processes the full meaning of a text, not just sentence-by-sentence patterns. Ambiguous words get the right translation based on context.
- Locale formatting: Langbly automatically applies locale-specific formatting (decimal separators, date formats, currency conventions), matching target language norms. NMT engines preserve source formatting regardless of locale.
We run an automated evaluation suite with 160+ test cases across 38 categories, including idioms, technical terminology, marketing copy, and edge cases like placeholders and HTML tags. This catches regressions before they reach production.
The Trade-Off: Latency
There is one area where NMT engines still have an advantage: speed. Google Translate typically responds in 50–200ms. Langbly's advanced approach takes 500ms–3 seconds depending on text length.
For most use cases (localizing UI strings, translating product descriptions, processing content batches), this difference is negligible. You're calling the API from a backend service, not blocking a user interface.
Where latency matters (real-time chat translation, live subtitle generation), NMT may still be the better choice. But for the vast majority of translation API usage, the extra 300–2,000ms is invisible to end users.
Drop-In Migration: Zero Engineering Effort
Langbly's API is 100% compatible with the Google Translate v2 API. Same request format, same response format, same error codes. Switching requires changing exactly one thing: the base URL. Check the API documentation for a step-by-step migration walkthrough.
Before:
POST https://translation.googleapis.com/language/translate/v2
After:
POST https://api.langbly.com/language/translate/v2
That's it. Your existing code, SDKs, and integrations continue working. No schema changes, no new authentication flows, no migration period. We also provide official Python and Node.js SDKs if you prefer a dedicated client.
Who Benefits Most from Switching?
Based on the volume tiers where savings are largest, these use cases see the biggest impact:
- SaaS platforms with multilingual features: Translating user interfaces, notifications, and user-generated content. Volumes grow linearly with your user base, so per-character cost matters enormously.
- E-commerce with international catalogs: Product titles, descriptions, and reviews across multiple languages. A 10,000-product catalog in 5 languages can easily hit 50M+ characters per month.
- Content management and publishing: Blog posts, documentation, help centers. Continuous content creation means continuous translation spend.
- Localization pipelines: Tools that connect with Crowdin, Lokalise, Phrase, or Transifex for continuous localization. High volume, recurring spend.
- WordPress and CMS plugins: Translation plugins like Loco Translate, TranslatePress, and WPML that call external APIs for machine translation. Lower per-character costs directly benefit end users.
What About Enterprise Volumes?
For volumes above 100 million characters per month, we offer custom Enterprise pricing. The economics scale even further, because our cost structure allows aggressive pricing at high volumes because AI inference costs continue to drop quarter over quarter.
Contact us at hello@langbly.com for custom Enterprise pricing.
The Bottom Line
Translation API pricing has been stagnant for years. Google and DeepL charge the same $20/1M characters they've charged since launching their APIs. Meanwhile, the underlying technology has shifted. Advanced AI models now produce better translations at a fraction of the inference cost. Want to see how the major providers stack up? Check our translation API comparison.
Langbly passes that cost reduction to customers: $1.99–$3.80 per million characters, with a free tier of 500K characters per month to test the quality yourself. No long-term contracts, no minimum commitments, and a drop-in compatible API that takes minutes to switch.
The question isn't whether to switch; it's how much you're willing to keep overpaying.
Related Articles
- LLM vs Neural Machine Translation: Why Advanced Models Produce Better Translations. A technical comparison of modern and NMT architectures, with real translation examples showing where advanced models excel.
- Google Translate API for Developers: A Complete Guide (2026). Setup instructions, code examples, rate limits, and when to consider switching to an alternative.