Naver Papago vs Apinoa Image Translation — Same Quality, Up to 24× Cheaper?
If you translate 1,000 Chinese product images into Korean every month, Naver Papago Image Translation API costs around ₩120,000 (~$85). About what your team spends on lunch.
Bump that to 10,000 images and you're at ₩1.2M (~$850) — half a junior dev's salary, every month, just for OCR + translation + inpainting.
Is that the right price? And — more importantly — is the workflow flexibility you get worth that price?
Price is half the problem. Workflow rigidity is the other half.
If your team runs its own LLM translation pipeline, Papago is heavier than you need. There's no way to get just the OCR results — translation and final rendering come bundled. One package, one outcome.
Want a designer to take an inpainted (text-removed) image and overlay text in their own font? Papago doesn't return the inpainted intermediate.
Want to render with your brand's custom font? System fonts only.
These three needs — OCR-only, inpainted-only, my-font-only — are real friction points for serious e-commerce, design, and tooling teams. The moment any one of them matters, Papago stops being the answer.
Apinoa is the answer — 8–24× cheaper, three modes Papago can't do
Apinoa Image Translation does the same job as Papago at 8–24× lower cost. It also exposes the three workflow modes mentioned above:
- OCR-only → pair with your own LLM for translation
- Inpainted image + translated text + bounding boxes (separated) → designer post-processing
- Render with a user-uploaded font → brand consistency (Pro tier and up)
Pricing — 5-tier subscription
Apinoa uses usage-based subscription tiers, not per-call billing.
- Starter — $20/mo / 1,800 calls / ₩15.5 per call / ~8× cheaper than Papago
- Pro — $80/mo / 10,000 calls / ₩11.2 per call / ~11× cheaper
- Scale — $200/mo / 35,000 calls / ₩8.0 per call / ~15× cheaper
- Business — $300/mo / 84,000 calls / ₩5.0 per call / ~24× cheaper
- Enterprise — contact sales / custom volume
Real-world savings
- 1,000 images/mo → Papago ~₩120,000 ($85) vs Apinoa $20 (₩28,000) — ~4× savings
- 10,000 images/mo → Papago ~₩1.2M ($850) vs Apinoa $80 (₩112,000) — ~11× savings
- 30,000 images/mo → Papago ~₩3.6M ($2,570) vs Apinoa $200 (₩280,000) — ~13× savings
- 84,000 images/mo → Papago ~₩10M ($7,200) vs Apinoa $300 (₩420,000) — ~24× savings
Papago charges ₩120/call + character-count overage. The simulation above counts only the per-call portion — actual savings are larger.
→ Run the savings calculator on the pricing page
Feature comparison
What Apinoa offers that Papago doesn't
- OCR-only endpoint (POST /image-translate/ocr)
- Inpainted image + translated text separated (?render=false)
- User-uploaded font rendering (Pro tier and up)
- 9-stop font-weight control (Thin to Black)
Both engines support
- Full e2e translation
- auto-detect language
- JPEG / PNG input
Where Papago wins
Papago is faster on a single uncached call. ~1.2 s vs ~3.2 s. On repeat requests Apinoa drops below 100 ms via cache, and the ability to split OCR / translation / rendering across separate calls usually wins net workflow time.
The three modes Papago can't do
1. OCR-only — for teams running their own LLM translation
httpPOST /image-translate/ocr Content-Type: image/jpeg X-Api-Key: apinoa_xxx → { "regions": [ {"text": "三种灵动色彩", "bbox": [120, 80, 580, 140], "confidence": 0.98} ], "regions_found": 6 }
Apinoa for OCR, GPT-4 / Claude / Gemini for translation. Useful when you have a domain glossary or need control over the translation tone.
2. Inpainted image + translated text + coordinates — for designer post-processing
httpPOST /image-translate/translate?render=false Content-Type: image/jpeg X-Api-Key: apinoa_xxx → { "inpainted_base64": "iVBORw0KGgo...", "regions": [ { "text": "三种灵动色彩", "text_en": "Three vibrant colors", "bbox": [120, 80, 580, 140] } ] }
You get a clean inpainted image (inpainted_base64), the translated text, and bounding boxes — separately. Designers can compose with their own font and layout, or generate multiple A/B variants on the same background.
3. User-uploaded font rendering — for brand consistency (Pro tier and up)
httpPOST /image-translate/translate?font=user/{font_id} Content-Type: image/jpeg X-Api-Key: apinoa_xxx → <binary JPEG rendered with your brand font>
Upload your brand font once, every translated image renders with it. Critical for fashion, beauty, and F&B brands where font consistency is part of the brand asset.
Quality — both engines are roughly equal
The "8–24× cheaper" headline raises a fair question: is the quality 8–24× worse? We tested both engines on identical Chinese product images, and the answer is no — they're roughly equal.
Where Apinoa reads more naturally — marketing copy
Source Chinese: "三种灵动色彩 / 满足不同需求的你":
- Apinoa: "Three vibrant colors / For your diverse needs" (concise marketing tone)
- Papago: "Three dynamic colors to meet different needs of you" (literal)
Where Papago reads more naturally — long structured text
For long, table-structured notices like Spring Festival logistics announcements, Papago renders slightly more naturally. Marketing copy and long structured text play to different engines' strengths.
Which engine fits which team
Papago is the right fit if you
- Process under 100 images/month — price gap is negligible
- Don't run your own LLM or design pipeline
- Need a final rendered image and nothing more
Apinoa is the right fit if you
- Process 1,000+ images/month — the gap widens to 4–24×
- Want OCR-only because you have your own translation engine
- Want designers to handle the final composition
- Need brand-font consistency
- Sell in markets where marketing copy tone matters (cosmetics, fashion, F&B)
If you're under 100 images a month, stay on Papago. Otherwise, give Apinoa a free trial.
Closing
Bringing image-translation cost down 24× changes what your team can actually do with it. A/B copy testing, multi-language expansion, design-system consistency — work that was previously gated by per-call cost suddenly becomes table stakes.
Add OCR-only / inpainted-only / brand-font modes on top, and Apinoa fits naturally into the workflows of teams that already run their own LLM translation and design tooling.
💬 Talk to us about Enterprise / high-volume
Result samples
A few before/after pairs from our test suite, picked because they show the OCR + render path on generic product imagery:
