spaCy vs TextLens API
spaCy is a production-grade NLP pipeline for linguistic analysis. TextLens API covers the content quality side — readability grades, sentiment scoring, keyword extraction, and SEO metrics — from a REST endpoint that works in Python, Ruby, Go, or any language.
Join the waitlist ↓Side-by-side comparison
| Feature | spaCy | TextLens API |
|---|---|---|
| Named entity recognition (NER) | ✓ | ✗ |
| Part-of-speech tagging | ✓ | ✗ |
| Dependency parsing | ✓ | ✗ |
| Text classification (custom) | ✓ | ✗ |
| Readability scoring (8 formulas) | ✗ | ✓ |
| Consensus readability grade | ✗ | ✓ |
| Sentiment analysis | ✗ (requires custom model) | ✓ |
| TF-IDF keyword extraction | ✗ | ✓ |
| SEO scoring | ✗ | ✓ |
| Reading time estimate | ✗ | ✓ |
| Works in Ruby, Go, PHP | ✗ | ✓ |
| No model downloads (100MB–560MB) | ✗ | ✓ |
| No GPU or venv setup required | ✗ | ✓ |
| Free tier | Free (Apache 2.0) | 1,000 req/mo |
| All content metrics in one call | ✗ | ✓ |
The code
spaCy Python
# pip install spacy
# python -m spacy download en_core_web_sm (12MB–560MB)
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("Your text here...")
# Linguistic analysis
entities = [(ent.text, ent.label_) for ent in doc.ents]
tokens = [(t.text, t.pos_) for t in doc]
# No readability scores
# No content quality metrics
# No SEO scoringspaCy gives you deep linguistic structure. Model download required. No readability grades.
TextLens API Python
import requests
result = requests.post(
"https://api.ckmtools.dev/v1/analyze",
headers={"X-API-Key": "your_key"},
json={"text": "Your text here..."}
).json()
grade = result["readability"]["consensus_grade"]
sentiment = result["sentiment"]["label"]
keywords = result["keywords"]["top_5"]
seo = result["seo"]["score"]All content quality metrics in one HTTP call. No model download. Works in any language.
Different tools, different jobs
spaCy is world-class for linguistic structure: named entity recognition, dependency parsing, part-of-speech tagging. It powers production information extraction pipelines and nothing else comes close for those tasks. But spaCy is not designed for content quality metrics — there is no readability formula, no grade level estimate, no SEO scoring. The models range from 12MB (en_core_web_sm) to 560MB (en_core_web_trf).
TextLens API focuses on different questions: Is this text readable for your target audience? What grade level does it require? What keywords does it emphasize? How does it score for SEO quality? These are content metrics, not linguistic structure. It works as a REST endpoint — no model download, no venv, no language requirement.
When to use each
When to use spaCy
- Named entity recognition (people, organizations, locations, dates)
- Part-of-speech tagging and dependency parsing
- Building information extraction pipelines
- Custom ML model training on text
- Python-only projects where model download is acceptable
- High-volume local batch NLP processing
When to use TextLens API
- Readability scoring for content — spaCy has none
- Content quality metrics for blogs, docs, marketing copy
- Multi-language stack (Python + Ruby, Go, etc.)
- Serverless functions where 100MB+ model files hit size limits
- You want sentiment + keywords + SEO scoring in one call
Get Early Access
TextLens API is in development. Join the waitlist to get notified at launch.
From the team behind textlens — 96/week npm downloads.
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