This page breaks down Spark NLP ratings from all verified review sources to give you a clear, unbiased view of what real users think in September 2025.
We’ve aggregated the latest scores from all major platforms like Trustpilot, G2, Capterra and many more to help you understand Spark NLP’s overall reputation. This data-driven overview will show you exactly how Spark NLP is rated - including how it compares to competitors.

Spark NLP's Overall Rating Summary

  • Average Rating:4.8/5
  • Total Reviews:2 reviews
  • Sources:G2, Gartner
  • Last updated:September 1, 2025

Spark NLP Ratings by Source

PlatformRatingReviewsLast Update
G2 logoG2
4.5/5
1+ reviews03.03.25
Gartner logoGartner
5/5
1+ reviews01.09.25

What is Spark NLP?

Spark NLP is an open-source library under the Apache 2.0 license that delivers scalable Large Language Models (LLMs) natively on Apache Spark, supporting Python, Scala, and Java. It offers a wide range of NLP features, pre-trained models, and pipelines, including tokenization, sentiment detection, and various embeddings like BERT, RoBERTa, and GPT-2. Spark NLP is available on PyPI, Conda, and Maven, and integrates with ONNX, TensorFlow, and Spark ML functions, providing over 24,000 pre-trained models in 200+ languages.

🤔 Spark NLP vs Competitors: How Do Ratings Compare?

Check out Spark NLP Alternatives

Frequently Asked Questions

How trustworthy is Spark NLP?

According to Gartner and G2 review sites Spark NLP holds ratings of 5/5 and 4.5/5 respectively from 2 reviews, highlights excellent reputation and user approval as of September 2025.

How many stars is Spark NLP rated?

Spark NLP has an average star rating of 4.8 out of 5 stars based on average of 2 reviews as of September 2025.

Where does the rating data come from?

The ratings are pulled from Spark NLP public reviews pages on platforms: G2, Gartner. We update these ratings monthly to ensure they reflect the most current customer feedback.

How overall average rating for Spark NLP is calculated?

The overall average rating is calculated by taking the weighted average of all ratings, using the total reviews count from each platform as the weight.