Spark NLP vs spaCy

Side-by-side — pricing, strengths, tradeoffs, and fit.

Spark NLP

Distributed NLP library for Apache Spark ML

spaCy

Industrial strength NLP with Python and Cython.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

Distributed NLP library for Apache Spark ML

One-liner

Industrial strength NLP with Python and Cython.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Teams processing massive datasets that require distributed computing
Developers building NLP applications on top of Apache Spark ML
Organizations needing scalable and performant NLP solutions

✓ Best for

Developers building high-performance NLP applications who need speed and accuracy
Data scientists working with large text datasets requiring efficient processing
Teams needing a robust, well-documented library for various NLP tasks

Not ideal for

✕ Not for

Projects with small datasets where distributed computing is not necessary
Users looking for a cloud-based managed service without self-hosting

✕ Not for

Projects that require real-time streaming capabilities (spaCy is batch-oriented)
Applications where the primary focus is on graphical user interfaces rather than backend processing

Strengths

↑ Strengths

Distributed processing with Apache Spark ML
High performance and accuracy in NLP tasks
Scalability for large datasets
Pre-trained models available out-of-the-box

↑ Strengths

High-speed processing for NLP tasks
State-of-the-art accuracy in various NLP models
Efficient model training and deployment
Comprehensive documentation and community support

Weaknesses

↓ Weaknesses

Limited direct support for Python users
Performance overhead due to Apache Spark integration
Resource-intensive setup and maintenance

↓ Weaknesses

Limited pre-trained models for non-English languages
Complex setup for custom pipelines and models
Full Spark NLP profile →Full spaCy profile →