Last updated on November 18th, 2025 at 12:17 pm
You know, I have spent too much weekend trying to prove instruments that make everything promising but who, in the reality, did not offer me anything. This is why when I chose to sample open source NLP APIs, I did so with skepticism. Surprisingly, even some of them really live to the hype.
This is what I was able to come across after constructing five projects using five different frameworks.
Table of Contents
Why I So Much as Tested These.
I had to create sentimental analyzer of user reviews. Simple enough, right? All paid APIs that I wanted to test had minimum price of 500/month. This is when I came across the open-source world of natural language processing.
The catch? It can be dozens of options, half of the tutorials available on the internet are outdated or simply erroneous.
Test 1: Hugging Face Transformers – The Swiss Army Knife.
Firstly, there was Hugging Face Transformers. It is getting all the hype, so I figured there to begin.
What I was testing: Sentiment analysis of 1, 000 product reviews.
The installation: Not as difficult as anticipated. One pip install, 3 lines of code, and it was a working model. They have more than 130,000 ready-to-use pre-trained models literally lying around.
What was successful: Pipeline API is dumb simple. I decreased to zero and in about 15 minutes, I analyzed text. Besides it supports 100+ languages by default.
The exasperation: It is RAM-hungry. The laptop fan had turned on to sound like an aircraft starting to take off. When you are running it on a simple machine, there will be some lag.
Honest conversation: Ideal prototyping. Not especially good when you are deploying something which requires to be fast.
Test 2: spaCy – The Speed Demon
And the second package I tried was spaCy since a person on Reddit claimed this was production-ready (whatever that is).
What I tried: Named entity recognition of news articles.
The initial experience: The set up was slower than Hugging Face however, the documentation is indeed readable. That’s rare.
What worked: holy speed. spaCy was able to process 5,000 articles in the time Hugging face made it 500 articles. It is real world optimised and you can touch it.
The trap: Fewer ready-made models than Hugging Face. What they provide or training your own, is mostly what you are working with.
My verdict: This is the place when you have to get something in a short period of time and trust it. I found myself using this in my production application.
Test 3: NLTK – The Old Reliable
NLTK has been around forever. I was interested to know whether it would still be relevant in 2025.
What I was testing: Text simplification and tokenization.
The atmosphere: Like a library in 2010 – because that is what it is. But here’s the thing: it works.
What worked: Excellent learning experience. The tutorials are enormous and you know what is going on in the background. Plus, it’s lightweight.
What didn’t: Slow. Really slow. And the API is bulky against the modern tools.
Whom are we addressing: NLP students or anybody learning the basics of NLP. Not to play with till you love to suffer.
Test 4: Spark NLP – The Multilingual Powerhouse.
I tried Spark NLP simply because one of my friends has been pushing me to use it over and over again, claiming that it is an underestimated service.
What I got to test: Translation and multilingual text analysis.
The surprise: It works with 200+ languages and 10 000 models. That’s insane. I hurled random languages at it Swahili, Vietnamese, Turkish and it dealt with them.
What was successful: This is the best bet when you are developing something international. The support with multiple languages is not a sham.
Learning curve: Sh Steeper than the others. There is documentation but it is not that user friendly.
Bottom line: It would be overkill in most projects, but it would be ideal where serious language coverage is required.
Test 5: Gensim – The Niche Expert
The last was Gensim, which I heard was suitable to topic modeling.
My test: Data similarity and topic clustering in blog posts.
When it worked: In a similar way, Gensim is designed to work with embeddings or topic modeling. There is no mess in Word2Vec implementation.
What didn’t: Limited scope. It does not aim to be all as well as it simply does great things.
My conclusion: It can be applied to certain tasks, such as similarity of documents. Do not think you are going to get NLP magic.
Which one, then, then should you actually be using?
This is my sincere piece of advice after having wasted (invested) all those weekends:
Starting out? Go with Hugging Face. It is worth the community support as such.
Constructing something real? spaCy. Speed is an issue of more than it seems.
Learning NLP concepts? NLTK. It has to do with going to the gym, which is a suffer but an educative experience.
Going global? Spark NLP, no question.
Doing topic stuff or embeddings? Gensim manages it in a better way than the rest.
The fact is that most of these open source NLP APIs have other strengths. I also settled on using spaCy in my primary application, and Hugging Face in my experiments.
And yeah, I saved that $500/month. Used it for pizza instead. No regrets.
I’m a technology writer with a passion for AI and digital marketing. I create engaging and useful content that bridges the gap between complex technology concepts and digital technologies. My writing makes the process easy and curious. and encourage participation I continue to research innovation and technology. Let’s connect and talk technology! LinkedIn for more insights and collaboration opportunities: