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Summarize this article with:
- For standard entity extraction at high volume, use spaCy. It is fast, free, production-ready, and works well for common entities like people, organizations, locations, dates, and amounts.
- For custom entity types without training data, use GLiNER. It is one of the most important new options in 2026 because developers can define labels at runtime and extract domain-specific entities without annotation.
- For maximum accuracy, use Hugging Face BERT or RoBERTa NER models. They are stronger for benchmark performance and fine-tuning, but require more compute and are slower than spaCy.
- For managed cloud APIs, choose based on your stack: AWS Comprehend for AWS workflows and custom training, Google Cloud NL API for entity disambiguation, and Azure AI Language for enterprise multilingual use cases.
- For flexibility and provider comparison, use Eden AI. It lets developers access multiple entity extraction APIs from one place, compare outputs, and switch providers without rewriting integrations.
Entity extraction means identifying and classifying named entities such as people, organizations, locations, dates, or custom types in unstructured text.
For developers, the right tool depends on the use case. Use it to extract fields from documents, analyze customer feedback, or review contracts for parties, dates, clauses, and obligations. Open-source models give more control, cloud APIs are easier to deploy, and LLM-based approaches are better for flexible or custom extraction.
This guide compares the best free entity extraction tools and APIs in 2026 across these three options so you can choose the right approach faster. Use this table to compare tool type, free tier, customization, and language coverage quickly.
Free tiers are usually enough for testing, prototyping, and low-volume entity extraction use cases. For production scale, cloud APIs charge per unit, but all are accessible via Eden AI from a single API key.
Open-Source Entity Extraction Models
spaCy - The Production Standard
spaCy is one of the most widely used open-source NLP libraries for production entity extraction in Python. It is designed for fast, reliable pipelines and is often used when developers need to process large volumes of text inside existing backend or data workflows.
For English entity extraction, the strongest current spaCy pipeline is en_core_web_trf, a transformer-based model that provides higher accuracy than the smaller statistical models. By default, spaCy can detect common entity types such as PERSON, ORG, GPE, LOC, DATE, MONEY, and CARDINAL, making it useful for general-purpose extraction tasks.
import spacy
nlp = spacy.load("en_core_web_trf")
doc = nlp("Apple opened a new office in Paris on March 3, 2026.")
for ent in doc.ents:
print(ent.text, ent.label_)
Use spaCy when you need speed, batch processing, and predictable behavior in Python. It is also a strong choice if your team already has annotated data and wants to fine-tune a custom NER model for domain-specific entities such as product names, legal clauses, or internal categories.
The main limitation is that spaCy’s default NER schema is fixed unless you train or fine-tune it on labeled examples. spaCy is released under the MIT license and is fully free to use.
GLiNER - Zero-Shot Entity Extraction Without Training Data
GLiNER is the standout new addition for 2026 that many older entity extraction articles still miss. Short for Generalist Lightweight NER, GLiNER is an open-source named entity recognition model built with a bidirectional transformer encoder. It was published at NAACL 2024 by Urchade Zaratiana, Nadi Tomeh, Pierre Holat, and Thierry Charnois.
The key difference from spaCy is flexibility. spaCy works well with predefined entity labels unless you fine-tune it on annotated examples. GLiNER lets you define entity labels at runtime, such as contract_party, payment_term, or jurisdiction, then extracts matching entities without training data. This makes it much easier to test new schemas or domain-specific extraction tasks.
# pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")
text = "Acme Corp will pay 50,000 EUR under French law."
labels = ["contract_party", "payment_term", "jurisdiction"]
entities = model.predict_entities(text, labels, threshold=0.5)
for entity in entities:
print(entity["text"], entity["label"])
GLiNER is especially useful when you need custom or novel entity types but do not have labeled training data. It fits document processing, contract review, compliance checks, product feedback analysis, and other domain-specific extraction workflows.
The NAACL 2024 paper reports that GLiNER outperforms ChatGPT and fine-tuned LLMs on zero-shot NER benchmarks. It also runs on CPU, making it production-feasible without requiring a GPU.
The project has around 3.2k GitHub stars, remains actively developed, and its latest PyPI release was published in March 2026. GLiNER is available on PyPI and Hugging Face under the Apache 2.0 license.
Hugging Face Transformers - Highest Accuracy NER
Hugging Face Transformers gives developers access to many BERT and RoBERTa-based NER models through a simple pipeline API. Two strong picks are dslim/bert-base-NER, a BERT model fine-tuned on the English CoNLL-2003 NER dataset, and Jean-Baptiste/roberta-large-ner-english, a RoBERTa-large model also fine-tuned on CoNLL-2003 and validated on email and chat data. CoNLL-2003 remains the standard benchmark reference for comparing classic NER models across entities such as persons, organizations, locations, and miscellaneous names.
from transformers import pipeline
ner = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
text = "Apple hired Sarah Chen in London in March 2026."
entities = ner(text)
for entity in entities:
print(entity["word"], entity["entity_group"], entity["score"])
Use Hugging Face Transformers when accuracy is the top priority and you can handle heavier inference. These models are a good fit when you have GPU access, need strong benchmark performance, or plan to fine-tune on domain-specific data such as legal, medical, or financial documents.
Compared with spaCy, transformer-based NER usually gives higher accuracy, but inference is heavier, slower, and more expensive to scale. The Hugging Face Inference API includes free-tier credits with rate limits, while self-hosting open models is free apart from your own infrastructure costs.
Flair - Strong Multilingual NER
Flair is an open-source NLP framework known for its contextual string embeddings, which capture meaning from character-level and word-level context. For entity extraction, it remains useful in multilingual pipelines and supports NER models across 12+ languages, including English, German, French, Spanish, Dutch, and others.
Flair is particularly relevant when your project involves non-English text or when you need a consistent framework for multilingual NLP tasks. It can be a good fit for teams processing support tickets, documents, or user-generated content across several languages.
That said, Flair is no longer the default recommendation for most new NER projects. GLiNER is generally more flexible for custom entity types without training data, while Hugging Face Transformers usually offer stronger model variety and accuracy. Flair is still worth considering when multilingual entity extraction is the main requirement.
Flair is released under the MIT license and is free to use.
Cloud Entity Extraction APIs
AWS Comprehend
AWS Comprehend is Amazon’s managed NLP API for extracting information from unstructured text. It can detect standard entity types such as PERSON, LOCATION, ORGANIZATION, DATE, QUANTITY, TITLE, and EVENT.
Its key advantage is Custom Entity Recognition. You can train your own entity extractor using labeled CSV data, which is useful when standard labels are not enough. For example, you can extract policy numbers, product references, claim IDs, medical terms, or contract-specific fields.
Use AWS Comprehend if your team already works with AWS, needs high-volume batch processing, or wants to train custom entity types with existing labeled data. It fits naturally into AWS workflows using services like S3, Lambda, and IAM.
For pricing, entity recognition starts at $0.0001 per unit. The AWS Free Tier includes 5 million units per month for the first 12 months, which is enough for testing and early-stage usage.
Google Cloud Natural Language API
Google Cloud Natural Language API is a managed NLP service for analyzing and extracting meaning from text. For entity extraction, it can detect entity types such as PERSON, LOCATION, ORGANIZATION, EVENT, WORK_OF_ART, CONSUMER_GOOD, and OTHER.
Its main strength is entity disambiguation. The API can link detected entities to real-world references through Google’s Knowledge Graph. For example, it can understand whether “Apple” refers to the company or the fruit, and whether “Paris” refers to the city or another entity. It also provides salience scores, which help identify which entities are the most important in a text.
This makes Google Cloud Natural Language API useful for content classification, news analysis, media monitoring, research workflows, and applications where entity context matters more than simple keyword extraction.
The free tier includes the first 5,000 units per month. After that, pricing starts at $1 per 1,000 units.
Azure AI Language (Named Entity Recognition)
Azure AI Language is Microsoft’s managed NLP service for extracting entities, analyzing text, and building language understanding workflows. Its NER capabilities support 80+ languages, making it a strong option for enterprise teams working across regions, markets, or multilingual datasets.
A key advantage is Custom NER through Azure Language Studio. Teams can train custom entity extraction models with a no-code interface, which is useful when subject-matter experts need to label and manage domain-specific entities without writing training scripts. Azure AI Language also integrates well with Azure Document Intelligence, making it suitable for structured document workflows such as invoices, contracts, forms, and internal business documents.
Use Azure AI Language if your company already relies on Microsoft or Azure, needs multilingual entity extraction, or wants custom model training without a fully code-based workflow. It is also a good fit for document AI pipelines that combine OCR, layout extraction, and named entity recognition.
Pricing includes 5,000 free transactions per month, then starts at $1 per 1,000 transactions.
IBM Watson Natural Language Understanding
IBM Watson Natural Language Understanding is a managed NLP API that can extract entities, detect sentiment, and analyze emotion in the same API call. This makes it useful when entity extraction is part of a broader text analysis workflow, not a standalone task.
It is a strong fit for regulated industries such as finance, healthcare, and legal, where teams often need to analyze documents, messages, tickets, or reports with consistent API behavior. It can support customer feedback pipelines, compliance monitoring, risk analysis, and internal review workflows.
The free tier includes 30,000 NLU items per month, which is generous compared to many competing NLP APIs. This makes it practical for testing, prototyping, and low-volume production use.
The main limitation is that IBM Watson NLU does not support custom entity training for this use case. It works with predefined entity types only.
LLM-Based Entity Extraction (New in 2026)
LLMs are now a serious option for entity extraction because they remove two major constraints of traditional NER: fixed schemas and labeled training data.
Instead of training a model to recognize predefined labels, you can describe any entity type in a prompt and request structured JSON output. This works especially well for novel entity types, relationship extraction, and cases where the meaning depends heavily on context.
OpenAI GPT-4o with Structured Outputs
With GPT-4o, entity extraction can be handled as a schema-driven generation task. Using response_format with a JSON schema, you can force the model to return entities in a predictable structure, which makes the output easier to validate and integrate into production pipelines.
from pydantic import BaseModel
from openai import OpenAI
class Entity(BaseModel):
text: str
label: str
confidence: float
client = OpenAI()
response = client.responses.parse(
model="gpt-4o",
input="Acme Corp signed a contract under French law.",
text_format=list[Entity]
)
entities = response.output_parsed
print(entities)
The tradeoff is cost and latency. GPT-4o is more expensive and slower than spaCy or GLiNER, with pricing typically around $0.005 to $0.015 per 1,000 tokens depending on input and output size. Use it when you need complex contextual entities, relationships, nested fields, or novel entity types where no training data exists.
LLM-Based Entity Extraction: The New Approach
LLMs are now a serious option for entity extraction because they remove two major constraints of traditional NER: fixed schemas and labeled training data.
Instead of training a model to recognize predefined labels, you can describe any entity type in a prompt and request structured JSON output. This works especially well for novel entity types, relationship extraction, and cases where the meaning depends heavily on context.
GLiNER vs. GPT-4o: Quick Decision Guide
For most structured NER tasks, start with GLiNER. Use GPT-4o when the extraction requires reasoning, relationships, or flexible schemas that are hard to capture with a lightweight NER model.
How to Choose the Right Entity Extraction Tool
Eden AI lets you run the same entity extraction call across multiple cloud providers, compare outputs, and switch providers by changing one parameter. This is useful for benchmarking models or building provider-agnostic extraction pipelines.
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