Summarize this article with:
What is Named Entity Recognition (NER)?
Named Entity Recognition, also called Entity Categorization or Entity Tagging, typically involves analyzing unstructured text data and categorizing named entities within text, such as people, organizations, locations, dates, and other predefined categories.
The technology uses machine learning and natural language processing (NLP) techniques to recognize patterns, context, and linguistic features, to determine the boundaries and types of named entities. The output can be used for various purposes, such as information extraction, content analysis, search, recommendation systems, and more.
Named Entity Recognition Use Cases
You can use NER in numerous fields, here are some examples of common use cases:
- Customer Relationship Management (CRM): automatically identify and extract entities from customer emails, support tickets, or social media interactions.
- Content Analysis: identify key entities from web pages, blog posts, or social media content and enable personalized recommendations based on user interests, preferences, or related entities.
- Market Research: analyze news articles, social media discussions, or customer reviews and identify mentions of brands, products, competitors, and other relevant entities to gain insights into market trends, sentiment analysis, or competitor analysis.
- Legal and Compliance: assist in analyzing legal documents, contracts, or case files to extract names of parties involved, important dates, legal terms, and other pertinent information for document classification, due diligence, or compliance monitoring.
- Healthcare and Biomedicine: extract medical entities such as diseases, symptoms, drugs, and treatment methods from clinical notes, research papers, or electronic health records.
- Financial Analysis: extract and categorize entities like company names, financial instruments, or market events from news articles, SEC filings, or financial reports. It can support investment decision-making, risk assessment, or portfolio management.
Best Named Entity Recognition APIs in 2026
The best Named Entity Recognition APIs in 2026 are AWS, Google Cloud, IBM Watson, Lettria, Microsoft Azure, NLP CLoud, OneAI, OpenAI and Tenstorrent. While comparing NER APIs, it is crucial to consider different aspects, among others, cost security and privacy. NER experts at Eden AI tested, compared, and used many NER APIs of the market.
| Provider | F1 Score | Languages | Entity Types | Free Tier | Price / 1K calls |
|---|---|---|---|---|---|
| OpenAI | ~93% | 95+ | Custom | None | ~$0.002 |
| Google Cloud NLP | ~91% | 10 | 11 | 5K/mo | $0.001 |
| AWS Comprehend | ~89% | 12 | 9 | 50K/mo | $0.0001 |
| Azure Text Analytics | ~88% | 22 | 10 | 5K/mo | $0.0025 |
| NLP Cloud | ~87% | 200+ | 16 | None | $0.0035 |
| Neural Space | ~86% | 87 | 36 | Limited | Custom |
| IBM Watson NLU | ~85% | 13 | 12 | 30K/mo | $0.003 |
| Lettria | ~84% | 3 (FR focus) | 20+ | None | Custom |
| OneAI | ~83% | 10 | 15 | 1M words/mo | $0.004 |
| Tenstorrent | ~82% | 6 | 8 | None | Custom |
| Test all 10 NER APIs free with one key → | |||||
F1 scores measured on a general-purpose NER benchmark dataset — April 2026. Pricing reflects standard pay-as-you-go rates; volume discounts may apply.
1. AWS
With its exceptional precision and versatile customization features, AWS's NER solution has gained significant popularity. The API can be effectively trained to identify distinct domains and languages, seamlessly integrating with various AWS tools to enable advanced analysis and processing. Moreover, Amazon's robust security and compliance measures provide a strong foundation for scalability and reliability.
2. Google Cloud
With its broad language support, Google Cloud Named Entity Recognition API can accurately detect addresses and phone numbers based on the chosen language. Its exceptional accuracy enables precise identification of entities while establishing meaningful connections among them.
This enriches the extracted information with valuable contextual insights, enhancing its overall quality.
3. IBM Watson
IBM Watson presents a remarkably customizable and feature-rich NER solution tailored for entity recognition. Available on Eden AI, the IBM Watson NLU API boasts the capability to efficiently handle diverse languages and identify entities across various contexts with utmost accuracy.
4. Lettria
Lettria's NER API strikes a harmonious balance between accuracy and processing speed, making it a fitting choice for a wide range of NLP-related applications. The company additionally offers the flexibility to fine-tune the NER API for specific use cases, enabling greater customization. Furthermore, the API boasts a user-friendly RESTful interface, simplifying its seamless integration into existing applications.
5. Microsoft Azure
Azure provides NER API services as part of the Microsoft Azure Cognitive Services suite. These services are hosted on the robust Microsoft Azure infrastructure, offering scalability and reliability.
Integration with the NER API is made effortless through the availability of comprehensive SDKs and APIs. Moreover, the API supports multiple languages, making it suitable for a wide range of global applications.
6. Neural Space
Neural Space's NER API delivers exceptional customization capabilities and remarkable accuracy, making it an ideal solution for organizations requiring precise textual data processing in specific domains or languages. Furthermore, the API supports over 36 different entities 87 languages, providing flexibility for a diverse range of use cases.
7. NLP CLoud
NLP Cloud's NER API offers advanced entity recognition with customization options, multilingual support, and pre-trained models for accurate extraction of names, locations, organizations, and more. Its user-friendly interface enables seamless integration into existing applications.
8. OneAI
OneAI's NER API presents a compelling blend of high accuracy, multilingual support, scalability, and seamless integration. This makes the API a suitable choice for organizations dealing with substantial volumes of text data across different languages, catering to their diverse processing needs effectively.
9. OpenAI
Open AI's technology leverages cutting-edge machine learning models to accurately identify and extract entities from textual data. Users have the flexibility to customize the API's behavior according to their specific requirements, enabling fine-tuning for domain-specific entity recognition. The API also offers multilingual support, allowing users to process text in different languages.
10. Tenstorrent
Tenstorrent's NER API offers an impressive combination of precise accuracy, support for multiple languages, the ability to scale, and effortless integration. It is an ideal option for enterprises handling large amounts of text data in various languages, as it meets their varied processing requirements efficiently.
How to Use Multiple NER APIs with a Single Integration
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI's unique API to easily integrate NER tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform amongst several technologies: Text-to-Speech, Language Detection, Sentiment Analysis API, Summarization, Question Answering, Data Anonymization, Speech Recognition, and so forth.
We want our users to have access to multiple NER engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs :
- Fallback provider is the ABCs: You need to set up a provider API that is requested if and only if the main NER API does not perform well (or is down). You can use confidence score returned or other methods to check provider accuracy.
- Performance optimization: After the testing phase, you will be able to build a mapping of providers' performance based on the criteria you have chosen (languages, fields, etc.). Each data that you need to process will then be sent to the best NER API.
- Cost - Performance ratio optimization: You can choose the cheapest NER provider that performs well for your data.
- Combine multiple AI APIs: This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because NER APIs will validate and invalidate each other for each piece of data.
Why Developers Use Eden AI for Named Entity Recognition
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies. Eden AI allows you to call multiple AI APIs.
- Centralized and fully monitored billing on Eden AI for all NER APIs
- Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider
- Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
- The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines)
- Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.
You can see Eden AI documentation here.
Get Started with NER APIs - Free
The Eden AI team can help you with your NER integration project. This can be done by :
- Organizing a product demo and a discussion to better understand your needs.
- By testing the public version of Eden AI for free: however, not all providers are available on this version. Some are only available on the Enterprise version.
- By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs
- Having the possibility to integrate on a third party platform: we can quickly develop connectors
.jpg)
