7 Best Named Entity Recognition APIs in 2026

Named Entity Recognition sounds technical, but the idea is simple.

You give an API a piece of text, and it finds the important “things” inside it: people, companies, places, dates, products, money amounts, medical terms, account numbers, and other useful details.

For example, this sentence:

Apple opened a new office in Chicago on July 15, 2026.

Can become this:

[
  {
    "text": "Apple",
    "type": "organization"
  },
  {
    "text": "Chicago",
    "type": "location"
  },
  {
    "text": "July 15, 2026",
    "type": "date"
  }
]

That is useful for search, analytics, CRM enrichment, compliance checks, document processing, customer support, fraud review, healthcare workflows, media monitoring, and AI automation.

The hard part is choosing the right API.

Some NER APIs are better for business text. Some are stronger for cloud workflows. Some are better for entity linking, where the API connects “Apple” to the company instead of the fruit. Some are better for custom entity extraction, like policy numbers, claim IDs, product SKUs, or legal clause names.

So instead of treating every NER API like the same tool with a different logo, let’s look at what each one is actually good for.

First, What Kind of Entity Problem Do You Have?

Before picking an API, ask what you need to extract.

A simple NER task may look like this:

That last one matters more now. Many apps use NER before sending text into an LLM workflow. For example, a system may extract names, company details, or PII first, then decide what to redact, route, summarize, or store.

NER research also keeps moving in this direction. The paper GPT-NER: Named Entity Recognition via Large Language Models explains that classic NER is usually treated as a sequence-labeling task, while LLM-based NER turns extraction into a generation task. That fits real apps because developers now often ask models to return entities as JSON, not just tag tokens inside text. The paper also warns about hallucination, which is why production NER workflows should validate results instead of blindly trusting every extracted entity.

How Should You Choose a NER API?

Here is the practical checklist.

NER can look easy on demos, but production text is messy. Company names get abbreviated. People use nicknames. Dates appear in many formats. Product names overlap with common words. “Amazon” may be a company, marketplace, river, or rainforest depending on context.

That is also why entity linking can matter. Raw NER may say “Apple = organization.” Entity linking tries to connect it to the right real-world entity. APIs like TextRazor and Dandelion are especially useful when you want semantic enrichment, not just labels.

Quick Picks by Use Case

Now let’s go through the 7 best NER APIs for 2026.

1. Amazon Comprehend

Amazon Comprehend is one of the best NER APIs if your app already runs on AWS.

The standard DetectEntities API returns entity text, entity type, begin and end offsets, and confidence scores. Those offsets matter because they let your app highlight entities in the original text, redact them, or link them back to the source sentence.

Amazon Comprehend can detect standard entities like people, locations, organizations, dates, quantities, commercial items, and more. It also supports custom entity recognition, which is useful when your business has entity types that generic NLP models do not know.

Choose Amazon Comprehend if your app uses S3, Lambda, Textract, Step Functions, or other AWS services. It fits neatly into document workflows where text may come from PDFs, OCR, forms, or uploaded files.

Amazon Comprehend is also worth testing if privacy matters. Its pricing page highlights NLP features like PII detection and redaction, custom classification, and custom entity detection. That makes it useful for apps that need to detect names, addresses, financial details, or private customer information before storing or sending text elsewhere.

A research example here is Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service. It focuses on Amazon Comprehend Medical, not the general Comprehend API, but it is still useful because it shows why domain-specific entity extraction matters. Medical text needs medication names, conditions, procedures, anatomy, protected health information, negation, and relationships. A generic “person/location/company” extractor would miss too much.

So for normal business text, use Amazon Comprehend. For medical entities, look at Amazon Comprehend Medical.

When Amazon Comprehend Makes Sense

2. Azure AI Language

Azure AI Language is a strong NER choice for teams already using Microsoft tools.

Azure’s Named Entity Recognition feature can identify entities such as people, locations, organizations, dates, quantities, email addresses, URLs, IP addresses, and more. Microsoft’s docs also cover data limits for Language service features, which is important before building production workflows around long documents or high-volume text.

Azure is a good fit if your company already uses Azure Storage, Azure Functions, Microsoft Entra ID, Power Platform, SharePoint, or Microsoft security tools.

It is also useful when NER is part of a wider enterprise workflow. For example, a company may use Azure Document Intelligence to extract text from PDFs, then Azure AI Language to detect entities, then a database or Power BI dashboard to track customers, vendors, projects, or locations.

Azure’s custom NER option is especially useful when normal labels are too generic. For example, a logistics app may need labels like shipment_id, carrier_name, delivery_window, and warehouse_location. A legal app may need court_name, case_number, party_name, and clause_reference.

NER research supports this idea of label design. The paper Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach explains that entity types are connected, and label relationships can affect recognition quality. In normal human language, this means your labels should make sense together. If your custom labels overlap too much, the model can get confused.

When Azure AI Language Makes Sense

3. Google Cloud Natural Language

Google Cloud Natural Language is a good choice for teams using Google Cloud or apps that need entity extraction with clean cloud APIs.

Google’s Natural Language API basics explain that Entity Analysis identifies entities in text, including famous people, landmarks, common objects, and other named things. The analyzeEntities method returns recognized entities, mentions, probability, types, and other properties.

Google Cloud Natural Language is especially useful when you want entity analysis and entity sentiment. Entity sentiment is helpful when the question is not just “Which company is mentioned?” but “How does the text feel about that company?”

For example:

The battery life on the Pixel phone is great, but the customer support was terrible.

A normal entity extractor may find Pixel and customer support. Entity sentiment can help show that one entity is discussed positively and another negatively.

Google’s pricing page explains that Natural Language API pricing is based on Unicode characters counted in units. That is one of those boring details that becomes very important in production. A tiny demo can feel cheap. A high-volume review analytics system may need careful cost estimates.

Google Cloud Natural Language is a good fit for:

Research around entity extraction often highlights context. One helpful example is Named Entity Analysis and Extraction with Uncommon Words, which discusses how uncommon words can help distinguish named entities from normal text. This fits entity analysis well because names, brands, and organizations often behave differently from common words. The tricky part is that product names and brands can also become everyday words, so real evaluation still matters.

4. IBM Watson Natural Language Understanding

IBM Watson Natural Language Understanding is a strong option for enterprise semantic analysis.

IBM’s docs say Watson NLU can analyze semantic features such as categories, concepts, emotion, entities, keywords, metadata, relations, semantic roles, and sentiment. The IBM Cloud docs also explain that you can send text, HTML, or a public URL and request features like entities, keywords, concepts, sentiment, and categories.

IBM Watson NLU is useful when entities are part of a larger understanding workflow.

For example, a media monitoring app may want:

1. People and companies mentioned.
2. Sentiment around each entity.
3. Keywords and concepts.
4. Categories for the article.
5. Relations between entities.

That wider semantic layer is where IBM can make sense.

A research paper called A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering compared platforms including IBM Watson, Google Dialogflow, Rasa, and Microsoft LUIS for software engineering chatbot tasks. It found that NLU platform choice matters by task, and some tools perform better for entity extraction in specific domains. This fits our point here: do not choose a NER API by brand alone. Test it on your actual text type.

When IBM Watson NLU Makes Sense

5. TextRazor

TextRazor is a strong NER API when you care about entity linking, semantic enrichment, and custom dictionaries.

The TextRazor REST API docs explain that callers can choose extractors to control which language analysis features they want. TextRazor also supports Entity Dictionaries, which let teams add custom entities relevant to their application.

That is a big deal.

Standard NER might detect “Stripe” as an organization. Entity linking can connect it to the payment company. A custom dictionary can also tell the system that an internal product name, campaign name, or customer tier should be treated as a specific entity.

TextRazor is a good fit when you need to understand what an entity refers to.

For example:

Jordan announced a new partnership with Apple.

Does “Jordan” mean a person, a country, a brand, or something else? Context decides. Entity linking and confidence signals help.

Research on approximate entity extraction is relevant here. The paper Entity Extraction using Both Character-based and Token-based Similarity explains how entity matching has to deal with spelling errors, name variations, and token-level differences. That fits custom dictionaries because real business names are messy: “OpenAI,” “Open AI,” “OpenAI Inc.,” and “OpenAI, LLC” may need to point to the same entity.

When TextRazor Makes Sense

6. Dandelion Entity Extraction API

Dandelion Entity Extraction API is a good option when you want entity extraction and linking in a lightweight API.

Its docs describe it as a named entity extraction and linking API that can work well on short texts. It can extract entities, tag text, connect entities to Wikipedia-style concepts, and return enriched data.

That makes Dandelion useful for content apps, social media analysis, article tagging, recommendation systems, and knowledge graph enrichment.

Dandelion is useful when entity context matters.

For example:

Tesla shares rose after the announcement.

A linked entity API can help connect “Tesla” to the company, not Nikola Tesla, the unit of magnetic flux density, or something else.

Dandelion’s getting started docs show how to call the API with text and include fields like types, abstracts, and categories. That makes it friendly for developers building quick text enrichment features.

When Dandelion Makes Sense

7. Eden AI Named Entity Recognition API

Eden AI Named Entity Recognition API gives you access to multiple NER providers through one API. Its NER docs show a unified endpoint pattern for named entity recognition.

This is useful if you do not want to pick one provider immediately.

You can compare outputs, route requests, and keep your app less tied to one vendor. That matters because NER accuracy depends heavily on text type, language, domain, and labels.

Eden AI is a good fit when you want flexibility.

For example, you may test AWS, Google, and other providers through a unified interface, then choose the one that performs best on your data. Or you may keep multiple providers available so your app can route by language, cost, or availability.

This is also where modern LLM workflow thinking matters. A paper called API Entity and Relation Joint Extraction from Text via Dynamic Prompt-tuned Language Model looks at extracting API entities and relations from software text using prompt-tuned language models. The details are software-engineering specific, but the lesson fits many custom NER workflows: when entities are domain-specific, prompt-based or custom extraction can reduce the need for huge labeled datasets.

Eden AI is useful if your team wants to test that kind of provider flexibility before building a custom model or committing to one cloud platform.

When Eden AI Makes Sense

Side-by-Side: Which NER API Should You Pick?

Here is the more practical comparison.

If your team already lives in one cloud, start there. AWS teams should test Amazon Comprehend. Azure teams should test Azure AI Language. Google Cloud teams should test Google Cloud Natural Language.

If your product needs semantic enrichment, test TextRazor and Dandelion.

If you are still comparing providers, test Eden AI.

What About LLMs for NER?

LLMs can do NER too.

You can ask a model:

Extract people, companies, products, dates, and money amounts from this text.
Return valid JSON only.

That can work very well when the entity labels are custom or weird.

For example:

The problem is consistency. LLMs can return extra fields, miss offsets, or infer entities that are not explicitly present. That is why classic NER APIs still matter.

The GPT-NER paper is useful here because it explains both the promise and the risk. LLMs can adapt to low-resource NER setups, but hallucination needs self-verification and careful prompting.

A practical setup is:

1. Use a NER API for standard entities.
2. Use an LLM for custom or fuzzy labels.
3. Validate the output against a strict schema.
4. Send uncertain results to review.

Where LLM API Fits

LLM API fits when NER is part of a bigger AI workflow.

A NER API extracts entities. LLM API can help route the follow-up work across models.

For example:

1. Your app sends customer text into a NER API.
2. The NER API extracts names, companies, dates, locations, and products.
3. LLM API routes a follow-up task to the best model.
4. A cheaper model handles simple classification.
5. A stronger model handles legal, finance, or customer-facing output.
6. A fallback model runs if one provider fails.
7. Your app stores the result with cost and usage tracking.

This is useful for workflows like:

The cleanest way to think about it: NER finds the important things. LLM API helps decide what your app should do with those things.

What Should You Test Before Choosing?

Do not test a NER API on one perfect sentence.

Use real text from your app.

Test:

Build a small test set with 100 examples. Mark the correct entities manually. Then compare each API.

Track these metrics:

Multilingual testing deserves special attention. The MasakhaNER paper created NER datasets for 10 African languages and showed how underrepresented many languages are in NLP resources. This matters for API buyers because “supports many languages” does not always mean “works equally well in every language.” Test the exact languages your users write in.

Common Mistakes When Choosing a NER API

The biggest mistake is treating NER like a solved checkbox. Real accuracy depends on your text, language, labels, and workflow.

Final Ranking

This ranking is based on general usefulness for developers and business apps in 2026. Your final choice should depend on your real text samples.

Final Thoughts

The best Named Entity Recognition API depends on what kind of entities your app needs.

Choose Amazon Comprehend if you already use AWS and need entity extraction, PII detection, or custom entities. Choose Azure AI Language if your workflow lives in Microsoft’s ecosystem and you want enterprise-friendly NER. Choose Google Cloud Natural Language if you want entity analysis, entity sentiment, and Google Cloud integration.

Choose IBM Watson NLU if entities are part of a wider semantic analysis workflow. Choose TextRazor if entity linking and custom dictionaries matter. Choose Dandelion if you need lightweight entity extraction and linking for short text or content tagging. Choose Eden AI if you want to compare several NER providers without wiring each one separately.

For LLM workflows, use NER as the structured extraction layer, then use LLM API to route follow-up tasks like classification, summarization, redaction, compliance checks, and fallback handling.

The practical advice is simple: pick three APIs, test them on your own text, compare precision and recall, then choose the one that creates the least cleanup for your team.