NeuroNER is live · free plan; paid plans on request · last verified 30 Aug 2026 · viability monitor
NeuroNER
An open-source named entity recognition toolkit for training custom models with neural networks.
NeuroNER offers a solid balance of usability and performance for custom NER, but the parked domain and lack of recent updates are concerning. If you need a maintained solution, consider alternatives like spaCy, Stanford NER, or Hugging Face Transformers. For teams willing to risk using a dormant project, NeuroNER's pre-trained models and training pipeline remain functional if you can get the code running.
Verdict generatedVerified 8/30/2026 · liveness 65/100 · cite: rightaichoice.com/tools/neuroner
- Data scientists needing custom NER
- Researchers in NLP and information extraction
- Businesses processing large volumes of text documents
- Users needing a plug-and-play NER without any training
- Non-technical users expecting zero configuration
- Projects requiring real-time recognition with sub-millisecond latency
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Skip NeuroNER if you need an actively maintained tool with current documentation and support, or if you can't afford time to source the code from GitHub and resolve installation issues yourself.
The label annotation interface is a custom web app with no hosted option, so you must set it up yourself and invest time in data labeling before training.
NeuroNER is free and open-source, but the real cost is maintenance. If you need a supported free option, spaCy offers a free open-source NER with active development and a large ecosystem, while Hugging Face Transformers also has free models with institutional backing.
In short
NeuroNER — An open-source named entity recognition toolkit for training custom models with neural networks. Best for Data scientists needing custom NER, Researchers in NLP and information extraction, Businesses processing large volumes of text documents. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
What is NeuroNER?
NeuroNER — An open-source named entity recognition toolkit for training custom models with neural networks. Best for Data scientists needing custom NER, Researchers in NLP and information extraction, Businesses processing large volumes of text documents. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
NeuroNER is an open-source named entity recognition (NER) toolkit that uses neural networks to extract entities such as persons, organizations, locations, and dates from text. It is built on TensorFlow and offers a web-based annotation interface for labeling training data, along with tools for training, evaluating, and deploying custom NER models. You can start with pre-trained models and fine-tune them on your own domain-specific corpus, making it suitable for researchers, data scientists, and developers who need accurate entity extraction without deep machine learning expertise. However, the official website (neuroner.com) is currently a parked domain, indicating the project may no longer be actively maintained. Documentation and community forums are inaccessible, so you may face challenges finding up-to-date installation guides or support for newer TensorFlow versions. Despite this, the underlying code and pre-trained models remain functional if you can obtain them from sources like GitHub. For a maintained alternative, you may consider spaCy, Stanford NER, or Hugging Face Transformers.
Key features
- Pre-trained named entity recognition models
- Custom model training with annotated data
- Web-based annotation interface
- Support for multiple entity types (person, organization, location, date)
- TensorFlow-based deep learning backend
- Deployment via API
- Command-line interface for automation
- Model export for production use
- Active learning to reduce annotation effort
- Evaluation metrics for model performance
Our long-form take has no recorded date, so it is being re-reviewed and is not shown.
Models under the hood
as of 2026-08-30
Limitations
- The official website is currently a parked domain, indicating the project may no longer be actively maintained.
- Documentation and community forums are inaccessible.
- Users may struggle to find up-to-date installation guides or support for newer TensorFlow versions.
as of 2026-08-29
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Is NeuroNER still active?
NeuroNER is live · free plan; paid plans on request · last verified 30 Aug 2026 · viability monitor.
Viability score
How well maintained and how widely used is NeuroNER? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What do users say about NeuroNER?
What people actually say about NeuroNER — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
ResearchedDiscussed by users across 2 sources (YouTube, GitHub) · researched Oct 2, 2026.
Weighted by the 22 posts each of 2 sources contributed.
- +Still produces solid NER accuracy on CoNLL-style datasets when correctly configured
- +Built-in web annotation UI is genuinely useful for small labeled-data projects
- +Open-source code under a permissive license, so you can fork and maintain it yourself
- +BRAT-format compatibility lets you reuse existing annotated corpora without conversion
- +Command-line interface and API deployment path work without extra glue code
- −Official website is a parked domain, eliminating all official docs and downloads
- −Open 2025 issue about missing glove.6B.100d.zip download has no maintainer answer
- −Recent open issue about which I2B2 entity types are covered remains unresolved
- −TensorFlow stack is dated, making modern Python environments painful to set up
- −No active maintainer response on new issues since early 2026
- • Engineering time to debug legacy TensorFlow and Python dependency conflicts
- • Sourcing word vectors and pre-trained models from unofficial mirrors now that neuroner.com is offline
- • Fork maintenance cost if you decide to keep the project alive internally
How we verified this
Every listed fact about NeuroNER — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 30 Aug 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified NeuroNER 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed domain expiry
- — changed hidden costs, switching in, switching out, pricing fit, setup time, who should skip it
Showing the 2 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does NeuroNER cost in 2026?
NeuroNER: Free to start; it also has paid plans whose price we could not confirm — see the pricing table. Pricing last verified 30 Aug 2026.
Plans compared
Every published NeuroNER tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Community | Free | — | Independent researchers, students, and hobbyists who want a free, self-hosted NER toolkit and are comfortable managing their own infrastructure and troubleshooting.Adds: Starting tier: free access to pre-trained models and a basic web annotation interface, but with limited training capacity and community support only. |
| Enterprise | Custom | quote | Large organizations that need unlimited training, custom model optimization, and on-premise deployment with formal support and SLAs, likely due to compliance or scale requirements.Adds: Adds unlimited training and deployment, priority support with SLAs, custom model optimization, and on-premise deployment options on top of the Community tier. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where NeuroNER's pricing actually pencils out — and where peers do it cheaper.
NeuroNER is free and open-source, but the real cost is maintenance. If you need a supported free option, spaCy offers a free open-source NER with active development and a large ecosystem, while Hugging Face Transformers also has free models with institutional backing.
Has NeuroNER's pricing changed?
No plan price for NeuroNER has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick NeuroNER?
Pick NeuroNER if you are Data scientists needing custom NER, Researchers in NLP and information extraction, Businesses processing large volumes of text documents.
Look elsewhere if you are Users needing a plug-and-play NER without any training, Non-technical users expecting zero configuration, Projects requiring real-time recognition with sub-millisecond latency.
Skip it if Skip NeuroNER if you need an actively maintained tool with current documentation and support, or if you can't afford time to source the code from GitHub and resolve installation issues yourself.
GeneratedReal-world workflow fit
Concrete scenarios for the personas NeuroNER actually fits — and what changes day-one when you adopt it.
You want to reproduce a published NER experiment on a domain corpus.
Outcome: You download the code from GitHub, use the web annotation interface to label a small dataset, train a custom model, and evaluate it with built-in metrics.
You need to extract financial entities from thousands of earnings transcripts.
Outcome: You annotate a sample set, train a domain-specific model, and deploy it via the API to process the full volume.
You want to extract parties and dates from contracts.
Outcome: You use the command-line interface to automate training and export the model, but you hit documentation gaps and need to patch TensorFlow compatibility—delaying your launch.
Use cases
- Extract person names, organizations, and locations from news articles
- Identify medical terms and drug names in clinical reports
- Automate tagging of financial entities in earnings transcripts
- Build a custom NER system for legal contract analysis
Setup time & first value
How long it actually takes to get something useful out of NeuroNER — broken out by persona, not the marketing-page minute.
For a technical user familiar with TensorFlow: 1-2 days to get the code running from GitHub, plus additional time for labeling and training. Non-technical users should expect significantly more time or a blocker.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “NeuroNER”, and we withheld 6: 6 could not be judged, because “NeuroNER” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about NeuroNER.
Official links
What are the best NeuroNER alternatives?
The closest alternatives to NeuroNER we list are Timecopilot, Autogluon and DB-GPT. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Timecopilot | Free | Free | — |
| Autogluon | Free | Free | — |
| DB-GPT | Free | Free | — |
Alternatives to NeuroNER
View all
Timecopilot
TimeCopilot is an open-source AI forecasting agent that picks, validates, and explains time series models from a plain-English prompt.
Tools that pair well with NeuroNER
Common stack mates teams adopt alongside NeuroNER, with the specific reason each pairing earns its keep.
Timecopilot
TimeCopilot is an open-source AI forecasting agent that picks, validates, and explains time series models from a plain-English prompt.
Autogluon
AutoGluon: open-source AutoML that trains tabular, time series, and multimodal ML models in 3 lines of Python.
DB-GPT
Open-source agentic AI data assistant that writes SQL, runs code in a sandbox, and chats with your self-hosted databases
NeuroNER vs its closest rival
NeuroNER's closest head-to-head is with Air Ai — NeuroNER and Air AI address completely different problems. Choose NeuroNER if you need to extract custom entities from text with a balance of usability and performance. Choose Air AI if you are a defense organization aiming to compress materiel release timelines and increase equipment readiness using AI-driven supply chain intelligence. There is no overlap in use case or market. Verdict generated
Head-to-head comparisons
Neuroner vs Air Ai
NeuroNER and Air AI address completely different problems. Choose NeuroNER if you need to extract custom entities from text with a balance of usability and performance. Choose Air AI if you are a defense organization aiming to compress materiel release timelines and increase equipment readiness using AI-driven supply chain intelligence. There is no overlap in use case or market.
GeneratedNeuroner vs Screenplayiq
Verdict being re-reviewed against current pricing and data.
Neuroner vs Praktika
These aren't competitors — pick based on your problem, not a head-to-head. If you need to extract entities (people, orgs, locations, dates) from your own text corpus and can annotate training data, NeuroNER gives you a TensorFlow-backed pipeline with pre-trained models, active learning, and API export — but treat it as an unmaintained project, since its site is a parked domain and community docs are gone. If you're an intermediate or returning speaker who freezes when talking and wants unlimited low-stakes reps, Praktika is the buy at roughly $8/month, with instant in-conversation corrections — just know it's capped on the free tier and mobile-only.
GeneratedNeuroner vs Persefoni
NeuroNER and Persefoni serve completely different domains, so the choice depends on your problem. If you need to extract named entities from text (custom or pretrained), NeuroNER offers deep learning NER with active learning and API deployment at a freemium price. If you need enterprise-grade carbon accounting for regulatory compliance (Scope 1-3, PCAF, CSRD, etc.), Persefoni is purpose-built with AI-assisted features like Copilot and Anomaly Detection, and has recent credibility boosts (TIME/Statista recognition, Amazon partnership). Pick NeuroNER for text extraction, Persefoni for sustainability reporting.
GeneratedNeuroner vs Geologicai
If you need to extract entities from text, NeuroNER's freemium model and custom training make it a solid choice for researchers and developers. For mining companies that need rapid, multi-sensor core analysis, GeologicAI's end-to-end platform and sub-48-hour turnaround are unmatched. Choose based on your domain: NLP vs. mineral exploration.
GeneratedFrequently asked questions
Categories
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