LabelGPT
FreemiumZero-shot auto labeling platform for images, video, text, and audio using foundation models.
By Tanmay Verma, Founder · Last verified 05 Jul 2026
0 views
Added 6d ago
77/100Safe BetIn short
LabelGPT — Zero-shot auto labeling platform for images, video, text, and audio using foundation models. Best for ML teams needing rapid pre-labeling for large datasets, Computer vision teams working with segmentation tasks, Healthcare AI teams annotating medical images (DICOM, NIfTI). Free to start; paid plans from $9999/mo.
Is LabelGPT actually worth it?
LiveSee what real users actually say. We scan live discussions, reviews and complaints across the web and hand you an honest verdict — in under a minute.
3 free scans · no card needed · downloadable report
Editorial Verdict
Best for
ML teams needing rapid pre-labeling for large datasetsComputer vision teams working with segmentation tasksHealthcare AI teams annotating medical images (DICOM, NIfTI)Autonomous vehicle and robotics teamsResearchers and small teams on a budget (free tier)
Not ideal for
Teams requiring fully on-premise annotation without cloud connectivity (Enterprise plan offers it but at custom cost)Users needing mobile or desktop apps (web-only)Teams expecting zero learning curve for SDK integrationReal-time video annotation at scale (SAM 2 memory bank helps but may have latency)
LabelGPT offers a compelling zero-shot labeling workflow for teams needing rapid pre-labeling. The SAM 3 integration and event tagging features are solid additions, but the Pro tier at $9,999/year may be pricey for smaller volumes. Consider the free tier to test before committing. Compared to Roboflow or Labelbox, LabelGPT focuses on zero-shot automation directly out of the box without requiring training data.
Skip LabelGPT if Skip LabelGPT if you need fully on-premise annotation without cloud connectivity or require mobile/desktop apps.
Compare with: LabelGPT vs Obviously AI, LabelGPT vs Morphik, LabelGPT vs Persana AI
Last verified: July 2026
What's new in LabelGPT
Checked 4 days agoAcross the latest 1 update: 1 changelog entry.
What independent users actually report about LabelGPT
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.
9 mentions across 1 source (Product Hunt).
95% positive5% critical
Recurring strengths
- +Fast zero-shot labeling reduces weeks to minutes.
- +Easy to use — upload image, type object name, get labels.
- +Supports diverse data types: image, video, text, audio, DICOM, LiDAR, NIfTI.
- +Freemium pricing lowers barrier for individual developers.
- +Integrates SAM, SAM 2, SAM 3 for high-precision segmentation.
Recurring frustrations
- −Community feedback is too thin to confirm reliability.
- −No large-scale performance data from real users.
- −Zero critical reviews — all 9 Product Hunt comments are promotional.
- −Dependence on SAM models may limit custom domain accuracy.
- −Scale AI comparison feels aspirational, not yet proven.
Patterns worth knowing
Ease of use and speed for basic labeling tasks
Seen on Product Hunt
Comparison to Scale AI as a benchmark
Seen on Product Hunt
Excitement about zero-shot auto-labeling potential
Seen on Product Hunt
Learning curve
beginnerProductive in ~5 minutes
Hidden costs people mention
- • Free tier limits and upgrade costs not publicly detailed.
- • Human-in-the-loop services likely incur extra fees.
Viability Score
77/100
Safe BetHow likely is LabelGPT to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Zero-shot automated labeling using foundation models
- Prompt-based labeling (text prompts for object/class detection)
- Model-assisted labeling (SAM, SAM 2, SAM 3 integration)
- Active learning based labeling automation
- Seamless SDK integration for pipeline automation
- Pre-annotation upload for review and editing
- Project management with analytics and EDA
- Support for image, video, text, audio, DICOM, LiDAR, NIfTI
- Smart guidelines and third-party annotation vendor access (premium)
- Attach custom models (HuggingFace, OpenCV) on demand
- Human-in-the-loop services
- Dataset management module
- Smart feedback loop for iterative improvement
- Master Control Panel for event tagging (2026 update)
- Foundation model-powered segmentation improvements
About LabelGPT
FreemiumIntermediateAPI availableWeb · API
LabelGPT, by Labellerr, is an automated data annotation platform that leverages multiple foundation models including Meta's SAM, SAM 2, and SAM 3 to generate high-quality labels with minimal human intervention. It supports image, video, text, audio, DICOM, LiDAR, and NIfTI data types, enabling teams to pre-label datasets via prompt-based, model-assisted, and active learning workflows. The platform also offers a human-in-the-loop layer for quality assurance, integration via SDK, and project management features like dataset management, EDA, and analytics. It is designed for ML teams, data scientists, and domain-specific practitioners in industries such as healthcare, automotive, security, retail, agriculture, and biotech. The key differentiator is its seamless integration of the Segment Anything Model family (SAM, SAM 2, SAM 3) for high-precision segmentation, combined with support for custom models from Hugging Face and OpenCV. As of 2026, updates include a Master Control Panel for event tagging in videos and foundation model-powered segmentation improvements. Available as a web-based platform with SDK for programmatic access.
Behind the Verdict
LabelGPT excels at rapid, zero-shot annotation for computer vision tasks, especially segmentation, using the SAM family of models. The platform's strength lies in its ability to generate labels from text prompts alone, eliminating the need for manual bounding boxes or polygon drawing. For teams dealing with large volumes of images or videos, this can reduce labeling time from weeks to minutes. The support for multiple data types (image, video, text, audio, DICOM, LiDAR, NIfTI) makes it versatile across industries like healthcare, autonomous vehicles, and retail. The free Researcher plan is excellent for students and small projects, offering up to 2,500 data credits and 1 seat. However, the Pro plan at $9,999/year for 100,000 credits may be expensive for small teams with limited needs. The platform is web-only, with no mobile or desktop apps, and requires internet connectivity. While the SDK allows for pipeline integration, there is a learning curve for developers. The video annotation capabilities, while improved with SAM 2's memory bank, may still have latency on very long videos. Overall, LabelGPT is best suited for teams that prioritize speed and automation over fine-grained manual control, and are willing to pay for the convenience.
Researching LabelGPT? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas LabelGPT actually fits — and what changes day-one when you adopt it.
Data scientist at a healthcare startup
You need to segment tumors in 5,000 DICOM images. With LabelGPT, you upload the images to AWS S3, connect the bucket, and type 'tumor' as a text prompt. The platform uses SAM 3 to generate segmentation masks in minutes. You review high-confidence labels and export them to your ML pipeline via SDK.
Outcome: Labeling time reduced from weeks to hours, enabling faster model iteration.
ML engineer at an autonomous vehicle company
You have 10,000 video frames to label for object detection. You give text prompts like 'car', 'pedestrian', 'traffic light'. LabelGPT uses SAM 2's memory bank to maintain temporal consistency across frames, generating bounding boxes and segmentation masks. You use the Master Control Panel to tag specific events like 'lane change'.
Outcome: Large-scale video annotation completed in days vs months, with high consistency across frames.
Researcher building a text classification dataset
You have 50,000 text documents to annotate for sentiment. Using LabelGPT's text annotation platform, you set up an active learning workflow: the model pre-labels text, and you only review low-confidence samples. You can also attach a custom Hugging Face model for better accuracy.
Outcome: Annotation effort reduced by 80%, with high-quality labels for LLM fine-tuning.
Use Cases
- Automatically annotate thousands of medical DICOM images for tumor segmentation using SAM 3 masks.
- Pre-label video frames for autonomous vehicle perception models with SAM 2's memory bank for temporal consistency.
- Generate bounding boxes and polygons for retail product images via prompt-based labeling with class text prompts.
- Build a text classification dataset for LLM fine-tuning using the text annotation platform's active learning workflow.
- Create a multi-modal dataset combining images and text for a visual question answering model.
- Rapidly prototype a yoga pose classifier by labeling skeleton keypoints with the AI yoga pose tutorial pipeline.
Models Under the Hood
Meta SAMSAM 2SAM 3Hugging Face modelsOpenCV models
as of 2026-07-05
Limitations
- The free Researcher Plan caps data credits at 2,500 and files at 1,000.
- The Pro plan costs $9,999 annually for up to 100,000 credits, which may be steep for small teams.
- Video annotation performance depends on SAM 2's memory bank, which may struggle with very long videos.
- Some advanced features (SSO, private cloud) are only in the custom Enterprise tier.
as of 2026-07-05
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
—
—
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published LabelGPT tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Researcher Plan
$0
Ideal for
Students, researchers, and solo practitioners with small datasets (up to 1,000 files) exploring zero-shot labeling.
What this tier adds
Free entry point with 2,500 data credits, 1 seat, 1 workspace, and support for image, video, text, audio (up to 2MB/5MB/1MB). Limited to 10 projects and prompt-based labeling.
Pro Plan
$9,999/year
Ideal for
Small teams and businesses with under 200 employees needing advanced automation and up to 100,000 annotations per year.
What this tier adds
Adds 100,000 data credits, up to 200 seats, unlimited projects, and advanced automation (SAM, SAM 2, SDK, active learning, model-assisted labeling). Includes human-in-the-loop services and 24/7 email support.
Enterprise Plan
Custom
Ideal for
Large organizations requiring unlimited data credits and seats, SSO, private cloud/on-premise deployment, and enterprise-grade support.
What this tier adds
Unlimited data credits and seats, multiple workspaces, SSO, enterprise SLA, private cloud/on-premise, and custom data type support (DICOM, LiDAR, NIfTI). 24/7 email, chat, and call support.
Integrations
AWS S3GCP Cloud StorageAzure Blob StorageMeta SAM / SAM 2 / SAM 3Hugging Face modelsOpenCVSDK (Python)
Where the pricing makes sense
The company stage and team size where LabelGPT's pricing actually pencils out — and where peers do it cheaper.
LabelGPT's free Researcher plan is ideal for students and small teams with limited data. The Pro plan at $9,999/year suits growing teams needing up to 100,000 annotations and up to 200 seats. This is competitive with Roboflow's similar tier, but may be pricier for low-volume users compared to CVAT (open-source). Enterprise pricing is custom, likely targeting large organizations with dedicated support and on-premise options.
Setup time & first value
How long it actually takes to get something useful out of LabelGPT — broken out by persona, not the marketing-page minute.
For image annotation: connect cloud storage (5 min), create project (2 min), set prompts (2 min), run labeling (minutes to hours). Video annotation may take longer due to SAM 2 processing. Text annotation setup similar. SDK integration requires developer time (1-2 days for pipeline automation).
Switching to or from LabelGPT
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From CVAT: Export your existing annotations in COCO JSON format and import them into LabelGPT. Pre-annotation upload feature allows you to review and edit labels within the platform.
- →From Labelbox: Download your dataset and annotations via Labelbox API, then upload to LabelGPT using SDK or cloud storage integration.
- →From Roboflow: Export dataset in Roboflow format and import via API. LabelGPT supports pre-annotation upload for review.
Migrating out
- ↗To CVAT: Export annotations in COCO JSON or CSV format and import into CVAT.
- ↗To Labelbox: Use LabelGPT export to JSON/CSV and migrate via Labelbox API.
- ↗To Roboflow: Export in Roboflow format and upload to Roboflow platform.
- ↗To custom pipeline: Use LabelGPT SDK to programmatically export annotations and integrate with any ML training engine.
Resources & Guides
Frequently Asked Questions
Tools that pair well with LabelGPT
Common stack mates teams adopt alongside LabelGPT, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Alternatives to LabelGPT
View allObviously AI
Always-on AI workers automate CRM, meeting prep, and account monitoring for revenue teams
FreemiumTry
Morphik
AI workers that automate AP, billing, collections, and payroll for senior living operators.
FreemiumTry
Persana AI
AI sales prospecting with 100+ data sources and automation agents
FreemiumTry
Used LabelGPT? Help shape our editorial sentiment research.