LabelGPT
Zero-shot auto labeling platform that turns raw images into labeled datasets in minutes.
LabelGPT delivers on zero-shot labeling, making it a strong pick for teams needing fast pre-annotation across diverse data types. The free tier is generous for experimentation, but the Pro plan's $9,999/year price may be a hurdle for small projects. If segmentation or multi-modal data is your focus, it's worth a look.
Verified 2d ago · liveness 77/100 · cite: rightaichoice.com/tools/labelgpt
- ML teams needing rapid pre-labeling
- Computer vision teams
- Healthcare AI teams
- Autonomous vehicle teams
- Teams requiring fully on-premise annotation
- Users needing mobile or desktop apps
- Small projects with low volume
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Skip LabelGPT if you're a small team with a tight budget, as the Pro plan's $9,999/year cost is steep for low-volume needs; consider free alternatives or pay-as-you-go tools.
Going past 2,500 data credits on the free plan forces a Pro plan upgrade, which costs $9,999/year.
LabelGPT's free tier is generous for experimentation, but the Pro plan at $9,999/year is a big jump. For small teams, Roboflow offers a more accessible price point, while Labelbox is pricier for enterprise needs. Best suited for mid-size teams needing automation features.
In short
LabelGPT — Zero-shot auto labeling platform that turns raw images into labeled datasets in minutes. Best for ML teams needing rapid pre-labeling, Computer vision teams, Healthcare AI teams. Free to start; paid plans from $9999/mo.
What people actually say about LabelGPT — 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.
14 mentions across 2 sources (YouTube, Product Hunt) · researched Aug 11, 2026.
- +Zero-shot labeling saves hours of manual annotation time.
- +Works across image, video, text, audio, and DICOM.
- +Simple text-prompt workflow — no training data needed.
- +Integrates with AWS, GCP, Azure, and Hugging Face.
- +Free Researcher plan offers 2,500 credits to test.
- −Very few independent user reviews — hard to gauge real-world reliability.
- −$9,999/year Pro plan is steep for small teams or startups.
- −Zero-shot accuracy on niche objects remains unproven.
- −Support responsiveness not mentioned in the community data.
- −Hype from launch comments may not reflect sustained performance.
- • Cost per credit above the included quota is not disclosed.
- • Enterprise custom pricing likely includes additional services not itemized.
Viability Score
How well maintained and how widely used is LabelGPT? 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: September 2026
How we score →Key Features
- Zero-shot labeling
- Prompt-based labeling
- Model-assisted labeling
- Active learning
- Support for image, video, text, audio
- Support for DICOM, LiDAR, NIfTI
- Confidence-score filtering
- Human-in-the-loop review
- SDK for pipeline integration
- Cloud storage integrations
- Project management with analytics
- Dataset management module
- EDA for images
- Full EDA for all data types
- Attach custom models on demand
About LabelGPT
LabelGPT, from Labellerr, is a zero-shot auto labeling platform that uses foundation models to generate labels from a simple text prompt—no training data, no manual labeling, no one-by-one review. You type the class or object name, choose bounding box or segmentation, and the engine detects and segments labels across your dataset in minutes. Built for ML teams in computer vision, healthcare AI, autonomous vehicles, and more, it supports a wide range of data types including images, videos, text, audio, DICOM, LiDAR, and NIfTI.
Behind the Verdict
LabelGPT excels at zero-shot labeling, letting you generate labels from text prompts without training data. This is a major time-saver for exploratory projects, but the cost jumps significantly at the Pro tier. Its support for multiple data types (images, video, text, audio, medical formats) is a differentiator, and integration with cloud storage and ML pipelines streamlines workflows. However, the pricing may be prohibitive for small teams, and some advanced features like SAM integration are paywalled. Competitors like Roboflow offer more generous free tiers for computer vision, while Labelbox provides enterprise-grade collaboration but at a higher cost. LabelGPT is best for teams that need rapid pre-labeling across diverse modalities and are willing to invest in the Pro tier for automation features.
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Real-world workflow fit
Concrete scenarios for the personas LabelGPT actually fits — and what changes day-one when you adopt it.
You need to label a dataset of product images for a recommendation model.
Outcome: Upload images, type 'product', get bounding boxes in minutes, review with confidence filter, export JSON to your ML pipeline.
You need to segment tumors in DICOM scans.
Outcome: Upload DICOM files, prompt 'tumor', use SAM for segmentation, review labels, and export for training.
You need to label video frames for object detection.
Outcome: Upload video, prompt 'car' and 'pedestrian', get labeled frames, use event tagging for temporal consistency, export to cloud storage.
Use Cases
- Automatically annotate thousands of medical DICOM images for tumor segmentation.
- Pre-label video frames for autonomous vehicle perception models.
- Generate bounding boxes for retail product images via text prompt.
- Build text classification datasets for LLM fine-tuning.
- Create multi-modal datasets for visual question answering.
- Rapidly prototype a pose classifier by labeling skeleton keypoints.
Models Under the Hood
as of 2026-08-28
Limitations
- The free Researcher Plan includes up to 2,500 data credits and 1 seat, with 1,000 files, 10 projects, and all data type support.
- The Pro plan costs $9,999 annually and includes 100,000 data credits with up to 200 seats and unlimited projects.
- Pricing may be high for small teams.
- Some features may require the Enterprise tier.
as of 2026-08-31
Verification history
We have re-verified LabelGPT 5 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.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
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.
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/mo
Ideal for
Students and researchers exploring labeling with a free tier, need 2,500 credits and 1 seat.
What this tier adds
Free entry point with 2,500 data credits, 1 seat, 1 workspace, and 10 projects.
Pro Plan
$9,999/year
Ideal for
Small to mid-size teams under 200 employees needing advanced automation and more seats.
What this tier adds
Adds 100,000 credits, up to 200 seats, unlimited projects, SAM/SAM2, active learning, and human-in-the-loop services.
Enterprise Plan
Custom
Ideal for
Large organizations needing custom features, SSO, private cloud, and dedicated support.
What this tier adds
Adds multiple workspaces, unlimited credits and seats, SSO, private cloud, and dedicated ML engineers.
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 tier is generous for experimentation, but the Pro plan at $9,999/year is a big jump. For small teams, Roboflow offers a more accessible price point, while Labelbox is pricier for enterprise needs. Best suited for mid-size teams needing automation features.
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 a solo researcher, you can create an account, upload data, and run zero-shot labeling in under 30 minutes. Teams may take a few hours to set up projects, configure cloud storage, and integrate the SDK.
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.
- →From CVAT: Export your annotations in CVAT's format, then import into LabelGPT for zero-shot labeling and review.
- ↗To Labelbox: Export your LabelGPT annotations in JSON or CSV, then import into Labelbox's pipeline.
Integrations
Resources & Guides
Tutorials & Learning
Official links
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
Labelgpt vs Presto Voice
LabelGPT is for ML teams needing rapid, zero-shot annotation across diverse data types (images, video, medical imagery) with a generous free tier and flexible model integrations. Presto Voice is purpose-built for QSR chains to automate drive-thru ordering and boost revenue via upselling, but lacks self-service pricing. Choose LabelGPT if you label data; choose Presto Voice if you run a drive-thru.
Labelgpt vs Truleo
Truleo and LabelGPT serve fundamentally different purposes: Truleo is a specialized law enforcement intelligence platform connecting siloed data (RMS, CAD, jail calls) to generate automated leads and reduce report writing time, while LabelGPT is a general-purpose auto-labeling tool for ML datasets using zero-shot foundation models. Your choice depends entirely on your domain—if you're in law enforcement, Truleo is the only option; if you're building ML models, LabelGPT offers a free tier and powerful pre-labeling.
Labelgpt vs Screenplayiq
Choose LabelGPT if you need rapid, AI-driven data labeling across diverse data types (images, video, text, audio, medical) with zero-shot capabilities and SAM integration. Choose ScreenplayIQ if you are a screenwriter or producer seeking data-driven script analysis and box office predictions to improve marketability. They serve entirely different domains—LabelGPT for training data, ScreenplayIQ for creative storytelling ROI.
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Frequently Asked Questions
Best-of guides
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