LabelGPT
LabelGPT auto-labels images from a text prompt—zero-shot bounding boxes and segmentation in minutes.
LabelGPT is worth a look if your bottleneck is the first pass of labeling, not the correction pass. Zero-shot prompt labeling plus confidence filtering is genuinely useful for retail product boxes, medical image segmentation and autonomous vehicle frame pre-labeling. The Researcher tier at $0 with 2,500 data credits and API access lets a small team test that claim before spending anything. The Pro plan is $9,999 billed annually, which only makes sense for a team that will actually use the 100,000 credits. If you need pixel-perfect ground truth at scale or on-prem from day one, Enterprise is the conversation—and Roboflow, Encord and Labelbox all compete for the same budget.
Verified 5d ago · liveness 77/100 · cite: rightaichoice.com/tools/labelgpt
- Computer vision teams with large unlabeled image sets
- Healthcare AI teams on Enterprise
- Autonomous vehicle and robotics teams
- Researchers and students (free Researcher plan)
- Teams that need cheap on-premise deployment from day one
- Projects of only a few hundred images
- Teams wanting a purely manual annotation tool
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Skip LabelGPT if your dataset is DICOM, LiDAR or NIfTI and you are not ready for an Enterprise conversation, or if a few hundred images is the whole job.
Going past the Pro plan's 100,000 data credits means buying extra credits, so high-volume runs cost more than the $9,999 annual figure suggests.
The free Researcher plan fits students and researchers experimenting under 2,500 credits. Pro at $9,999 billed annually suits small teams under 200 employees doing limited-volume production labeling. Enterprise is for teams that need DICOM/LiDAR/NIfTI, SSO, on-premise or unlimited credits. Roboflow and CVAT compete lower for image-only work; Encord and Labelbox compete for the enterprise annotation budget.
In short
LabelGPT — LabelGPT auto-labels images from a text prompt—zero-shot bounding boxes and segmentation in minutes. Best for Computer vision teams with large unlabeled image sets, Healthcare AI teams on Enterprise, Autonomous vehicle and robotics teams. Free to start; paid plans from $9,999/yr.
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.
Average across the 2 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- Zero-shot label generation from a text prompt
- Bounding box and segmentation labeling types
- Foundation-model ensemble for automated data labeling
- Confidence-score filtering for label review
- Model-assisted labeling
- Active learning
- Support for jpg, png, wav, mp4, pdf and txt files
- DICOM, LiDAR and NIfTI support (Enterprise)
- Python SDK and API access
- Connect data from AWS, GCP, Azure or local upload
- Dataset management module
- Project management and analytics
- Exploratory data analysis (EDA) for images
- Full EDA for all data types
- Human-in-the-loop annotation services
About LabelGPT
LabelGPT is the auto-labeling engine inside Labellerr's data annotation platform. You upload images from AWS, GCP, Azure or your local machine, type the class or object name as a prompt, pick bounding box or segmentation, and a combination of foundation models labels the whole set in one run. Results come back with confidence scores so you can filter the high-confidence labels and validate visually instead of reviewing every image by hand. It handles jpg, png, wav, mp4, pdf and txt on the Researcher and Pro plans, with DICOM, LiDAR and NIfTI reserved for Enterprise. It is built for ML teams in computer vision, healthcare, automotive, robotics and agriculture who need pre-labeled data fast. Where it differs from a pure prompt tool: the same platform carries dataset management, project management, EDA for images, model-assisted labeling, active learning, and a Python SDK. Labellerr also publishes comparison pages against Roboflow, Encord, Dataloop, Supervisely, Labelbox and V7 Labs.
Behind the Verdict
The pitch here is narrow and honest: replace the manual first pass. You write a class name, the foundation-model ensemble detects and segments, and you review by filtering on confidence score rather than clicking every object. For teams pre-labeling thousands of DICOM slices, retail product shots or video frames for perception models, that is the step that eats weeks. Strengths, grounded in the platform's own pages: multi-modal intake (jpg, png, wav, mp4, pdf, txt on paid tiers, plus DICOM, LiDAR and NIfTI on Enterprise), cloud storage connections to AWS, GCP and Azure, a Python SDK, and integrations that let you push labeled output straight to GCP Vertex AI, AWS SageMaker or Azure ML. Model-assisted labeling, active learning and smart data curation are listed as Pro-tier features, and you can attach your own model from Hugging Face, OpenCV, GCP Vision API or AWS SageMaker on demand. Weaknesses worth naming. Data credits are the unit of consumption and the Pro tier's 100,000 credits are what you are buying — extra credits and extra seats are priced as add-ons, with additional seats listed at $499/user and $290/user depending on the tier. DICOM, LiDAR and NIfTI support is Enterprise-only, which matters if healthcare or robotics is the whole reason you came. The Pro plan is quoted annually at $9,999, so the entry cost to leave the free tier is a five-figure commitment, not a monthly card charge. Where it fits: computer vision and multi-modal teams that already have annotation tooling and want an automated pre-labeling layer, or teams that want pre-labeling plus dataset management in one place. Where it doesn't: teams that need on-prem deployment on the cheap, or anyone doing a few hundred images as a side project.
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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 have 20,000 product photos in an S3 bucket. You connect the bucket, prompt 'product box' and 'price tag', choose bounding box, and run LabelGPT across the set.
Outcome: You filter to high-confidence boxes, spot-check a few hundred visually, export JSON, and hand the set to training days earlier than a manual pass would allow.
Your archive is DICOM. You upload studies, prompt for the structure of interest, and select segmentation as the labeling type.
Outcome: You get per-study segmentations with confidence scores, review the low-confidence cases, and export for model training without drawing contours on every slice.
You start on the free Researcher plan with 2,500 credits, load a sample from the open-source dataset library, and prompt a handful of classes.
Outcome: You see actual label quality on your own domain before committing to the $9,999 annual Pro plan.
Use Cases
- Auto-label thousands of medical DICOM images for tumor segmentation on Enterprise
- Pre-label video frames for autonomous vehicle perception models
- Generate bounding boxes for retail product images from a text prompt
- Build text classification datasets for LLM fine-tuning
- Create multi-modal datasets for visual question answering
- Filter and validate auto-labels by confidence score instead of reviewing every image
Models Under the Hood
as of 2026-10-18
Limitations
- Data credits drive cost: the free Researcher plan gives 2,500 credits, Pro gives 100,000 with extra credits sold as an add-on.
- Additional seats are listed at $499/user and $290/user depending on tier, and the Pro plan is quoted at $9,999 billed annually, so leaving the free tier is a five-figure annual commitment.
- DICOM, LiDAR and NIfTI support is Enterprise-only, and Researcher and Pro file types top out at jpg, png, wav, mp4, pdf and txt.
- Several Pro features—smart data curation, inter-annotator agreement, smart quality assurance, IP whitelisting, white-label—are marked as ask-for-add-on rather than included by default.
as of 2026-10-02
Verification history
We have re-verified LabelGPT 7 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-checked, vendor evidence unchanged
- — 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
- — 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
Showing the 6 most recent of 7 verification passes.
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, researchers and small teams testing zero-shot labeling on up to 1,000 files before spending anything.
What this tier adds
Free entry point: 1 seat, 2,500 data credits, 10 projects, up to 100 annotation projects, all data type support and API access with SDK.
Pro Plan
$9,999/year
Ideal for
Small teams and businesses under 200 employees running limited-volume production labeling.
What this tier adds
Adds 100,000 data credits (extra credits purchasable), up to 200 seats, unlimited projects, advanced automation (SAM, SAM 2, SDK, active learning, model-assisted labeling) and human-in-the-loop services. $9,999 billed annually.
Enterprise Plan
Custom
Ideal for
Organizations needing DICOM, LiDAR or NIfTI data, SSO, private cloud or on-premise deployment and dedicated ML engineers.
What this tier adds
Adds multiple workspaces, unlimited data credits and seats, SSO, enterprise SLA and support, private cloud and on-premise, custom development and custom export formats.
Where the pricing makes sense
The company stage and team size where LabelGPT's pricing actually pencils out — and where peers do it cheaper.
The free Researcher plan fits students and researchers experimenting under 2,500 credits. Pro at $9,999 billed annually suits small teams under 200 employees doing limited-volume production labeling. Enterprise is for teams that need DICOM/LiDAR/NIfTI, SSO, on-premise or unlimited credits. Roboflow and CVAT compete lower for image-only work; Encord and Labelbox compete for the enterprise annotation budget.
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.
Researcher plan: minutes to first labels—sign up, upload a sample, type a prompt. Pro plan: expect a short onboarding with customer onboarding support, then a data-source connection and first full run in under a day. Enterprise with on-premise or private cloud: plan for a longer deployment and SLA-led onboarding.
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 manual annotation in spreadsheets or CVAT: upload the same image set, prompt the classes, and use the output as a pre-label pass before human review.
- →From a cloud storage workflow on AWS, GCP or Azure: connect the bucket directly and run LabelGPT in place instead of downloading files.
- →From a custom in-house labeling script: call the Python SDK or API to generate labels and export JSON and CSV into your existing pipeline.
- ↗To Roboflow: export your labeled set as JSON and CSV and re-import into Roboflow for training and deployment.
- ↗To Labelbox or Encord: export JSON and CSV, then map the class schema into the new platform's ontology.
- ↗To an in-house pipeline: push exports straight to GCP Vertex AI, AWS SageMaker or Azure ML for training.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “LabelGPT”, and we withheld 6: 6 could not be judged, because “LabelGPT” 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 LabelGPT.
Official links
Tools that pair well with LabelGPT
Common stack mates teams adopt alongside LabelGPT, with the specific reason each pairing earns its keep.
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Outlier AI
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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.
Alternatives to LabelGPT
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Coram AI
Coram AI turns existing IP cameras into an AI video security system with natural language search and autonomous investigation agents.
Viz.ai
Viz.ai is AI care coordination software that auto-detects stroke, cardiac, vascular, and pulmonary disease from imaging and alerts the
Outlier AI
Outlier AI pays subject-matter experts to train AI models remotely through flexible, project-based prompt writing and answer grading.
Frequently Asked Questions
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