Xtreme
PaidPrivate-deployment data annotation platform for multimodal AI training.
By Tanmay Verma, Founder · Last verified 06 Jul 2026
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Added 5d ago
77/100Safe BetIn short
Xtreme — Private-deployment data annotation platform for multimodal AI training. Best for Autonomous driving perception teams annotating LiDAR + camera fusion data, Robotics companies needing 3D point cloud and sensor fusion annotation, Smart city and construction site monitoring projects with multimodal data. Plans from $6600/mo.
Is Xtreme actually worth it?
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Editorial Verdict
Best for
Autonomous driving perception teams annotating LiDAR + camera fusion dataRobotics companies needing 3D point cloud and sensor fusion annotationSmart city and construction site monitoring projects with multimodal dataGen AI and LLM teams creating RLHF/SFT datasets in a secure environmentEnterprises requiring on-premise or private-cloud data labeling with data sovereignty
Not ideal for
Individuals or small teams with occasional labeling needs — no free tier or monthly planProjects that only need basic 2D image annotation without 3D or radar supportTeams needing real-time collaboration on large datasets without a paid enterprise planOrganizations that prefer SaaS-style per-seat monthly pricing over annual private-cloud fees
Xtreme1 is a strong choice for enterprises annotating LiDAR+camera fusion data who need on-premise deployment. Its 3D sensor fusion and 4D radar tools are rare to find in a single platform. However, budget-conscious teams or those needing only basic 2D annotation should consider alternatives like Labelbox or Supervisely, which offer monthly subscriptions and free tiers.
Skip Xtreme if Skip Xtreme1 if you need a free or monthly subscription, only basic 2D annotation, or prefer a SaaS platform with per-seat pricing.
Compare with: Xtreme vs PublicAI, Xtreme vs ScreenplayIQ, Xtreme vs Truleo
Last verified: July 2026
What independent users actually report about Xtreme
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.
91 mentions across 7 sources (Hacker News, Product Hunt, App Store, Bluesky, Stack Overflow, GitHub, Lemmy).
11% positive89% critical
Recurring strengths
- +Only open-source tool supporting 3D LiDAR + camera fusion.
- +Supports multimodal: 3D, image, video, text, audio, LLM.
- +AI-assisted auto-annotation speeds up labeling workflows.
- +Private-cloud deployment starts at $6,600/year for data security.
- +Role-based management and custom QA rules for teams.
Recurring frustrations
- −App Store reviews are for a different product, not Xtreme1.
- −Local deployment often fails with 401 login errors.
- −Missing basic features like copy-paste between frames.
- −CUDA errors prevent model annotation on some GPUs.
- −Camera-LiDAR calibration plot is inaccurate.
Patterns worth knowing
Powerful open-source LiDAR annotation tool – best in class for fusion.
Seen on GitHub
Deployment is buggy: HTTP 401 errors and CUDA compatibility issues plague setup.
Seen on GitHub
Missing basic features like copy-paste between frames, forcing manual rework.
Seen on GitHub
Learning curve
intermediateProductive in ~A few hours to days (depending on infrastructure)
Hidden costs people mention
- • Deployment requires significant infrastructure and GPU resources.
- • Enterprise support likely extra.
Viability Score
77/100
Safe BetHow likely is Xtreme 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
- 3D LiDAR point cloud annotation
- Image annotation (bounding boxes, polygons, semantic segmentation)
- Video annotation
- Text annotation
- Audio annotation
- LLM and Gen AI annotation (RLHF, SFT)
- 3D sensor fusion (LiDAR + camera) annotation
- 4D radar annotation (beta)
- AI-powered auto-labeling and model-assisted annotation
- Scalable workflow management (task, role, performance)
- Custom QA rules and data conversion pipelines
- Role-based organization and authority management
- Private-cloud or on-premise deployment
- Model training integration support
- Multi-level quality assurance (99%+ QA)
About Xtreme
PaidIntermediateAPI availableWeb
Xtreme1 (BasicAI) is a private-deployment data annotation platform built for enterprises that need to label complex multimodal datasets — 3D LiDAR point clouds, images, video, text, audio, and LLM training data — all within a single interface. Its AI-powered toolset includes auto-labeling, model-assisted annotation, and a scalable workflow engine with role-based management, custom QA rules, and data conversion. The platform supports 3D sensor fusion (LiDAR + camera) and 4D radar annotation (beta), making it a strong choice for autonomous driving, robotics, and smart city projects where data security is a priority. Pricing starts at $6,600/year for private-cloud deployment, with no free tier — designed for teams that need secure, on-premise infrastructure over SaaS convenience.
Behind the Verdict
Xtreme1 stands out for its comprehensive support of multimodal annotation covering images, video, text, audio, 3D point clouds, sensor fusion, and even beta 4D radar. The AI-assisted auto-labeling and scalable workflow with custom QA rules are valuable for large teams. The private-cloud deployment model is a major plus for data-sensitive industries like autonomous driving and defense. However, the lack of a free tier or monthly subscription makes it inaccessible for small teams or individual researchers. The web-only platform with no mobile or desktop apps may limit flexibility. Integration options are minimal (only GitHub mentioned), which could be a friction point for teams using a diverse tech stack.
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Real-world workflow fit
Concrete scenarios for the personas Xtreme actually fits — and what changes day-one when you adopt it.
Autonomous driving data manager
You need to annotate thousands of LiDAR point clouds fused with camera images for training perception models.
Outcome: Upload sensor data, use AI-assisted auto-labeling to pre-label objects, refine with manual adjustments, and export in your required format — all within a secure private deployment.
LLM training lead
Your team needs to create RLHF preference datasets from conversational data.
Outcome: Use the text/LLM annotation tools to rate model responses, perform SFT labeling, and manage the workflow with role-based assignment and custom QA rules.
Smart city project manager
You need to label video footage for pedestrian and vehicle detection across multiple intersections.
Outcome: Import video files, apply bounding boxes and tracking across frames, leverage auto-labeling to speed up the process, and run QA checks with batch validation.
Use Cases
- Annotate 3D LiDAR point clouds for autonomous vehicle perception training.
- Fuse camera and LiDAR data to create ground truth for sensor fusion models.
- Label images and videos for object detection and semantic segmentation in smart city projects.
- Create training datasets for LLMs including RLHF and SFT data.
- Annotate 4D radar data for advanced automotive safety systems.
- Manage large-scale annotation projects with custom workflows and QA rules.
Limitations
- Pricing is only offered via private deployment starting at $6,600/year, with no free tier or usage-based trial publicly visible.
- The platform is web-only with no mobile or desktop app.
- Some advanced features like 4D radar are in beta and may have limited stability.
- Integration options are limited to GitHub.
as of 2026-07-06
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
$6,600
Over 12 months
Effective monthly
$550
Implied — billed annually
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Xtreme tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Private-Cloud Deployment
$6,600/year (starting)
Ideal for
Enterprise teams with sensitive data needing on-premise or private-cloud annotation for multimodal projects (LiDAR, sensor fusion, LLM).
What this tier adds
Starting tier with all teamwork features, full toolset including beta 4D radar, custom seats/storage/model calls, and on-premise deployment options.
Integrations
GitHub
Where the pricing makes sense
The company stage and team size where Xtreme's pricing actually pencils out — and where peers do it cheaper.
Xtreme1's $6,600/year private-cloud pricing is competitive for enterprise teams needing secure, multimodal annotation, but lacks the flexibility of per-seat monthly plans offered by rivals like Labelbox ($25/seat/month) or Supervisely (free tier available). Best for mid-to-large teams with a dedicated budget.
Setup time & first value
How long it actually takes to get something useful out of Xtreme — broken out by persona, not the marketing-page minute.
For private-cloud deployment, expect 1-2 weeks to provision infrastructure and set up the platform. Teams familiar with their data pipeline can begin annotation within a few days of deployment. BasicAI offers support to accelerate onboarding.
Switching to or from Xtreme
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 Labelbox: Export your data in COCO or Pascal VOC format, then import into Xtreme1's data converter.
- →From Supervisely: Use the platform's data conversion pipeline to map project schemas.
Migrating out
- ↗To Labelbox: Export annotations in common formats (COCO, Pascal VOC) and upload to Labelbox.
- ↗To Scale AI: Use API-based batch export of your datasets.
Resources & Guides
- Resourcebasic.ai
Resources · Xtreme
Helpful link from basic.ai
- Resourcebasic.ai
Academy · Xtreme
Helpful link from basic.ai
- Resourcebasic.ai
Wiki · Xtreme
Helpful link from basic.ai
- Resourcedocs.basic.ai
Home · Xtreme
Helpful link from docs.basic.ai
- Resourcegithub.com
Xtreme1 · Xtreme
Helpful link from github.com
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