Xtreme
Private-deployment data annotation platform for multimodal AI training, from BasicAI.
Choose Xtreme if your annotation data cannot leave your infrastructure and you need LiDAR + camera fusion or 4D radar annotation, which most SaaS labeling tools don't cover. BasicAI's private-cloud deployment starts at $6,600/year with customizable seats, storage and model calls, and includes the full toolset plus QA and workflow management. If you only need 2D image bounding boxes, or you want to pay per seat month to month, a hosted tool such as Labelbox will cost less and start faster. The private-deployment model is the whole point here, not a limitation to work around.
Verified 6d ago · liveness 55/100 · cite: rightaichoice.com/tools/xtreme
- Autonomous driving perception teams annotating LiDAR, camera and sensor fusion data
- Robotics companies that need 3D annotation with on-premise data control
- Smart city, construction and agriculture projects with complex spatial data
- Gen AI and LLM teams creating RLHF/SFT datasets under data-sovereignty rules
- Individuals or small teams with occasional labeling needs
- Projects that only require basic 2D image bounding boxes
- Teams that want a hosted SaaS setup with no hosting overhead of their own
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Skip Xtreme if you only need 2D image bounding boxes or occasional labeling — the private-cloud deployment model and its starting price of $6,600/year are built for standing enterprise annotation programs, not one-off jobs.
Seats, storage and model calls are all customizable line items rather than fixed inclusions, so adding annotators, keeping more raw sensor data, or increasing auto-labeling volume changes what you pay.
At a starting figure of $6,600/year for private-cloud deployment, Xtreme prices like enterprise software, not per-seat SaaS. It sits above Labelbox and Supervisely for teams that only need 2D work, and roughly in line with other private-deployment or self-hosted annotation platforms. You're paying for data staying inside your environment and for sensor-fusion and 4D radar coverage, not for a low entry price.
In short
Xtreme — Private-deployment data annotation platform for multimodal AI training, from BasicAI. Best for Autonomous driving perception teams annotating LiDAR, camera and sensor fusion data, Robotics companies that need 3D annotation with on-premise data control, Smart city, construction and agriculture projects with complex spatial data. Plans from $6,600/yr.
What people actually say about Xtreme — is it worth it?
We scanned public community sources for Xtreme on Jul 5, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Xtreme? 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
- Private-cloud deployment inside your own environment
- On-premise deployment options
- 3D LiDAR point cloud annotation
- 3D sensor fusion annotation (LiDAR + camera)
- 4D radar annotation (beta)
- Image annotation: bounding boxes, polygons, semantic segmentation
- Video annotation for object detection and tracking
- Text and NLP annotation
- Audio annotation
- RLHF and SFT dataset creation for LLMs
- AI-assisted auto-labeling for images, point clouds and audio
- Customizable annotation workflows with task and role management
- Performance tracking and reporting for annotation teams
- Customizable batch QA rules with multi-level inspection
- Data conversion pipelines for dataset export
About Xtreme
Xtreme is BasicAI's data annotation platform, sold for installation inside your own environment rather than as a hosted SaaS subscription. BasicAI is currently transitioning the platform to private deployment, which is what the pricing page now describes: you customize seats, storage and model calls, and the platform runs under your own data-security policy. The toolset covers image, video, text, audio, 3D point cloud, 3D sensor fusion (LiDAR + camera) and 4D radar annotation in beta, plus RLHF and SFT dataset creation for LLM and Gen AI teams. AI-assisted labeling reduces manual work across images, point clouds and audio. Project management is built in: customizable workflows, task and role management, performance tracking, and customizable batch QA rules with multi-level inspection. BasicAI advertises 99%+ quality assurance across its data labeling work. Data conversion pipelines let you export to the formats your models expect. It's aimed at autonomous driving perception teams, robotics companies, smart city and construction monitoring projects, and Gen AI teams that can't send training data to a third-party cloud. The published price for private-cloud deployment starts at $6,600/year, with the payment cycle, seats, storage and model calls all customizable. A separate BasicAI product, Xtreme1, is open-source on GitHub.
Behind the Verdict
Xtreme's differentiator is deployment, not feature count. BasicAI states on its pricing page that it is transitioning the platform to private deployment to prioritize customer data security, and that is the version being sold today: installed in your environment, with customizable payment cycle, seats, storage and model calls starting at $6,600/year. For an autonomous driving or robotics team whose legal and security reviewers will not approve sending raw LiDAR and camera captures to a vendor cloud, that removes the blocker rather than working around it. The annotation coverage is genuinely broad. Image annotation (bounding boxes, polygons, semantic segmentation), video annotation, text and NLP annotation, audio annotation, 3D point cloud annotation, 3D sensor fusion combining LiDAR and camera, and a 4D radar tool in beta. Most competing platforms stop at 2D plus point clouds. Sensor-fusion ground truth for autonomous systems is where this platform earns its keep, and if that is your task, the shortlist is short. LLM teams are served too: RLHF and SFT dataset creation sits alongside the computer vision tooling, so a single platform can produce preference data and perception labels. AI-assisted auto-labeling applies across images, point clouds and audio, which is where the manual-hours savings come from on large projects. Operations tooling is more complete than most labeling tools: customizable workflows, task and role management, organization structure and role authority management, performance tracking, and smart quality check with customizable batch QA rules and multi-level inspection. BasicAI advertises 99%+ quality assurance on its labeling work. Data conversion pipelines handle export into the formats your training stack expects. Model training integration support is listed, and GitHub appears as the documented integration link. The honest constraints: it's a paid platform with private-cloud deployment starting at $6,600/year, so it does not fit one-off labeling jobs or a two-person team. It carries hosting overhead: someone on your side runs the deployment. BasicAI's site pairs the platform with its managed labeling services, so buyers should be clear about which they are purchasing. BasicAI is based in Irvine, California, with 160+ annotation teams and 300K+ datasets built on its tools per its own site.
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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.
Your team captures multi-sensor drives — LiDAR, camera and radar — and security review has blocked uploading raw captures to a vendor cloud. You deploy Xtreme in your own environment, load point clouds and camera frames, and use the 3D sensor fusion tool to produce fused ground truth. Auto-labeling pre-annotates the point clouds, annotators correct them, and batch QA rules plus multi-level
Outcome: Training-ready fused datasets stay inside your infrastructure, with a documented QA pass behind each batch.
You're coordinating dozens of annotators across tasks and need visibility. You set up the org structure and role authority, build a custom workflow with task assignment, and configure QA rules that match each label type. Performance tracking shows throughput per annotator and per batch.
Outcome: You can report on throughput and quality without reconciling spreadsheets from several tools.
You need RLHF preference data and SFT examples built from internal documents that can't leave the company. You use the text annotation tooling and the RLHF/SFT dataset creation features on the deployed instance, then export through the data conversion pipeline into your training format.
Outcome: A preference and instruction dataset produced under your own data policy, in the format your fine-tuning stack expects.
Use Cases
- Annotate 3D LiDAR point clouds for autonomous vehicle perception training inside your own infrastructure.
- Create fused LiDAR + camera ground truth for sensor fusion models.
- Label images and video for object detection, tracking and semantic segmentation in smart city projects.
- Build RLHF and SFT datasets for LLM and Gen AI training without exporting source data.
- Annotate 4D radar data for advanced automotive safety systems (beta tool).
- Run large annotation programs with customizable workflows, role management and batch QA rules.
Limitations
- The platform is sold as private-cloud deployment, which means someone on your side hosts and maintains it.
- BasicAI's pricing page quotes a starting figure of $6,600/year for private-cloud deployment and notes that the payment cycle, seats, storage and model calls are customizable, so the actual contract depends on your requirements and is confirmed with sales.
- The 4D radar tool is labeled beta.
- BasicAI's site pairs the platform with its own managed labeling services, so you should confirm whether you're buying software or software plus services.
as of 2026-10-02
Verification history
We have re-verified Xtreme 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.
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- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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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 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
Ideal for
Enterprise AI teams — autonomous driving, robotics, smart city, or LLM/Gen AI — that must keep training data inside their own environment and are running a standing annotation program.
What this tier adds
Starting tier: installs in your own environment from $6,600/year, with the full image, video, text, audio, 3D sensor fusion and 4D radar (beta) toolset, all teamwork features, and customizable seats, storage and model calls.
Where the pricing makes sense
The company stage and team size where Xtreme's pricing actually pencils out — and where peers do it cheaper.
At a starting figure of $6,600/year for private-cloud deployment, Xtreme prices like enterprise software, not per-seat SaaS. It sits above Labelbox and Supervisely for teams that only need 2D work, and roughly in line with other private-deployment or self-hosted annotation platforms. You're paying for data staying inside your environment and for sensor-fusion and 4D radar coverage, not for a low entry price.
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.
Private-cloud deployment means setup starts with infrastructure, not a signup form: expect days to a few weeks from contract to a running instance, depending on your environment and how much of the org structure, roles and QA rules you configure up front. A small team starting on a single image or point cloud task reaches first labeled batch sooner; a multi-team rollout with custom workflows and
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.
- →From Labelbox: map your existing label taxonomy and QA rules onto Xtreme's customizable batch QA configuration, then re-export historical datasets through the data conversion pipeline.
- →From Supervisely: bring your project structure across, rebuild the workflow as a custom Xtreme workflow with matching roles, and re-import image and video datasets.
- →From in-house spreadsheets or scripts: define the org structure and role authority in Xtreme, then set up workflows and batch QA rules to replace manual tracking.
- ↗To Labelbox: export through the data conversion pipeline into a supported interchange format, then re-import into Labelbox projects.
- ↗To a self-hosted open-source stack such as Xtreme1: use the data conversion pipeline to produce standard annotation formats the open-source tooling can read.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Xtreme”, and we withheld 6: 6 could not be judged, because “Xtreme” 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 Xtreme.
Official links
Tools that pair well with Xtreme
Common stack mates teams adopt alongside Xtreme, with the specific reason each pairing earns its keep.
Cvat
Open-source, multimodal data annotation platform for image, video, 3D point cloud, and audio labeling—self-hosted, cloud, or fully managed
Prodigy Recipes
A downloadable Python annotation tool and web app you run yourself to build training and evaluation data for custom AI, ML and NLP models.
Eventual
Daft is an open-source multimodal data engine for turning video, images, audio, and sensor data into training-ready datasets.
Featured Head-to-Head Comparisons
Xtreme vs Presto Voice
Xtreme and Presto Voice serve completely different markets: Xtreme is an enterprise data annotation platform for multimodal AI training (especially 3D LiDAR), while Presto Voice is a drive-thru voice AI automation tool for QSR chains. Your choice depends on whether you need to label sensor fusion data or automate drive-thru orders. There is no overlap in use case, pricing model, or integrations.
Xtreme vs Truleo
Xtreme and Truleo target completely different domains: Xtreme is an enterprise-grade data annotation platform for multimodal AI training (3D LiDAR, sensor fusion, LLM), while Truleo is an AI intelligence assistant for law enforcement to connect siloed data and generate leads. Your choice depends on whether you need to label complex data for autonomous systems or streamline police investigations. No crossover in use cases.
Xtreme vs Screenplayiq
Choose Xtreme if your team needs enterprise-grade, secure annotation for multimodal AI training (especially 3D/radar). Choose ScreenplayIQ if you're a screenwriter or producer wanting data-driven script feedback and box-office forecasting. These tools serve completely different domains, so the decision hinges on your industry.
Alternatives to Xtreme
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Cvat
Open-source, multimodal data annotation platform for image, video, 3D point cloud, and audio labeling—self-hosted, cloud, or fully managed
Prodigy Recipes
A downloadable Python annotation tool and web app you run yourself to build training and evaluation data for custom AI, ML and NLP models.
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