Vendor profile · Directory · Platform
Dataloop
An Israeli data-pipeline and annotation platform with a strong autonomous-vehicle story — acquired by Dell Technologies in December 2025.
| Model | Data management + annotation platform (now part of Dell Technologies) |
|---|---|
| Founded | 2017 |
| Headquarters | Herzliya, Israel |
| Pricing | Not published — free tier reported; paid plans sales-led |
| Website | dataloop.ai |
What Dataloop does
Dataloop was founded in 2017 in Israel by Eran Shlomo, Avi Yashar and Nir Buschi. The platform combines data management, pipeline orchestration (including a Python SDK and serverless functions) and annotation, with a dedicated sensor-fusion capability unifying multi-angle video, cameras, LiDAR and infrared in one annotation view.
It raised $33M in a 2022 Series B and published an AV case study with Foresight Automotive. In December 2025, Dataloop was acquired by Dell Technologies, per CB Insights.
Strengths
- Pipeline automation and SDK are first-class, not add-ons
- Sensor-fusion annotation for AV programs
- Dell ownership brings enterprise distribution and support depth
Considerations
- Post-acquisition roadmap is the open diligence question
- No published pricing
- Small company (~80 staff reported pre-acquisition) relative to the platform majors
Other platforms in the directory
- Labelbox — The platform pick: run annotation in-house on published usage pricing, and bolt on expert services (Alignerr) only where you need them.
- Prolific — The transparency benchmark: a self-serve participant platform with fully published pricing and an enforced participant pay floor.
- Kili Technology — A Paris-built enterprise platform for annotation and LLM evaluation, sold to regulated industries that need data quality controls more than raw labeling volume.
- SuperAnnotate — A multimodal annotation platform that has grown an expert-services arm (SME) hiring domain specialists for AI training and evaluation work.
- Encord — A data infrastructure platform — Annotate, Index, Active — built for physical AI: robotics, autonomous vehicles and sensor-heavy multimodal data.
- CVAT — The open-source annotation workhorse: born at Intel, now run by CVAT.ai as a free self-hosted Community edition plus paid cloud and enterprise tiers with published prices.
- Label Studio (HumanSignal) — The most popular open-source labeling tool — multimodal by design — with HumanSignal selling cloud and enterprise editions and optional expert data services.
- Lightly AI — An ETH Zurich spin-off built on one idea: label less, but label the right data — curation and self-supervised pretraining first, annotation second.
- BasicAI — A platform-and-services company built around multi-sensor data — LiDAR fusion annotation for automotive and robotics is its home ground.
- V7 — The London platform company behind Darwin — AI-assisted visual annotation with a low-cost self-serve entry — now also selling document-workflow agents as V7 Go.
Frequently asked questions
What does Dataloop do?
An Israeli data-pipeline and annotation platform with a strong autonomous-vehicle story — acquired by Dell Technologies in December 2025.
How much does Dataloop cost?
Dataloop does not publish pricing (not published — free tier reported; paid plans sales-led). Any figure quoted by third parties is an estimate; budget discovery requires engaging the vendor directly.
Who is Dataloop best for?
Engineering-led teams — especially in the AV ecosystem — that want annotation wired into automated data pipelines rather than a standalone tool.
Who should not use Dataloop?
Buyers who need certainty about standalone product direction before the Dell integration settles.
Sources
- Dataloop raises $16M for its AI data platform — TechCrunch (2020)
- Dataloop company profile incl. Dell acquisition — CB Insights (accessed Nov 2026)
Facts on this page were drawn from the public sources above, checked November 2026. If you spot an error, tell us at the corrections address in the footer — we fix sourced errors and note the change.
Profile last updated: November 8, 2026 · How we write profiles · Corrections: [email protected]