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Annotide vs Labelbox

Labelbox is a hosted, proprietary annotation and data-platform SaaS aimed at larger ML teams, with usage-based pricing and managed-workforce options. People comparing it to a “self-hosted Labelbox alternative” are usually weighing its scale and polish against where their data sits and what it costs at seat count. This page compares the two directly.

Annotide Labelbox
Licence Source-available, Elastic License 2.0; a key adds users and Business features Proprietary, closed source
Deployment Self-hosted only, on your own cloud or servers Hosted SaaS (Google Cloud); on-premises/airgapped offered to Enterprise (scope not published)
Where media lives Your own storage, never copied to Annotide’s vendor Labelbox’s cloud by default
Pricing model Per seat, published: free up to 3 users; Team €12/user/month (or €29/month for 5 plus €12 per extra user); Business €20/user/month Free tier (capped: 30 users, 50 projects, 25 ontologies); Starter pay-as-you-go at $0.10 per Labelbox Unit; Enterprise custom
SSO OIDC and SAML 2.0, in Business (€20/user/month, self-serve, 30-day trial) Add-on, Enterprise accounts only — not on Free or Starter
Model-assisted labelling Bring-your-own model gateway: batch pre-labelling, interactive segmentation, active learning, retraining webhook; optional bundled SAM 2.1 image for click-to-polygon segmentation, on CPU Model-assisted labelling and a dedicated “Model” product; bring-your-own-model integration
Media types Image, video, audio, text (NER/relations), PDF, time series, LLM evaluation, multimodal Image, video, text, documents, 3D/point cloud

This is the sharpest difference between the two. Labelbox is cloud-hosted by default: your data is uploaded to Labelbox’s infrastructure (Google Cloud) for annotation. An on-premises/airgapped option is offered to Enterprise customers, but the scope of that offering isn’t published in Labelbox’s own pricing or docs, so it should be confirmed directly with their sales team if data residency is a hard requirement. Annotide has no other mode: the backend never proxies media at all, so there’s no cloud copy to opt out of — browsers read and write media via signed URLs straight to your own Azure Blob, S3, GCS or local storage.

Labelbox’s Free plan caps at 30 users, 50 projects, 25 ontologies and a monthly Labelbox Unit (LBU) allowance, with no SSO. Its Starter plan is usage-metered at $0.10/LBU, where LBU consumption differs by product (Catalog, Annotate, Model each consume LBUs differently) — a model that scales with data volume rather than seats, and complicates predicting a bill before you’ve run it. Enterprise is quote-only.

Annotide charges per seat (a human who signed in in the last 30 days), not per data row or per LBU: free for up to three users, Team at €12/user/month (or €29/month for a team of 5 plus €12 per extra user), and Business with SSO, SCIM and quality control at €20/user/month.

Labelbox’s core platform is closed-source, proprietary SaaS; some of its SDKs and tooling are open source (Apache 2.0/MIT), but the product itself isn’t something you can read or self-host. Annotide’s source is available to read, modify and run under the Elastic License 2.0, commercially too, with a key for more than three users or the Business features.

  • You want a fully managed SaaS with no infrastructure to run, and your data governance allows storing media in Labelbox’s cloud (or you can negotiate their Enterprise on-premises option).
  • You need Labelbox’s dedicated Model product and large-scale Catalog tooling for very large unstructured datasets.
  • You want access to Labelbox’s managed labeling-workforce services, which Annotide does not offer — Annotide is software you or your own annotators run.
  • Your media legally or contractually cannot leave your own cloud account — there’s no vendor cloud in the data path to negotiate around.
  • You want SSO at a published per-seat price you can buy and trial yourself, rather than an Enterprise add-on.
  • You want predictable, flat per-seat pricing instead of usage-metered units that vary by which product feature touched the data.
  • You need LLM evaluation (ranking, scoring, conversations) and audio/time series in the same tool as image and video annotation.