7 best video data providers for AI in 2026

The useful comparison follows the footage from recording to permitted use and final delivery. These five tests apply whether the proposal covers an existing library or commissioned work.

1. Keep the action you need in view

A selected scene, a complete task and synchronized views are different purchases. Start with the sequence the model must see, then inspect its beginning, end and any cuts. In the hypothetical pick-and-place task, footage of the pickup cannot answer a question about placement. An archive owner can make the same distinction by identifying edits, missing context and viewpoint changes before offering the material.

A hypothetical red flag is: “It is high resolution, so scene boundaries should not matter.” Resolution cannot replace the missing part of an action.

2. Match permissions to the recording and its use

Inspection, training and redistribution need to be addressed separately in the proposed agreement. Trace the authority to grant each intended use, including any third-party material and exclusions. The test is whether the relevant provenance and release evidence supports the offered scope. Owners need that scope to describe what they are granting; buyers need it to describe what they can do with the delivery.

“It is online, so you can train with it” is a hypothetical red flag. An agreement that is not public calls for an answer from the provider, not an assumption that rights are absent.

3. Specify the files and labels you will receive

Follow one annotation from its file to the correct moment in a sample video. That exposes whether identifiers, timestamps and label definitions connect. Captions, action boundaries, object interactions and pose serve different purposes; a delivery containing one does not automatically contain the others. Raw footage is a valid choice when the buyer intends to create the labels.

A hypothetical red flag is: “It is training-ready; we can settle the label format after delivery.” Owners should distinguish annotations already present in the archive from work still to be commissioned.

4. Compare quotes for the same finished delivery

Use one scope to compare licensing, preparation, annotation, review, corrections and transfer. Establish whether the billable unit is supplied, recorded or accepted duration, a project or something else. An owner also needs compensation and its payment trigger stated separately from the buyer's total. A scoped proposal can answer these questions without a public price list.

“The minute price excludes your required labels; we will price those after you commit” is a hypothetical red flag. The cheaper recording is not necessarily the cheaper finished delivery.

5. Agree how the full batch will be checked

A showcase sample demonstrates only what it contains. The acceptance plan needs to cover the actual delivery: who checks it, how ambiguous actions are resolved and what happens when a sequence or label fails. A demonstration against agreed criteria is more useful than a broad quality claim. For owners, known exclusions and transformations belong with the sample so it does not imply coverage the archive cannot supply.

A hypothetical red flag is: “The showcase looks good, so the full batch is accepted automatically on upload.”

What you pay for

“Not published” means absent from the relevant public pages checked for this guide, not unavailable by negotiation. Questions identify terms to settle in the selected written offer before committing.

Rightmark: licensing an existing footage archive

We arrange licenses between footage owners and AI buyers. Our video route starts with unused footage and B-roll, with ownership retained through non-exclusive video-model licenses or pooled creator revenue share. For an owner, the asset is material already recorded: its scenes, metadata and usable rights. For a buyer, the job is finding an archive that contains the required footage. A sourcing category is a starting point for that search, not a promise that a matching batch is ready.

Our trust rule is explicit: “Nothing is licensed until you sign, and each agreement names the permitted uses.” The owner decides which uses to grant before licensing begins. On standard licenses, “the buyer pays our fee on top” of the agreed owner payout. That separates what the owner receives from the buyer's total cost. Premium and bespoke deals instead leave owners 75% to 85%, agreed in writing. Buyers see reports and samples under NDA before signing, then receive graded batches with metadata and an ownership pack against a written scope. The workflow adds a second checkpoint: “You scan again on receipt.” Sample evaluation and a training license remain separate permissions.

This route suits a library that can be described and licensed. For commissioned task capture or temporal annotation, Shaip and Appen publish more detailed specifications than our video pages. The aerial-video lot with flight logs is still marked in preparation. Agree the video acceptance test, correction process and any remedy in writing before licensing. The distinction matters to both sides: existing footage may be licensable without becoming a commissioned recording or labeling service.

Choose Rightmark for licensing an existing footage archive; choose a specified capture or annotation service when your purchase depends on creating missing recordings or labels.

Protege: curated scenes paired with language material

Protege gives video-model buyers a route into professionally produced film, television, dialogue and sports material. Its video offering includes curated scenes as well as footage paired with language material. The attraction is selection from existing production: a buyer can look for a movement, conversation or visual setting already present in the library. For owners, the partner program describes a revenue-share agreement. They retain ownership and grant limited AI-use rights, excluding public exhibition and redistribution.

The SHOT Human Movement subset selects scenes around full-body motion and continuity, filtering for camera movement, lighting and distracting subtitles. This is curation within a recorded scene; it does not establish that a clip contains an entire procedure. Script and subtitle pairing adds another selection dimension. A dialogue-focused buyer can inspect the relationship between the words and the visible scene, while aligned multilingual audio and video suit a different brief from silent motion footage. Dr. Patrick Hörl, Managing Director of Autentic, describes the owner perspective on Protege's program: “we believe deeply in the ethical licensing of content for AI training.”

Selecting existing production fixes the original viewpoint: curation cannot recover action outside the shot. A prescribed camera position, uninterrupted demonstration or synchronized sensor requirement therefore needs its own evidence. Protege is a stronger starting point for suitable audiovisual scenes than for recordings that must be created to order. The customer terms put authorized training use in the Statement of Work, which controls conflicting baseline terms. Platform access does not itself buy those training rights. For a rights holder, retained ownership and excluded exhibition rights keep the proposed AI license distinct from permission to screen or redistribute the original program.

Choose Protege for curated scenes and script-linked video; decline a clip-based offer if your task needs a newly recorded, uninterrupted procedure.

Defined.ai: video with companion data and custom collection

Defined.ai offers existing video datasets and custom collection. Its video page describes head-mounted recordings with motion or IMU sensor streams, with synchronization where available. Those streams make the purchase more than a folder of video files: the buyer is choosing footage together with information about movement. The separate collection service includes first-person footage and action sequences, giving a buyer a commissioning route when the required scene is missing from an existing package. Owners can become Data Partners earning licensing fees; AI Service Suppliers instead take collection and annotation jobs.

The published video checks cover temporal continuity, taxonomy alignment and labels, with packaging for short clips and longer recordings. For a motion-based task, continuity and synchronization answer different questions. One concerns whether the action survives across the sequence; the other concerns whether a moment in the video can be located in its companion stream. A clean image alone answers neither. An anonymous Technical Program Manager, Robotics, at a Leading Artificial Intelligence Organization says: “Their ability to source multi-sensor hardware kits gave us flexibility and confidence throughout the project.”

The public license addresses recognizable contributor outputs and separates commercial model use from redistribution, which requires written authorization. For the footage itself, the choice is between adapting the buyer's pipeline to an existing package and specifying new capture around the task. Commissioning first-person action sequences makes sense when the recording must show a particular activity; it should begin with that activity rather than a target number of hours. The task determines what must happen in view, while duration alone leaves that question open. An existing package needs a sample with its promised companion data: sensor availability and included labels determine the preparation left to the buyer.

Choose Defined.ai for video paired with companion data; decline an offer missing the redistribution rights your product requires.

Shaip: first-person task capture with pose labels

Shaip combines first-person recording and annotation in a managed service. Its physical-AI catalog describes scenarios through a goal, starting setup, materials, steps and expected outcome. That gives the recording a defined job: the take must preserve the action the scenario calls for. A kitchen or workplace category alone would leave much more for the recording team to interpret. The egocentric service lists typical MP4 video, JSON metadata and keypoint files; Parquet, WebDataset and LeRobot are available on request. Depth, body or hand pose and synchronized streams are configurable options.

In Shaip's robotics capture case, an unnamed client needed consistent tracking across varied scenes and participant setups. Calibration came before task rehearsal. Moderators then observed movement and sensor behavior before live capture, and recorded sessions went through review, with unusable recordings canceled or retaken. This places part of the quality work at recording time, while a take can still be repeated. The anonymous VP, Data & Simulation Infrastructure, says, “Shaip helped us build the data operations backbone for our Physical AI roadmap.”

The main choice is how much of the recording and annotation package to commission together. Commercial-use participant releases are part of the stated capture process, and the actual delivery remains configurable. A buyer needing pose labels has a concrete file and modality specification to compare, rather than relying on a general promise of usable video. The catalog's AI-training license is non-exclusive, so a buyer requiring sole use of the same recordings needs a different negotiated offer. That catalog term does not establish the license for every custom project. Shaip's terms leave customer refund rights to the applicable agreement; the case's retake process is not itself a refund promise.

Choose Shaip for task-specific first-person capture and pose labels; decline its standard catalog license if the same footage must be exclusive to your team.

iMerit: task capture with an evolving label taxonomy

iMerit offers first-person video collection using employees and iMerit Scholars under standardized protocols. Its service covers activity-specific recordings in indoor and outdoor settings, with raw or annotated output. That choice assigns different work to the buyer: raw footage leaves the labeling task open, while an annotated delivery needs an agreed account of the actions and objects it describes. Ango Hub's skeleton tooling adds predefined keypoint structures across video frames. It is a capability to include in the project, not a pose layer that automatically accompanies every recording. EXL announced completion of its iMerit acquisition on August 3, 2026.

The value of an evolving taxonomy is clear in iMerit's household-task case. An unnamed robotics startup needed richer classification, versioning and review than its internal tools supported. Participants recorded with Meta Quest 3 headsets; the annotation team added categories as new behaviors appeared. A utensil sequence separated identifying, selecting, cleaning and placing the object, so one broad household activity became distinct action steps. The anonymous Head of Imaging says, “consistency in labeling is just as critical as the footage itself.”

That project removed ambiguous object states, partial occlusions and unsafe motions after client review. For a failure-recovery or occlusion-handling task, some of those same recordings could be the point of the dataset. The choice belongs in the project's selection rules; the case does not establish a universal iMerit exclusion policy. This is a collection-and-annotation engagement for buyers developing a corpus. It is a different decision from licensing an existing film archive: the buyer is paying to shape what gets recorded and how it is described.

Choose iMerit for a project combining first-person capture and label development; choose a footage-licensing route when the material already exists and its owner wants to license it.

Appen: action boundaries and interaction labels

Appen's video annotation service treats an action as something that happens over time. Frame-range and clip-level labels mark its beginning and end, while human-object labels describe the interaction. This is useful when the target is a movement or use of an object rather than its mere presence in a frame. Appen also offers inferred intent as a distinct label type. Its separate catalog licenses pre-collected datasets, with specification sheets covering source, consent, annotations, limitations and license terms.

The AI Data Platform makes the review workflow configurable: task parameters, routing and multiple review stages sit alongside contributor monitoring, gold test questions and validators. The practical distinction is between producing a label and deciding whether it meets the rules. A buyer can specify separate review stages for those decisions instead of treating a completed annotation task as the final quality check. The listed controls provide ways to organize that work; the annotation rules still have to say what a correct answer means. In a September 2024 Appen publication, Francois Jehl, then identified as Director of Data and Analytics at Onfido, says: “We appreciate all the support, from the customized project plan to the weekly status meetings.”

For a dataset grounded in visible behavior, intent should remain separate from the observed action. Otherwise a precise time boundary can give an interpretation more certainty than the footage supports. Appen's distinction supports separate observation and intent labels when the task requires them. Catalog purchases follow a separate route: standard licenses are perpetual and non-exclusive with commercial training rights. NDA samples are offered for most datasets, but inspection does not authorize training.

Choose Appen for action and interaction annotation; decline an annotation-only offer if new recordings are essential to your purchase.

DataForce: robotic action and subtask annotation

DataForce's video annotation service works at three useful levels: the whole recording, a timestamped segment and an individual frame. A file-level description can identify the activity, but it cannot locate one step inside it. Segment labels add that interval; frame attributes answer a question about a particular image. That makes annotation granularity a buying choice, rather than a technical detail to settle after the work is delivered. DataForce also offers collection of gestures, object interactions and scenes across environments. Its own, client or third-party tools can be part of the proposed workflow, so the annotation service is not tied in its public description to a single tool route.

An unnamed robotics and automotive client in its robotic-arm case needed current-action, objective and subtask labels for assembly-line footage. DataForce agreed guidelines with the client, prepared annotators and reviewers through quizzes and practice sets, and screened unsuitable videos before labeling. Comparing annotation with review outputs then supplied per-video correction feedback. The client's unnamed Associate Scientist says, “It’s always a pleasure working with this team, and I look forward to future collaboration.”

The case makes early agreement on categories consequential. If a buyer has not decided where one action ends and the next begins, additional labelers would inherit that ambiguity. A definition-and-testing phase belongs before production when those boundaries remain unsettled. This is a lesson from the project, not evidence that DataForce requires every client to arrive with a finished taxonomy. For a buyer whose model needs individual steps, a proposal limited to whole-file tags would leave the central task unfinished. For an archive owner, commissioning collection is a separate transaction from licensing existing recordings; the described collection service should be evaluated on that basis.

Choose DataForce for annotation that separates robotic actions into timed subtasks; choose a footage licensor when you need an existing archive license rather than a collection or annotation project.

Narrow to 2 to 3 that cover the same job

Start with the deliverable that would remove the bottleneck: a footage license, new recordings, annotation or a specified combination. Then compare 2 to 3 providers whose proposals cover that job. One brief should state required scenes, viewpoints, permissions and labels, along with the volume and spending boundary. Work left to your own team belongs in the comparison too.

For archive owners, a collection assignment and a library license are different sources of income. Establish which transaction is proposed before comparing compensation: payment for new recordings does not price the existing archive.

Ask every candidate the same six questions

Use the sample-pack worksheet as a starting point, then add the answers, supporting evidence, dates and unresolved points to your own comparison record. The six questions below expose whether the proposals describe the same purchase. Each red-flag answer is hypothetical.

1. “Does this cover a footage license, new capture, annotation or a combination? What must we supply separately?” This separates work included in the purchase from work left to your team. Red flag: “We will decide whether to source footage or label yours after starting.”
2. “Show a sample we may inspect. Where does the required action begin and end? How will capture instructions preserve it?” The sample should reveal whether the complete required sequence survives the recording and edits. Red flag: “We do not distinguish complete tasks from clips that stop halfway through.”
3. “Who authorizes our intended use, and which terms cover the sample and final delivery, including exclusions and redistribution?” This checks who can grant each use and which exclusions limit the proposed license. Red flag: “The NDA covers anything you want to do.”
4. “Walk us through the files, identifiers, timing information and agreed labels. What must we create ourselves?” The answer reveals whether the files and annotations connect well enough to use together. Red flag: “The videos and label sheet have no matching identifiers or timestamps.”
5. “What unit triggers payment, what minimum applies, and which preparation, labeling, review and correction costs are separate?” This exposes the full cost of the agreed delivery and when payment is due. Red flag: “Required annotations will be priced after you commit.”
6. “What are the batch acceptance criteria, who resolves disputed items, and which correction, replacement or refund terms apply?” The answer assigns responsibility for acceptance, disputed items and failed deliveries. Red flag: “Upload counts as acceptance even if agreed labels are missing.”

Agree a bounded evaluation before expanding

Propose a licensed 30 to 60 day evaluation with a written start, end and review milestones. This is a buying recommendation, not a published trial offer. A model test belongs in it only if the agreed permissions allow one.

Define the tasks, scenes, viewpoints, recording count, duration cap and maximum spend. Specify raw or annotated output, file and label connections, required rights evidence and known exclusions. Set task-specific acceptance tolerances before production, including a label-definition phase if the categories remain unsettled.

Review the first delivery before authorizing the remaining volume. Name the reviewer, dispute owner and decision dates. Missing action segments, mismatched identifiers, missing permission evidence or label disagreements beyond the agreed tolerance should lead to a defined response. Put correction windows and costs, any replacement or refund, stop conditions and treatment of retained copies in writing. None of those remedies should be assumed.

If a provider declines the proposed evaluation, weigh its minimum or alternative, such as a smaller paid delivery or permitted inspection. Where no bounded fit check is possible, the unresolved uncertainty remains part of the buying decision.

License an archive when it already contains the required scenes. Commission capture when the missing action must be recorded. Buy annotation when the footage exists but the labels do not. Owners should make the same distinction before granting rights or accepting a collection assignment. A proposal that leaves the central job unfinished is not a fit, however useful its other features may be.

Use the sample-pack checklist to describe the sample's purpose, contents and gaps before comparing offers.