Ranked lists of data annotation and labeling companies: managed annotation services for computer vision, medical, speech and audio and NLP data, providers in India, RLHF and LLM training data vendors, Scale AI alternatives, annotation platforms and open source tools, and synthetic and video data companies, plus guides to annotation pricing, how RLHF works and when to outsource labeling. New annotation lists and guides appear here as soon as they are published.
Eleven data annotation and labeling companies that supply managed human workforces for AI and machine learning, from image, video and LiDAR labeling to RLHF, red-teaming and LLM evaluation, with their security credentials and best-fit buyers.
Vendors that supply human feedback, fine-tuning and evaluation data for large language models, plus Scale AI alternatives and how RLHF works.
Labeling software for teams that annotate in-house, from enterprise platforms to open source tools, with head-to-head comparisons.
What annotation costs, how vendors price their work and when to keep labeling in-house instead of outsourcing it.
Managed annotation services ranked by data type and region: computer vision, medical, speech and audio, NLP and providers in India.
Trust and safety work overlaps with human review and red teaming, so these providers sit alongside the annotation market.
Companies that generate synthetic training data or collect large-scale video data when real-world labels are scarce or sensitive.
Ranked Lists
Our editorial rankings of annotation vendors, training data providers and labeling platforms, side by side.
Data annotation, also called data labeling, means adding labels to raw data so machine learning models can learn from it: boxes and masks on images, tags on text, transcripts for audio. It now also covers human feedback data for large language models, such as ranked responses and expert-written examples.
Most vendors price per label, per annotator hour or as a managed program with a dedicated team. Simple image tasks can cost cents per object, while expert work such as medical or coding data for LLMs is billed at specialist hourly rates. Our data annotation pricing guide explains each model.
In-house labeling gives more control over sensitive data and edge cases, while outsourcing adds scale and speed without hiring. Many teams run a platform internally and bring in a vendor for volume. See in-house vs outsourced data labeling.
Each company is checked on its own website for the data types it handles, where its teams work, its security credentials and who owns it. Placements are editorial, and lists are updated as providers change.
Annotation, training data and labeling platform companies can submit their details through our submission page to be considered for new lists and interviews.
Get your company considered for ReVerb's data annotation lists, training data rankings and interviews.
