Scale vs Encord

Manage, curate, and label multimodal AI data. Achieve scale without Scale.

Maximize AI project budgets by efficiently creating high-quality, balanced datasets to improve model performance. Join AI teams choosing Encord's neutral, software-first platform following Meta's investment in Scale.

Powering the world's leading AI teams

Optimize data labeling budgets by cleaning data before annotation

With teams leaving Scale after the Meta deal, many discover duplicate or low-signal assets that were inflating budgets. Encord’s Index auto-deduplicates and surfaces long-tail edge cases—cutting annotation volume by 25–40% before the first label is drawn.

Curate high quality data

Seamlessly search, filter, and organize the most relevant diverse dataset for labeling and model training. Encord flags blur, bias, and class imbalance up-front, so ex-Scale customers report F1 uplift after one cleansing pass.

Automatically clean datasets

Move existing Scale projects—images, video, and now LiDAR—into Encord’s micro-model pipeline with expedited migrations. Ensure that labeling efforts are not wasted on irrelevant and erroneous data that will hinder AI model performance.

Automate multimodal data annotation at scale

Move existing Scale projects—images, video, and now LiDAR—into Encord’s micro-model pipeline with accelerated timelines. Active learning then drives 10 × faster annotation with full audit trails.

Evaluate model performance to deploy production ready AI fast

Validate your models against your data to surface, curate, and prioritize the most valuable data for training and fine-tuning to supercharge model performance.

Fix underperforming models

Uncover failure modes and issues, export explainability reports to your team to correct issues swiftly and exceed your AI benchmarks.

Pre-empt blindspots & data-drift

Utilize advanced analytics to detect and rectify model weak spots, ensuring your model remains adaptable and accurate amidst evolving data landscapes.

Compare model performance

Integrate humans-in-the-loop to build active learning workflows to refine model performance iteratively, significantly reducing deployment timelines.

Hear from our customers

Unify your data development toolstack

Co-founder and Head of Computer Vision @ Automotus

"We now have an integrated, one-stop solution where we can manage our data and also understand our model performance – and the errors and issues where the models are not performing well – and create the feedback mechanisms to improve our models and data."

Co-founder and Head of Computer Vision @ Automotus

Why Encord?

Join thousands of ML teams deploying production-ready AI applications using best-in-class data curation, labeling, and model evaluation tools with Encord.