# Scale AI

Scale AI provides data labeling, model fine-tuning, and evaluation services to help enterprises build and deploy accurate, production-ready machine learning models.

## Enterprise LLM Platforms

**Specialization:**  
Data Labeling, AI Training Data, Computer Vision, Natural Language Processing, Sensor Fusion, 3D Sensor Data, Synthetic Data Generation.

**Company Type:**  
Startup

**Total Financing Amount:**  
$15.9B

**Founded Date:**  
June 2016

## Table Of Contents
- [Description](#description)
- [Customers](#customers)
- [Problem They Solve](#problem-they-solve)
- [Pros](#pros)
- [Cons](#cons)
- [Investors](#investors)

## Description

Scale’s Data Engine manages large-scale data labeling across formats like 3D, images, text, and audio using APIs to distribute tasks efficiently. It blends AI pre-labeling with expert human review to create high-quality training data for model development. The GenAI Platform supports fine-tuning and reinforcement learning (RLHF) by routing enterprise data through models from OpenAI, Anthropic, and Meta. AI teams use curation tools to find valuable unlabeled data with active learning. Developers can also deploy pre-built apps, like Donovan, built for defense analytics. All results feed into evaluation tools that benchmark model performance against custom enterprise goals.

## Customers

Meta  
Time  
Cisco  
Opensea  
Cohere  
Microsoft  
Adept

## What Problem Does Scale AI Solve?

AI teams often face delays getting the high-quality labeled data required to train machine learning models, spending months on manual annotation or dealing with inconsistent results. This bottleneck delays product launches, increases development costs, and results in poorly performing AI applications. Scale AI provides professional data labeling and annotation services that deliver consistent, accurate training data at scale, allowing teams to focus on model development instead of data preparation.

## Pros

- **High-Quality Data Labeling:**  
Scale AI provides precision crowd-verified annotations—including vision, text, and sensor data—accelerating model training.
- **End-to-End Pipeline Support:**  
Offers management tools, workforce orchestration, and review mechanisms to maintain labeling accuracy at scale.
- **Vertical-Specific Solutions:**  
Tailors annotation workflows to sectors like autonomous vehicles, geospatial, and enterprise search use cases.

## Cons

- **Cost Sensitivity:**  
High-fidelity labeling at scale can be expensive and may affect project budgets for continuous data annotation.
- **Workflow Rigidity:**  
Predefined pipelines may lack flexibility for emerging use cases or unstructured data types.
- **Data Security Responsibility:**  
While secure, enterprises are accountable for handling sensitive data during annotation workflows.

## Investors

Tiger Global Management  
Wellington Management  
Meta  
Y Combinator  
DFJ Growth  
Human Capital  
Ruttenberg Gordon  
Accel  
Guillermo Rauch  
Next Play Ventures  
Intel Capital  
Index Ventures  
Thrive Capital  
Founders Fund  
Spark Capital  
Qualcomm  
Cisco Investments  
Bradley Horowitz  
Durable Capital Partners  
Coatue  
Kevin Systrom  
Scott Belsky  
Dragoneer Investment  
Amazon  
Geodesic Capital  
Bryan Rosenblatt  
NVIDIA  
Greenoaks  
Adam D'Angelo  
Nat Friedman  
Justin Kan  
Drew Houston  
Charlie Cheever  
Outlander Fund  
Mike Krieger  
Greg Brockman  
Junipero Capital  
AMD Ventures  
Ilya Sukhar  
Raison  
ServiceNow Ventures  
Alspace Ventures  
WCM Investment Management  
Quantra Ventures

Last updated: November 13, 2025