Side-by-side

Scale AI vs Augment Code

Scale AIAugment Code

Bottom line: Scale AI for enterprises with large data-labeling needs; Augment Code for enterprise teams with large monorepos or multi-repo systems.

	Scale AI Data labeling and AI data platform (Meta-invested) Visit	Augment Code Enterprise AI coding platform built for large, complex codebases Visit
Votes	0	0
Pricing	Paid	Paid
Category	Coding	Coding
Tags	data labelingtraining datarlhfenterprise aimachine learning	ai-codingcoding-agententerprisecodebase-contextdeveloper-tools
Best for	Enterprises with large data-labeling needs Teams requiring RLHF and evaluation at scale Government and defense AI programs	Enterprise teams with large monorepos or multi-repo systems System-wide refactors and migrations Onboarding to big legacy codebases
Pros	Large-scale, high-quality data operations Strong RLHF and evaluation capabilities Vertically integrated platform and workforce Serves demanding enterprise and government needs Deep experience across data modalities	Genuinely strong on very large codebases via a real Context Engine Cross-file and cross-repo dependency awareness reduces manual context Strong PR-review benchmark results versus rivals Multi-surface: IDE, CLI, and cloud Remote Agents Clear no-training-on-your-code privacy stance
Cons	No self-serve or free tier Custom, enterprise-oriented pricing Meta's stake raised neutrality concerns for some labs Several competing AI labs reportedly reduced use Revenue guidance trimmed after the 2025 deal	Credit-based pricing is unpredictable for heavy agentic use and drew backlash Overkill and pricey for solo devs and small teams Benchmark comparability across tools is murky Very large codebases can still hit performance limits Sub-$1B valuation in a consolidating market implies some roadmap risk

Comparison generated from each tool's listing. Add or remove tools above to change it.