5 Best Scale AI Alternatives for LLM Evals and Post-Training Data
Scale AI is the industry giant, but it isn't the right fit for every team. Here are the top alternatives for high-quality data annotation and evaluation.
Written by Hustlin from public information. MangoDesk hasn’t joined Hustlin.
Scale AI has defined the data labeling market for years, providing the massive volumes of RLHF data needed to train foundation models. However, many AI teams find that Scale's enterprise-heavy focus leads to long onboarding times, high costs, and a lack of transparency in how experts are sourced. If you need bespoke pipelines or specialized domain expertise without the overhead of a massive incumbent, these alternatives offer more agility.
First, what is Scale AI?
Best for: Large enterprises and foundation model builders requiring massive-scale data labeling with a high budget.
Strengths
- Massive operational capacity for high-volume data needs
- Broad suite of tools covering computer vision, NLP, and generative AI
- Established reputation with government and Fortune 500 clients
Where it falls short
- High minimum spend and complex contract structures
- Opaque sourcing processes for domain-specific experts
- Slow setup times for custom or experimental data pipelines
The top alternatives
- #1Top pick
1. MangoDesk: The Agile Alternative for Expert-Led Evals
MangoDesk was built by former Scale AI engineers who saw the need for a more flexible, expert-driven approach to data. Instead of forcing teams into a standardized process, MangoDesk allows AI teams to create bespoke annotation pipelines in seconds. The platform focuses on staffing projects with verified domain experts rather than generalist workforces, making it ideal for complex post-training data and rigorous model evaluations.
- Founded by ex-Scale AI infrastructure engineers with deep ops experience
- Bespoke pipeline creation that launches in seconds, not weeks
- Direct access to vetted domain experts for high-complexity tasks
- End-to-end management of the operational burden of data creation
- #2Alternative
Surge AI
Surge AI focuses on high-quality data for LLMs, emphasizing the use of native speakers and specialists for RLHF and red teaming.
Best for: NLP-heavy teams needing high-quality human feedback for language models.
- #3Alternative
Labelbox
Labelbox provides a robust software platform that allows teams to manage their own labeling workflows or use Labelbox's on-demand workforce.
Best for: Teams that want a powerful software interface to manage internal and external labeling teams.
- #4Alternative
Snorkel AI
Snorkel focuses on programmatic labeling, using 'weak supervision' to label data at scale using code and heuristics rather than manual human effort.
Best for: Enterprises with massive datasets that can be labeled via programmatic rules.
- #5Alternative
Toloka
Toloka offers a global crowdsourcing platform with a focus on data for search, ads, and generative AI, providing high scalability.
Best for: Projects requiring high-volume, global data collection across many languages.
Side-by-side comparison
| Category | MangoDesk | Scale AI | Edge |
|---|---|---|---|
| Setup Speed | Seconds to launch custom pipelines | Weeks of onboarding and sales cycles | Stronger |
| Expert Sourcing | Bespoke staffing per domain | Large-scale generalist workforce | Stronger |
| Operational Model | End-to-end managed service | Platform-centric with managed options | Neck-and-neck |
| Ideal Project Size | Agile teams and rapid iterations | Massive, long-term enterprise contracts | Neck-and-neck |
| Founder Background | Ex-Scale AI infrastructure engineers | Industry incumbent | Stronger |
Frequently asked questions
Is MangoDesk cheaper than Scale AI?
While pricing depends on project complexity, MangoDesk typically offers a more efficient cost structure for teams needing specialized expertise without the enterprise overhead of Scale AI.
Can MangoDesk handle high-volume requests?
Yes. MangoDesk is designed to scale expert-led data creation by automating the infrastructure behind sourcing and pipeline management.
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