Scale GenAI Platform

What it does

Scale GenAI Platform (SGP) connects to enterprise data sources such as Confluence, SharePoint, and S3, structures that data through pipelines, and lets teams build, host, and orchestrate agents that reason over it. Human feedback, evaluation scores, and execution traces are captured so teams can monitor and debug agent behavior.

Teams typically use SGP to run the agent lifecycle in one place: data connection, agent execution for long-running and multi-agent workflows, evaluation and monitoring, and a feedback loop that feeds usage signals back into improvement. The platform is designed to work alongside existing tools, frameworks, and models, including open-weight models, and can be deployed inside the customer's own VPC on AWS, Azure, or GCP.

SGP is aimed at enterprises and regulated organizations that need audit trails, governance, and compliance support. Access is through an enterprise engagement: the page directs prospects to request a demo or talk to the team, and pricing is quoted per engagement rather than published as self-serve plans. Improvement from feedback depends on ongoing human input and the customer's data rather than occurring automatically from deployment.

What it can do

• Connects to enterprise data sources and structures data into AI-ready pipelines.
• Deploys, hosts, and orchestrates agents, including long-running async and multi-agent workflows.
• Supports multiple models, including open-weight and major frontier models.
• Provides evaluation scoring, semantic layer monitoring, and execution trace visibility.
• Captures human feedback and usage data as a learning signal for agent improvement.
• Deploys within the customer's own VPC on AWS, Azure, or GCP.

Works with

• Confluence
• SharePoint
• S3
• AWS
• Azure
• GCP

Example workflows

Monitoring a production agent

Input: an agent deployed against enterprise documents. Steps: capture each query and outcome as usage data, review traces and evaluation scores, route human feedback into the improvement loop. Output: an audit trail with source-cited answers and feedback data for improvement.

Running a long-running multi-agent task

Input: a multi-step workflow over connected data sources. Steps: host and orchestrate agents asynchronously, coordinate across agents, check traces for each step. Output: consolidated results with traces showing what each agent did.

Before you choose

• Pricing is quoted per engagement; cost requires a conversation with Scale AI.
• Self-improvement depends on ongoing human feedback and usage data; it is not automatic from deployment alone.
• Security certifications listed on the page (DoD IL4 Provisional Authorization, SOC 2 Type III, ISO 27001, FedRAMP High Authorized) are stated for Scale's offerings; confirm which apply to your deployment.

Listed under

• Coding
• DevOps and observability
• Agent monitoring
• Agent platform
• AI agents
• Data pipelines
• Enterprise
• Evaluation

Get started

Enterprise engagement; the page directs prospects to a demo and expert conversation.

1. Connect enterprise data sources such as Confluence, SharePoint, or S3 to the platform.
2. Choose and test models for your use case, including open-weight or frontier options.
3. Deploy inside your own VPC on AWS, Azure, or GCP with governance configured.

Cost details are provided by Scale AI during an enterprise engagement.

We couldn’t find published prices for Scale GenAI Platform. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• Scale GenAI Portfolio | Scale AI ↗scale.com
• Getting Started with Scale - Scale Documentation ↗scale.com
• Meet with our team | Scale AI ↗scale.com

Company website

scale.com: product, pricing and policy pages

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Human review

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knowledge_base

Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.