Overview
HyperLake and Ray 3.2 are two vastly different products aimed at distinct audiences. HyperLake is a sovereign data infrastructure platform designed for organizations that want to deploy a full lakehouse stack inside their own cloud or on-prem environment, with a focus on AI agents as primary users. Ray 3.2, on the other hand, is a cloud-based AI video creator that enables users to generate, edit, and reframe videos using advanced AI models.
While both products leverage AI, their purposes are orthogonal: HyperLake provides the underlying data and compute infrastructure for AI workloads, whereas Ray 3.2 is an application that uses AI to create video content. This comparison will highlight their features, pricing, pros and cons, and help you decide which—if either—fits your needs.
Feature Comparison
| Feature | HyperLake | Ray 3.2 |
|---|---|---|
| Primary Use Case | Sovereign data infrastructure for AI agents and humans | AI video generation and editing |
| Deployment Model | Self-hosted in your VPC, private cloud, or on-prem; multi-cloud | Cloud-based SaaS; browser and platform integrations |
| Core Technology | Open lakehouse: Trino, Apache Iceberg, Kafka, Flink, Airbyte | Proprietary AI video model with keyframes and motion transfer |
| Data Sovereignty | Full data sovereignty; data never leaves your perimeter | Data processed in vendor cloud; no on-prem option |
| AI Agent Support | Built for AI agents; agent APIs and MCP protocol | Not applicable |
| Video Generation | Not applicable | Text-to-video, image-to-video, video edit, reframing; up to 20s, 1080p HDR, 16-bit EXR |
| Keyframe Control | Not applicable | Up to 16 keyframes per clip |
| Governance & Security | Fine-grained RBAC, audit logging, data contracts | Standard SaaS security |
| Pricing Model | Three tiers; zero compute markup | Per-second pricing; free tier |
| Target Audience | Enterprises, data teams, AI infrastructure builders | Creators, marketers, filmmakers |
Pricing
HyperLake offers three tiers: Self-Serve (guided setup with community support), Guided Launch (expert onboarding and architecture review), and Expert-Led (full deployment using the RAPIDâ„¢ methodology). All plans come with zero compute markup, meaning you pay only for the underlying cloud resources you consume.
Ray 3.2 uses a per-second pricing model for video generation, with a free tier available. Exact rates depend on resolution and duration. For example, generating a 10-second 1080p clip will cost more than a 5-second 540p clip. The pricing is designed to be accessible for creators and scalable for teams.
Pros and Cons
HyperLake
Pros:
- Full data sovereignty and control
- Open standards eliminate vendor lock-in
- Zero compute markup
- Built for AI agents and future infrastructure
Cons:
- Requires technical expertise to deploy and manage
- Not a turnkey solution for non-technical users
Ray 3.2
Pros:
- No high-end GPU required
- Native 1080p HDR and 16-bit EXR output
- Four workflows in one model
- Accessible via browser and major creative platforms
Cons:
- Limited to video generation and editing
- No on-prem or self-hosted option
- Per-second pricing can add up for long projects
Verdict
HyperLake and Ray 3.2 serve entirely different markets. HyperLake is for organizations that need sovereign, open-source data infrastructure to support AI agents and analytics within their own cloud. Ray 3.2 is for creators and marketers who need fast, high-quality AI video generation without specialized hardware. Choose HyperLake if you're building AI infrastructure; choose Ray 3.2 if you're producing video content.