Overview
Tuning Engines and HyperLake are both products from CerebrixOS, but they serve different layers of the AI stack. Tuning Engines is a unified AI control and governance layer for teams building production intelligence across models, agents, tools, and fine-tuned systems. HyperLake is an open lakehouse infrastructure deployed inside your cloud, enabling AI agents and humans to access data safely at scale.
Feature Comparison
| Feature | Tuning Engines | HyperLake |
|---|---|---|
| Primary Focus | AI control plane: inference, routing, fine-tuning, agents | Sovereign data lakehouse for AI agent access |
| Deployment | Cloud-based platform | Inside customer VPC, private cloud, or on-prem |
| Data Sovereignty | Governance via policies | Full sovereignty — data never leaves perimeter |
| AI Agent Support | Built-in agents, MCP, guardrails, integrations | Agent-ready infrastructure with vector search, agent APIs |
| Model Management | Model routing, fallback, fine-tuning, custom models | Not a primary feature |
| Data Infrastructure | Data capture, traces, analytics | Full lakehouse: Kafka, Iceberg, Trino, governance |
| Governance | AGT YAML policies, roles, API keys | RBAC, audit logging, data contracts |
| Open Standards | OpenAI/Anthropic APIs, MCP | Apache Iceberg, Trino, Kafka |
| Pricing | Not specified; likely usage-based | Three tiers; zero compute markup |
Pricing
Tuning Engines: Pricing details are not explicitly listed on the provided website. It likely follows a usage-based or subscription model for API calls, fine-tuning jobs, and team seats. A free tier is available (Get Started Free).
HyperLake: HyperLake offers three pricing tiers: Self-Serve (guided setup with community support), Guided Launch (expert onboarding and architecture review), and Expert-Led (full deployment using RAPID™ methodology). All plans feature zero compute markup.
Pros and Cons
Tuning Engines
Pros:
- Comprehensive AI lifecycle management from inference to fine-tuning to agents
- OpenAI/Anthropic-compatible APIs simplify integration
- Built-in guardrails, policies, and team roles for governance
- Supports multiple coding-agent integrations (Claude Code, Cursor, etc.)
Cons:
- Less emphasis on data sovereignty — data may reside on platform
- Pricing details not transparent from available information
- May be overkill for teams needing only data infrastructure
HyperLake
Pros:
- Full data sovereignty — deployed in customer's own cloud or on-prem
- Built on open standards (Iceberg, Trino, Kafka) avoiding vendor lock-in
- Zero compute markup on all plans
- Designed specifically for AI agent workloads
Cons:
- Narrower focus on data infrastructure; lacks model management and fine-tuning
- Requires more setup and infrastructure management
- Less mature agent orchestration compared to Tuning Engines
Verdict
Choose Tuning Engines if your priority is a unified AI control plane for model routing, fine-tuning, and agent orchestration with broad API compatibility. Choose HyperLake if you need a sovereign data lakehouse that keeps data inside your cloud while enabling AI agent access. For teams that need both, consider using them together — Tuning Engines for AI governance and HyperLake for data infrastructure.
HyperLake
Tuning Engines