Decentral AI provides an enterprise AI orchestration platform that runs large language model inference across on‑premises, hybrid‑cloud, and edge environments, keeping data within the corporate perimeter. The platform includes secure data connectors, policy‑driven compliance, and a cost optimizer that selects the lowest‑cost compute node, while offering a unified API and observability console for integration and governance.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises are forced to run AI workloads on centralized cloud services, which limits control over model ownership, exposes sensitive data to third‑party providers, and makes compliance with regulatory governance difficult. The resulting unpredictable pricing and vendor lock‑in further hinder strategic AI adoption at scale.
Solution
Decentral AI delivers an enterprise‑grade AI orchestration platform that lets organizations run internal Large Language Models (LLMs) directly against their own data while retaining full governance. The platform abstracts compute across on‑premises, hybrid‑cloud, and edge environments, enabling decentralized processing that reduces infrastructure spend and eliminates vendor‑specific lock‑in. Built‑in security enclaves and policy‑driven data connectors ensure that proprietary information never leaves the corporate perimeter. A unified API and dashboard provide real‑time monitoring, cost analytics, and compliance reporting, giving IT and data teams predictable budgeting and auditability. By decoupling AI workloads from any single provider, Decentral AI offers a scalable, secure, and cost‑transparent foundation for enterprise AI initiatives.
Target Audience
Primary customers are large enterprises and regulated industries—such as finance, healthcare, and manufacturing—that require internal AI capabilities, strict data governance, and predictable infrastructure costs.
Features
- Orchestration engine that schedules LLM inference across on‑prem, private cloud, and edge nodes using container‑native workloads (Docker/Kubernetes)
- Secure data connectors with end‑to‑end encryption and zero‑trust authentication for direct access to internal databases and data lakes
- Policy engine that enforces role‑based access control, data residency, and regulatory compliance (GDPR, CCPA, HIPAA) at runtime
- Compute cost optimizer that dynamically selects the lowest‑cost execution node based on workload characteristics and real‑time pricing signals
- Unified REST/GraphQL API and SDKs (Python, Java) for seamless integration with existing enterprise applications and CI/CD pipelines
- Centralized observability console with telemetry, audit logs, and customizable cost dashboards for finance and governance teams
- Plug‑and‑play deployment model supporting bare‑metal, VMware, OpenStack, and major public cloud providers (AWS, Azure, GCP)