From intent to execution
Orchestration, compliance, and operations. From cloud intent to GPU execution.
Cloud platforms define intent. Dapple governs how AI workloads are executed.
Dapple operates at the execution layer between enterprise cloud control planes and private AI infrastructure. It enforces workload placement, compliance, and operational governance where cloud abstractions end.
Three layers, clear ownership: the enterprise defines policy, Dapple governs execution, and the physical infrastructure delivers compute.
Three layers, clear boundaries
Enterprise Control Plane
The enterprise control plane remains the system of record. It defines identity, policy, observability, and machine learning workflows. Dapple integrates using standard interfaces. It does not introduce a parallel control plane.
Dapple Execution Layer
Dapple governs how AI workloads are executed where cloud abstractions end. Execution decisions are evaluated before workloads run. Workloads execute correctly or not at all.
Private AI Infrastructure
The physical systems used to run AI workloads. Dapple models infrastructure explicitly rather than abstracting it away. Topology, failure boundaries, and capacity are first-class primitives.
Four integrated layers from cloud integration to developer tooling
Cloud Integration
Native integration with enterprise cloud services, identity, and observability.
GPU Orchestration
Topology-aware, deterministic scheduling across heterogeneous accelerator environments.
Compliance Layer
Policy-driven execution with audit trails, model versioning, and data residency enforcement.
Developer Experience
Native support for standard AI/ML frameworks and tooling.
Execution guarantees for private AI
Topology-Aware Scheduling
Workload placement accounts for physical GPU arrangement, network fabric, and failure domains. Infrastructure topology is a first-class primitive.
Deterministic Execution
Workloads run only when full placement correctness is verified. Requirements checked before admission.
Compliance Enforcement
Policy, isolation, and residency constraints enforced at execution time. Recorded as auditable evidence.
Managed Operations
End-to-end operational responsibility. Structured onboarding, SLA-backed support, continuous monitoring.
Designed for production performance and regulatory requirements
Optimized execution environments for high-value AI workloads
AI Training
Private infrastructure for large-scale, regulated training runs with deterministic capacity.
Inference
Scalable inference with predictable latency, cost control, and execution guarantees.
Research
Isolated environments for AI experimentation and scientific research.