Scale AI Workflows Across Cloud and On-Prem Environments
Modern AI development is multi-modal, compute-intensive and increasingly hybrid – requiring workloads to run simultaneously across on-prem and cloud environments.
From text and images to audio, video, and sensor data, today’s enterprises require flexible, secure, and scalable infrastructure to manage and process data – wherever it lives.
But balancing compute availability, data sovereignty, and operational efficiency across on-prem and cloud environments is no small feat.
The Reality of Hybrid AI Infrastructure
AI teams are often caught between competing infrastructure requirements. As development expands across cloud and on-prem environments, they face growing pressure to stay compliant, access the right compute at the right time, and manage increasingly complex operations. To succeed at scale, organizations must navigate three key challenges that define hybrid AI strategies:
1. Governance, Compliance, and Control
Sensitive data often needs to stay on-prem for regulatory, security, or sovereignty reasons. Certain workloads must run in specific regions to comply with data residency laws, and enterprises must account for disaster recovery and failover as part of regulatory readiness. Hybrid orchestration must support these controls without sacrificing flexibility.
2. Compute Access, Cost, and Portability
While the cloud provides elasticity, compute capacity – especially for GPUs – isn’t always guaranteed. Even when available, cloud costs can spike quickly, pushing teams to prefer on-prem or hybrid setups for better cost efficiency. At the same time, organizations aim to make the most of existing cloud accounts and credits, with the freedom to optimize workloads across various environments as needed. But portability remains a challenge – models trained on-prem may not run smoothly in the cloud.
3. Infrastructure Fragmentation and Orchestration Complexity
Data and compute don’t always reside in the same place, leading to latency and resource underutilization. With each cloud provider bringing its own APIs, quirks, and limitations, orchestrating AI workflows across multiple environments becomes a complex, resource-heavy task. A unified layer is needed to abstract this complexity and keep operations smooth.
Since no single environment fits all AI workloads, a hybrid strategy is essential.
The Solution: Dataloop’s Hybrid Cloud AI Orchestration
Dataloop provides a unified AI orchestration layer that connects your data, models, compute, and pipelines across any environment-on-prem, private, or public cloud ,empowering AI teams to train and deploy anywhere with full control over performance, security, and cost efficiency.
Key Capabilities of Dataloop’s Hybrid AI Cloud:
1. Unified Governance and Secure Control
Dataloop offers a single control layer to monitor and manage hybrid AI pipelines across environments. Built-in governance tools help enforce compliance policies, support data residency requirements, and simplify operations. Organizations maintain complete visibility and control over distributed compute and storage resources, ensuring smooth and secure workflow execution.
2. Flexible Compute Access with Cost Optimization
The platform supports both Dataloop-managed infrastructure and customer-provided compute, enabling intelligent scaling of GPU/CPU resources based on workload needs. Enterprises can take full advantage of their existing cloud accounts while avoiding vendor lock-in. Predictive scaling and smart scheduling help optimize costs without compromising performance—whether workloads run on-prem or in the cloud.
3. Seamless and Simultaneous Orchestration Across Cloud and On-Prem
With interoperability across major cloud providers and support for Kubernetes clusters , Dataloop simplifies cross-environment orchestration. Teams can train models on-prem and run inference in the cloud, or vice versa. The platform automatically selects the best execution environment per task and keeps datasets synchronized across storage layers, reducing operational friction and fragmentation.
Built for Enterprise AI
A Customer Success Story
An automotive company building an autonomous system needed to process massive volumes of multimodal sensor data – including LiDAR, camera footage, and radar — to improve object detection models and accelerate development cycles:
On-prem ingestion and preprocessing enabled secure, high-throughput handling of raw sensor data collected from test fleets.
Cloud-based training allowed for elastic scaling of compute for large, complex 3D and video datasets.
Dataloop’s orchestration platform synchronizes data pipelines across environments, enabling efficient data management, versioning, and continuous model improvement.
This hybrid AI approach is also ideal for other data-intensive verticals like healthcare, retail, manufacturing and media – where large volumes of unstructured data and strict compliance requirements demand flexible, scalable infrastructure.
Deployment Options To Fit Your Stack
Dataloop offers unmatched flexibility with deployment options that include public cloud, single-tenant SaaS, private cloud (VPC or dedicated), and fully on-prem environments—including secure, air-gapped setups.
Why Enterprises Choose Dataloop for Hybrid Cloud AI
Dataloop provides a unified platform to manage the full AI lifecycle – spanning data, models, and pipelines -across any environment. It simplifies infrastructure, cuts operational overhead, and ensures high performance without compromising security or compliance. Designed for real-world AI, the platform is built to scale with multi-modal GenAI workloads, giving teams the flexibility to adapt as projects evolve.
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