Enterprises are facing rising pressure to scale AI and machine learning capabilities, but many struggle to overcome the operational complexity, high costs, and fragmented infrastructure that stand in the way. Parallel Works, known for its ACTIVATE control plane for hybrid multi-cloud computing, has introduced ACTIVATE AI, a next-generation orchestration platform designed to streamline enterprise-grade AI deployment.
ACTIVATE AI integrates Kubernetes, GPU-as-a-Service (GPUaaS), and Neocloud support into a single, unified control plane, providing flexibility, scalability, and production-readiness for AI workloads across any cloud or on-premises environment.
The Challenges in AI Infrastructure Today
- Fragmented environments create deployment friction and delay time-to-value.
- High GPU costs and underutilized resources waste budget and hinder performance.
- Lack of orchestration tools across hybrid and legacy systems creates operational bottlenecks.
- Deep Kubernetes expertise is often required, creating resource constraints.
- No unified control for diverse infrastructure blocks the path to production-ready AI.
ACTIVATE AI: Solving Enterprise AI Challenges at Scale
1. Kubernetes Integration Without Complexity
- Seamlessly connects to existing Kubernetes clusters—on-prem or in the cloud.
- Offers fine-grained resource control across CPU, GPU, and RAM per namespace or project.
- Eliminates the need for deep Kubernetes knowledge to scale AI infrastructure.
2. Chargeback & Showback Capabilities
- Enables usage-based pricing for internal departments.
- Delivers transparent insights into resource consumption and cost accountability.
- Supports precise budgeting and operational efficiency for IT and finance teams.
3. GPU Fractionalization with Juice Labs
- Dynamically partition and share GPUs (MIG-enabled or non-MIG) across multiple users.
- Helps avoid GPU overprovisioning and underutilization.
- Maximizes return on expensive GPU infrastructure investments.
4. Multi-Infrastructure Orchestration
- Unified management across:
- Kubernetes clusters
- Batch schedulers
- Virtualized environments
- Allows for seamless workload migration and policy-based execution across platforms.
5. Hardware Portability & Flexibility
- Supports NVIDIA, AMD, and Intel AMX architectures.
- Ensures cross-platform performance and avoids vendor lock-in.
- Empowers teams to run inference, training, or simulation workloads in heterogeneous environments.
Accelerating Enterprise AI: From Research to Production
ACTIVATE AI is designed to empower industries where high-performance computing (HPC) and AI are converging—such as:
- Aerospace & defense
- Climate modeling
- Water forecasting
- Academic research
- Government and secure computing
With support for GPUaaS, encrypted environments, and data residency compliance in regions like Europe and Iceland, ACTIVATE AI is tailored for secure, global AI operations.
“ACTIVATE AI is the bridge between complex hybrid infrastructure and practical, scalable AI deployment.”
— Matthew Shaxted, CEO, Parallel Works
Industry Validation and Use Cases
- Collaboration with Neocloud providers ensures real-world validation and deployment-readiness.
- Orion Space Solutions, for example, uses ACTIVATE AI to deploy models across hybrid environments with mission-critical flexibility.
- Enables researchers and commercial teams to train, test, and deploy models at scale without reinventing infrastructure.
“This flexibility will be crucial in advancing our mission objectives efficiently.”
— Junk Wilson, SVP, Orion Space Solutions
Pricing & Availability
- ACTIVATE AI is now available, bundled with existing ACTIVATE user seat licenses.
- No additional setup or licensing required for current ACTIVATE customers.
With ACTIVATE AI, Parallel Works delivers a powerful solution to the most pressing challenge in enterprise AI: orchestrating complex, hybrid infrastructure into a single, manageable control plane. By bridging legacy systems, modern cloud-native tools, and GPU-intensive workloads into a unified framework, ACTIVATE AI helps enterprises accelerate the shift from experimentation to production, while optimizing performance and spend.
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