Artificial intelligence is no longer a laboratory experiment but a strategic priority. However, for many systems and operations teams, moving from isolated testing to production is a path full of uncertainties: What infrastructure do I need? How do I manage GPUs, Kubernetes, monitoring, and costs? How do I maintain control over sensitive data?
In this context, Rackspace Technology has introduced Rackspace AI Launchpad, a managed service specifically designed to provide companies with a private, secure, and scalable platform to evaluate, pilot, and deploy AI workloads without building infrastructure from scratch.
From the “eternal proof of concept” to AI in production
The promise of AI Launchpad is especially relevant for system administrators, SREs, and infrastructure teams: turning AI adoption into a structured process with clear phases and specialized support.
Rackspace proposes a three-phase model:
- Proof of Concept (PoC)
- A lightweight and secure environment for experimenting with AI use cases without large initial investments.
- Ideal for validating whether a model, data pipeline, or business case truly adds value.
- Virtualized infrastructure optimized for quick testing and initial adjustments.
- Pilot
- Once use cases are validated, the client moves to an environment with high-performance GPU servers.
- The goal here is to verify real-world performance, inference times, resource consumption, costs, and stability.
- This stage bridges the laboratory and operational reality: configuring, tuning models, and simulating production loads.
- Production
- An enterprise-grade environment, managed and prepared for continuous AI workloads.
- Based on Rackspace AI Anywhere and AI Business solutions, aimed at large-scale deployments and ongoing operation.
- Designed to integrate AI into critical business processes, focusing on security, performance, and governance.
For many teams, this represents a key difference: avoiding starting from zero each time moving from a prototype to a reliable 24/7 operation that is monitored, audited, and well-governed.
Managed infrastructure: ready-to-use GPUs, Kubernetes, and network
From a technical standpoint, Rackspace AI Launchpad offers what most internal teams would take months to set up, test, and harden:
- Managed infrastructure for AI workloads
- GPU clusters based on cutting-edge servers with NVIDIA® GPUs (and other accelerators as needed).
- Compute, storage, and network resources sized and managed by Rackspace.
- Security and isolation designed for sensitive environments (like healthcare or banking).
- AI-ready Kubernetes
- Preconfigured Kubernetes clusters optimized for AI/ML workloads.
- Common frameworks and tools (deep learning libraries, runtimes, etc.) already installed, avoiding “reinventing the stack”.
- Orchestration capable of scaling training and inference without the system team having to build everything from scratch.
- Managed and documented environment
- VPN access for distributed teams and remote data scientists, ensuring consistent performance.
- Documentation, onboarding guides, and best practices to help teams adapt quickly.
- Continuous monitoring, infrastructure management, and infra-level troubleshooting handled by Rackspace specialists.
- Each engagement includes up to 16 hours per month of expert support during business hours.
For a systems administrator, this means being able to focus on policies, integration, and observability, instead of battling drivers, CUDA versions, manual Kubernetes deployments, or tuning GPUDirect, storage, and network configurations.
AI Anywhere: Private AI, Data Under Control
AI Launchpad leverages Rackspace AI Anywhere, a private cloud solution designed to combine advanced AI/ML capabilities with strong security and data privacy.
This is especially relevant for sectors such as:
- Healthcare (EHR, clinical data, patient histories).
- Banking and Financial Services (BFSI), where regulation and compliance are critical.
- Energy and Industry, with sensitive operational data, IoT, and critical systems.
The proposition for system teams is clear:
- Data remains within controlled environments (third-party data centers, colocation, or managed infrastructure).
- The AI layer does not require exposing sensitive information in uncontrolled public clouds.
- Facilitates compliance with policies on data sovereignty, compliance, and auditing.
Case Study: A Clinic Turning AI into Real Productivity
For example, Rackspace highlights Compass, a U.S.-based healthcare provider that already used its private cloud to host Electronic Health Record (EHR) systems and disaster recovery environments.
Later, Compass leveraged AI Launchpad to:
- Implement a private AI solution that combines:
- NLP queries on patient records.
- Automated documentation analysis.
- Real-time reports.
- Deploy an AI agent capable of intuitive searching in clinical histories and detecting incomplete documentation.
According to Rackspace,:
- The AI assistant reduced manual review time by 80%.
- Improved documentation quality and speed for clinicians to gain insights.
- Reduced administrative burden with automated reports.
- The private, scalable environment boosted internal user satisfaction.
While results are specific to this client and Rackspace notes they may vary, the clear message for system administrators is: with the right infrastructure, AI can be integrated into critical processes without compromising security or overwhelming the team operationally.
Why It Matters for System Administrators
For a technical audience of system administrators, infrastructure architects, and platform managers, Rackspace AI Launchpad addresses several key concerns:
- Reduces “time-to-GPU”: Instead of spending months designing and deploying AI clusters, teams start from a validated baseline.
- Prevents chaotic proliferation of experimental environments: AI is managed through a phased model with governance and monitoring.
- Assists cost control: Scaling hardware and services from PoC to pilot to production is driven by actual results.
- Supports hybrid strategies: Using private clouds and colocation, it adapts well to organizations with distributed on-premise and cloud workloads.
As many CIOs and CISOs push to “do something with AI,” AI Launchpad offers a way to say yes… without overburdening or risking platform stability.
FAQ for System Administrators about Rackspace AI Launchpad
1. What advantages does Rackspace AI Launchpad have over building my own on-premises AI cluster?
It provides GPU infrastructure, managed Kubernetes, and preconfigured AI tools managed by specialists. This drastically reduces deployment time and ongoing maintenance effort. Instead of months spent integrating hardware, drivers, networking, storage, and observability, system teams can focus on policy, application integration, and cost control.
2. Is Rackspace AI Launchpad suitable for sensitive AI workloads (healthcare, banking, government)?
Yes, it is backed by Rackspace AI Anywhere, a private cloud solution optimized for security and data privacy. It enables deploying AI within third-party data centers or colocation environments, maintaining strict control over data residency and access, essential for compliance and sovereignty requirements.
3. How is scalability managed from PoC to production?
The three-phase model (PoC, pilot, production) is designed for growth. Initial use cases are validated in lightweight environments; then moved to high-performance GPU servers in pilot mode; finally deployed in enterprise environments supported by AI Anywhere and AI Business, with orchestration, monitoring, and managed support.
4. What role does Kubernetes play in Rackspace AI Launchpad for system teams?
Kubernetes serves as the standard orchestration layer for AI workloads. Rackspace provides preconfigured clusters tailored for AI/ML, with common frameworks pre-installed, simplifying deployment of training and inference pipelines. For sysadmins, this offers a consistent, automatable, and observable platform, allowing management practices similar to those in other cloud-native environments.