AI Inference Strategy: Cloud, On-Prem, or Neo-Cloud?

Multi-modal Generative AI Inference Clusters: Strategies for Enterprise Deployment 

The rapid adoption of open-source AI models marks a pivotal moment for business leaders. The initial excitement about trying out large language models (LLMs) is giving way to a more practical question: where should our AI inference capacity be primarily located? The answer isn't straightforward, as it impacts cost, performance, security, and scalability. Making the right decision requires understanding the trade-offs between public cloud, on-premises infrastructure, and an expanding category of "neo-cloud" providers. 

As stewards of business and technology, we guide our partners through complex decisions, helping them forecast capacity needs and adapt infrastructure as workloads and priorities change. This article presents a structured framework for evaluating your options and developing a flexible, cost-effective AI inference strategy, with a focus on proactive capacity planning and infrastructure flexibility to support your long-term objectives. We will examine the main deployment models, the role of agentic workflows and Retrieval-Augmented Generation (RAG), and a practical roadmap for implementation. 

The Shifting Landscape of AI Deployment 

Several market trends are influencing how organizations approach AI inference. Most enterprises now rely on foundation models developed by leading AI labs, recognizing that pre-training their own models is too resource-intensive and unnecessary for their specific needs. This rapid shift away from pre-training and re-training entire datasets also prompts enterprise leaders to seek infrastructure solutions that offer greater flexibility—enabling clusters to scale responsively while maintaining firm control and consistent performance. 

• Total Cost of Ownership (TCO): Early cloud experiments can be costly. As usage increases, awareness of the long-term operational costs of inference grows, leading to a search for more affordable solutions. 

• Data Sovereignty and Privacy: Maintaining sensitive corporate and customer data within designated geographic or network boundaries is a non-negotiable requirement for many industries, affecting infrastructure decisions. 

• GPU Scarcity and Portability: The high demand for GPUs has led to supply chain issues and concerns about vendor lock-in. Forward-looking companies are developing strategies to move workloads to where the best compute resources are available and affordable. 

• Latency Requirements: For real-time applications, such as conversational AI or fraud detection, inference speed is crucial. This creates a need to position compute capacity closer to the data source or the end-user.

Comparing Your Deployment Options: Public, On-Premises, and Neo-Cloud 

Deciding on the placement of your inference capacity requires weighing the benefits and trade-offs of different deployment models based on key factors: latency, data gravity, compliance, elasticity, and the critical Gen AI metrics of performance, latency, and cost. Emphasizing these core criteria helps keep your choices focused and trustworthy. 

1. Public Cloud (AWS, Azure, Google Cloud) 

Public clouds offer the quickest way to get started with AI. Their extensive service catalogs and pay-as-you-go models provide unmatched flexibility and access to a wide range of GPU instances. 

Benefits: 

• Speed to Market: Rapidly provision infrastructure and access managed AI services to accelerate pilot projects and development. 

• Elasticity: Scale resources up or down on demand to match fluctuating workloads, optimizing for intermittent usage patterns. 

• Managed Services: Leverage pre-built MLOps tools, model APIs, and integrated services that reduce the operational burden on your teams. 

• Cost at Scale: While ideal for experimentation, running high-volume, continuous inference workloads in the public cloud can become prohibitively expensive. 

• Data Egress Fees: Moving large datasets in and out of the cloud to interact with models can incur high and often unpredictable costs. 

• Vendor Lock-In: Deep integration with a specific cloud's proprietary AI services can make future migrations complex and costly. 

• Security: For many enterprises, public cloud environments raise concerns about protecting proprietary or highly sensitive data sets. While some workloads, such as customer service bots, may be suitable, organizations are often reluctant to deploy critical use cases—such as drug pipelines or R&D Gen AI initiatives—in shared cloud infrastructure due to increased risk exposure. 

2. On-Premises Infrastructure

For organizations with strict data governance needs or consistent, high-volume workloads, on-premises deployment provides maximum control and potentially lower long-term TCO. 

Benefits: 

• Data Control and Security: Keep sensitive data entirely within your own data centers, ensuring compliance with stringent regulatory frameworks such as GDPR, HIPAA, or PCI DSS. 

• Predictable Costs: After the initial capital expenditure, operational costs are more predictable, and you avoid data egress fees. 

• Performance: Co-locating compute with your systems of record can deliver the lowest possible latency for internal-facing applications. 

• High Capital Outlay: Requires a significant upfront investment in hardware, data center facilities, and networking equipment. 

• Operational Overhead: Your team is responsible for all aspects of infrastructure management, including procurement, maintenance, and security. 

• Inflexibility: Scaling capacity requires long procurement cycles, making it difficult to adapt to sudden spikes in demand. 

3. Neo-Cloud Providers (e.g., Zenlayer, Latitude.sh, Lambda, CirraScale) 

A new type of specialized provider is emerging to bridge the gap between public cloud and on-premises solutions. These "neo-clouds" offer access to bare-metal GPU infrastructure in distributed, well-connected locations, often at a fraction of the cost of hyperscalers. 

Benefits: 

• Performance-per-Dollar: By prioritizing specialized hardware such as GPUs or LPUs (Language Processing Units) and maintaining lean operations, these providers can offer highly competitive prices for compute-intensive workloads. 

• Global Reach with Low Latency: Their distributed data centers can position inference capacity closer to your end-users or data sources, enhancing application responsiveness. 

• Flexibility and Portability: By focusing on open standards and bare-metal access, they allow the development of a portable AI stack that prevents vendor lock-in.  

• Limited Service Catalog: These providers focus on core compute, networking, and storage services. You will need to bring your own tools for MLOps, observability, and governance. 

• Maturity: As a newer market segment, the ecosystem of supporting tools and enterprise-grade features might be less developed compared to established public clouds. 

Architecting for Modern AI: Agents and RAG

Modern AI applications are increasingly built using agentic workflows, where practical agent frameworks often boil down to "LLM + tools"—with the large language model handling reasoning and supporting tools (such as APIs or task runners) driving actions and integration. While much thought is given to where the LLM itself runs, it is equally important to determine the best location for these tools.

APIs, task runners, and other components may reside within the same environment as the LLM or be distributed across on-premises, public cloud, and hybrid cloud infrastructures, depending on factors such as latency, security, and network architecture. Each placement choice affects performance, observability, and governance, making it crucial to design with both the LLM and its toolchain in mind. The decision about where all these components reside—whether co-located or connected across different environments—is a key architectural consideration. 

• Agentic Workflows: The agent itself—the LLM performing the reasoning—can operate in any of the three environments. The choice depends on latency and cost. For example, an agent running in a neo-cloud for cost efficiency can securely access a system of record (like a CRM) located on-premises via a private network connection. Observability, cost controls, and governance must be implemented at the orchestration layer managing these agents, ensuring a unified view regardless of where the inference takes place. 

• Retrieval-Augmented Generation (RAG): RAG enhances AI models by providing access to external, current information from knowledge bases. Notably, RAG processes include two separate model paths: "index-time embeddings," which are generated during data ingestion to organize and store information for retrieval, and "query-time embeddings," which are created when a user asks a question to find matching indexed data. The placement of your RAG components is vital for performance. 

• Index Locality: The vector database, which stores numerical representations of your data, should be located as close to the inference compute as possible to reduce latency and improve bandwidth. If your models run in a public cloud, your vector DB should be there too. 

• Data Synchronization: The method used to keep your vector index up to date can vary. For less active data, batch synchronization from a central data lake may be enough. For real-time requirements, event-driven methods can update the index immediately when source data changes. 

• Centralized vs. Federated Retrieval: You can use a single, large vector database for all enterprise knowledge (centralized) or develop smaller, domain-specific databases closer to their respective data sources (federated). A federated approach often offers better performance, simplifies data governance, and provides more precise domain boundaries and access controls. This aligns well with how large enterprises organize risk and compliance.

A Pragmatic Roadmap to AI Inference

Instead of relying on a single model, we recommend a phased approach that provides flexibility and safeguards the future of your investment. This strategy helps you manage speed, cost, and control as your AI experience evolves. 

Phase 1: Accelerate with Public Cloud
Start by piloting and building your AI applications on a public cloud platform. This helps you take advantage of managed services for quick iteration and verify business value without a high upfront cost. Use this stage to strengthen your security and governance policies. 

Phase 2: Optimize with Neo-Cloud and On-Premises Pilots 
Once an application shows value and a consistent usage pattern, find opportunities for optimization. 

• For latency-sensitive or data-sovereign workloads, test a deployment on-premises to ensure optimal performance.

• For cost-sensitive, high-volume workloads, consider testing a migration to a neo-cloud provider to assess performance per dollar. 

Phase 3: Cluster Optimization and Automation 
As your deployment matures, focus on optimizing and automating your AI inference clusters to improve resilience, efficiency, and ongoing innovation. Key capabilities at this stage include: 

• Auto-scaling to adapt to workload demand and maximize resource efficiency 

• Unified observability across infrastructure, models, and applications for complete visibility 

• Proactive detection of model drift ensures models remain accurate and perform well in changing environments. 

• Future-proofing for pre-fill/decode architectures to adapt to evolving AI workloads seamlessly. 

• Fully automated MLOps pipeline to speed up experimentation and simplify deployment cycles 

• Self-healing with streamlined GitOps deployment templates for resilient, reliable operations and reduced manual intervention 

We understand that AI deployment maturity is an ongoing journey, not a fixed goal. Redapt is dedicated to supporting you from start to finish—guiding your teams through every stage for lasting success and innovation.

Building Your Flexible AI Future 

Deploying a Gen AI inference cluster involves more than just choosing a location. Success relies on developing a strategy that continuously harnesses the evolving strengths of public cloud, on-premises, and neo-cloud resources to achieve your business goals. 

AI deployment maturity is an ongoing journey, not a static milestone, shaped by emerging technologies and evolving business needs. Maintaining portability, resilience, and operational consistency allows you to adapt, optimize, and innovate as the landscape changes. With a well-architected platform and continuous improvement, you turn workload placement into a business advantage rather than a fixed technical constraint. 

Redapt is dedicated to guiding you throughout this journey—from start to finish—by partnering with your teams to design, automate, and prepare your AI inference strategy for long-term business success. To discuss how we can assist you in developing your AI inference plan, connect with one of our experts today.