AI Infrastructure Services

Cloud AI is convenient until you see the bill. Or until your provider can’t allocate GPUs during peak demand. Or until you realize your entire AI capability depends on a vendor who could change pricing, deprecate models, or experience outages at any moment.

We build AI infrastructure that you control. Kubernetes clusters with NVIDIA GPU virtualization, intelligent queue systems, and deployment automation that works across cloud providers and on-premise data centers. The same infrastructure we run in production, designed for your requirements.

The Problem We Solve

The economics of AI compute are brutal at scale:

• Cloud GPU costs compound fast. A few thousand inference requests per day might cost $500/month. A few hundred thousand costs $50,000. At some point, owning hardware makes financial sense.

• GPU availability isn’t guaranteed. Try provisioning A100s or H100s during an AI boom. Cloud providers ration capacity, and your workloads wait.

• Data gravity matters. If your data lives on-premise for compliance or latency reasons, shipping it to cloud AI services adds complexity, cost, and risk.

• Single-provider dependency is fragile. When your cloud provider has an outage—and they all do—your AI capabilities go dark.

The alternative isn’t easy. NVIDIA GPU integration with Kubernetes is notoriously complex. Managing GPU VRAM across multiple workloads requires careful orchestration. Building queue systems that handle SLA prioritization takes real engineering. Most organizations don’t have the expertise in-house.

We do. We’ve built this infrastructure for production workloads, and we can build it for you.

What We Build

GPU Infrastructure Design & Implementation

We design and deploy GPU clusters optimized for AI workloads—not generic compute infrastructure with GPUs bolted on, but purpose-built systems for inference and training.

Kubernetes Foundation

Our infrastructure runs on Kubernetes, configured from scratch on Ubuntu servers. This isn’t managed Kubernetes with its constraints and costs—it’s bare-metal clusters that you control, with the flexibility to run anywhere.

The architecture supports:

• AWS, Azure, Google Cloud, and on-premise deployments
• Hybrid configurations spanning cloud and data center
• Automated scaling based on workload demand
• Infrastructure as Code deployment for repeatability

NVIDIA GPU Operator Integration

The NVIDIA GPU Operator is essential for efficient GPU utilization in Kubernetes, but getting it right requires navigating driver version compatibility, resource allocation, and pod scheduling. We’ve done this with consumer-grade GPUs (RTX 4090, RTX 3090) and enterprise hardware.

Key capabilities:

• Virtual GPU configuration allowing multiple pods to share physical GPUs
• VRAM allocation management across concurrent workloads
• Driver version coordination across cluster nodes
• Graceful migration paths when upgrading GPU operators

Virtual GPU Economics

Virtual GPUs change the cost calculus. A single RTX 4090 running one workload at a time might generate an image in 10 seconds. Two virtual GPUs on that same card might take 17 seconds each—but you’re generating nearly twice the throughput overall.

For a single user, dedicated GPU access is faster. For a system serving many users, virtualization dramatically improves cost efficiency. We help you find the right tradeoff for your workload patterns.

Queue-Based Workload Management

GPU workloads aren’t like CPU workloads. You can’t just throw more pods at the problem—VRAM is finite, model loading is expensive, and contention kills performance. Intelligent queue management is essential.

RabbitMQ Queue Architecture

We implement queue systems using RabbitMQ with sophisticated organization:

• Per-model queues — Each AI model and version gets its own queue, enabling efficient resource allocation
• SLA-tiered prioritization — Premium requests process before standard requests without starving lower tiers
• Dynamic work allocation — Workers pull from queues based on real-time load and resource availability

This architecture means a surge in standard-tier requests doesn’t block premium users, and idle capacity automatically serves lower-priority work.

Model Loading Optimization

Loading AI models into GPU VRAM takes significant time. Our queue system keeps frequently-used models loaded and directs work to GPUs that already have the required model in memory. This reduces per-request latency and increases overall throughput.

The work coordinator balances competing concerns:

• Keep hot models loaded to minimize load time
• Unload cold models to free VRAM for other workloads
• Match incoming work to GPUs with models already loaded
• Handle graceful degradation when demand exceeds capacity

Overload Protection

When demand exceeds capacity, the system remains stable. Requests queue rather than fail. Priority tiers ensure critical work completes. No single user or workload can monopolize resources. The system degrades gracefully rather than collapsing.

Multi-Provider AI Integration

Infrastructure isn’t just GPUs—it’s also managing relationships with cloud AI providers. We implement multi-provider architectures that optimize cost, availability, and capability.

Provider Routing

Not every request needs the most expensive model. Our routing layer directs traffic based on:

• Task complexity — Simple queries go to fast, cheap models; complex reasoning goes to capable, expensive ones
• Cost optimization — Automatic selection of the most cost-effective provider for each request type
• Availability — Failover to alternate providers during outages
• Capability matching — Route to providers with specific capabilities (vision, code, long context)

Intelligent Caching

Many AI requests are similar or identical. The K-token economy system caches responses and avoids redundant API calls, achieving 44-59% cost reductions in production deployments.

Caching strategies include:

• Exact match caching for repeated queries
• Semantic similarity matching for near-duplicates
• Prompt template optimization to increase cache hit rates
• Cache invalidation policies based on content freshness requirements

Provider Abstraction

Applications talk to a unified API. Provider details—authentication, rate limits, response formats—are handled by the infrastructure layer. Adding a new provider or replacing an existing one requires no application changes.

On-Premise & Hybrid Deployment

For organizations that need AI infrastructure on their own hardware—whether for cost, compliance, or control—we deploy production-ready systems in your data center.

Infrastructure as Code

Everything deploys through automated scripts. No manual configuration, no snowflake servers, no “we’ll just SSH in and fix it” operations. The same automation works for:

• Single-server development environments
• Multi-node production clusters
• Cloud provider deployments (AWS, Azure, GCP)
• On-premise data center installations
• Hybrid configurations spanning multiple locations

Network Integration

Cloud deployments integrate with provider-specific networking:

• AWS WAF, VPC configuration, security groups
• Azure virtual networks and NSGs
• GCP VPC and firewall rules

On-premise deployments integrate with existing network infrastructure, firewalls, and security policies.

Operational Handoff

We don’t just deploy and disappear. Infrastructure includes:

• Monitoring and alerting configuration
• Runbook documentation for common operations
• Capacity planning guidance
• Upgrade and maintenance procedures

Your team—or ours, if you engage us for ongoing support—can operate the infrastructure without reverse-engineering how it works.

Why Build Infrastructure With Us

Production-proven architecture. This isn’t theoretical design—it’s the same infrastructure running our production workloads today. 100K+ monthly operations, multi-tenant isolation, SLA-based prioritization, all in production.

Full-stack understanding. We’ve built from GPU metal to conversational UI. Infrastructure decisions are informed by what actually matters for AI application performance, not generic Kubernetes patterns.

Cloud-agnostic by design. No lock-in to any cloud provider. Run on AWS today, migrate to on-premise tomorrow, hybrid the day after. The infrastructure is portable.

Realistic economics. We’ll tell you honestly whether on-premise makes sense for your scale and workload. Sometimes cloud is the right answer. We’ll help you model the tradeoffs.

Typical Engagement

Assessment (1-2 weeks)

• Analyze current AI workloads and costs
• Document compliance and data locality requirements
• Model on-premise vs. cloud economics for your specific situation
• Recommend architecture approach

Design (2-3 weeks)

• Architecture specification for GPU cluster and queue system
• Network and security design
• Capacity planning and scaling strategy
• Infrastructure as Code development

Deployment (2-4 weeks)

• Cluster provisioning and configuration
• GPU operator and driver setup
• Queue system implementation
• Provider integration and routing configuration
• Monitoring and alerting deployment

Optimization (ongoing or as-needed)

• Workload analysis and tuning
• Virtual GPU allocation refinement
• Queue priority adjustments
• Capacity scaling as demand grows

Total timeline for a production deployment: typically 6-10 weeks from assessment to live traffic.

Cost Comparison Framework

The decision to build AI infrastructure depends on your specific numbers. Here’s the framework we use:

Cloud GPU costs at scale:

• Calculate current monthly spend on cloud AI compute
• Project growth based on usage trends
• Include hidden costs (data transfer, storage, API overhead)

On-premise investment:

• Hardware costs (GPUs, servers, networking)
• Data center costs (power, cooling, space) or colo fees
• Setup and integration (our engagement)
• Ongoing operations (internal team or managed support)

Break-even analysis:

• At what monthly cloud spend does on-premise pay off in 12 months? 24 months?
• What’s the risk-adjusted value of control and availability?
• How does hybrid compare to all-cloud or all-on-premise?

For organizations spending $10K+/month on cloud GPU compute, on-premise infrastructure often pays for itself within a year. We’ll help you run the numbers for your situation.

Ready to Take Control?

If you’re tired of cloud GPU costs, availability constraints, or vendor dependency, let’s talk about what your own AI infrastructure could look like. We’ll give you an honest assessment of whether it makes sense for your scale and requirements.