Scalable AI Infrastructure
Deploy GPU-optimized AI workloads on AWS, Azure, or GCP. Handle latency-sensitive inference at scale with proper caching and orchestration for sub-300ms response times.
AI at scale is an infrastructure problem
Building an AI prototype is easy. Running it reliably at scale is not. GPU instances are expensive and have unique operational challenges. Model inference has different scaling characteristics than typical web apps. Without proper architecture, costs explode and performance degrades.
Common mistakes: over-provisioned GPUs sitting idle, cold starts causing timeouts, no caching leading to unnecessary API calls, lack of observability making debugging impossible, and architectural decisions that lock you into expensive managed services.
We design AI infrastructure that scales with your usage, optimizes costs, and maintains consistent low latency. Whether you are serving 100 or 100,000 requests per minute, the architecture handles it gracefully.
What We Build
End-to-end AI infrastructure. From GPU selection to production monitoring.
GPU-Optimized Deployment
Run LLMs and ML models on the right GPU instances. We optimize for cost, latency, and throughput based on your traffic patterns.
- Instance selection (A10G, A100, H100)
- Model quantization and optimization
- Batch inference for cost efficiency
- Auto-scaling based on demand
Latency Optimization
Achieve sub-300ms response times for real-time AI applications. Caching, model serving optimizations, and edge deployment where needed.
- Response caching with semantic deduplication
- Streaming responses for perceived speed
- Regional deployment strategies
- Cold start mitigation
Model Serving & Orchestration
Serve multiple models efficiently. Load balancing, A/B testing, and gradual rollouts for model updates.
- Multi-model serving (vLLM, TGI, Triton)
- Model versioning and canary deployments
- Request routing and prioritization
- Health monitoring and auto-recovery
Observability & Monitoring
Full visibility into your AI infrastructure. Track latency, throughput, costs, and model performance in production.
- Real-time latency and error dashboards
- Cost tracking per model and customer
- Model drift detection
- Alerting and incident response
Multi-Cloud Expertise
We deploy on the cloud that makes sense for your business. Each provider has strengths; we help you leverage them.
AWS
Most mature ML infrastructure. Best for complex, multi-service architectures.
Key Services:
Azure
Strong Microsoft integration. Azure OpenAI Service for enterprise GPT-4 access.
Key Services:
Google Cloud
Cutting-edge ML tooling. TPU access for training large models.
Key Services:
Architecture Patterns
There is no one-size-fits-all for AI infrastructure. We select the right pattern based on your requirements.
Serverless Inference
Pay only for what you use. Ideal for variable or unpredictable traffic. Higher latency on cold starts.
Dedicated GPU Instances
Consistent low latency. Reserved capacity for predictable performance. Higher base cost.
Kubernetes-Based Serving
Maximum flexibility and control. Complex to operate but enables advanced patterns.
Managed Model Hosting
Least operational overhead. Provider handles scaling and infrastructure.
Why we build orchestration in Rust
Python is the language of AI research. For production inference orchestration, we often reach for Rust. The performance difference is significant when you are processing thousands of requests per second.
Rust-based orchestration layers can reduce latency by 50% and cut infrastructure costs by 30-40% compared to Python equivalents. For high-throughput systems, this translates to real money.
- Near-zero memory overhead per request
- Compile-time safety prevents runtime crashes
- Predictable latency under load
// High-performance request routing
async fn route_inference(
req: InferenceRequest,
pool: &ModelPool,
) -> Result<Response, Error> {
let model = pool
.select_optimal(req.complexity)
.await?;
let result = model
.infer(req.prompt)
.timeout(Duration::ms(300))
.await?;
Ok(Response::new(result))
}GPU costs can spiral quickly. We keep them under control.
A single H100 instance costs over $30/hour. Running that 24/7 for a workload that only needs it during business hours wastes thousands monthly. Without optimization, AI infrastructure costs grow faster than your usage.
We implement cost optimization strategies at every layer:
- Right-sizing GPU instances for your workload
- Spot/preemptible instances for batch workloads
- Model quantization to use smaller/cheaper GPUs
- Intelligent caching to reduce inference calls
- Auto-scaling to match actual demand
Typical Savings
Infrastructure as Code, Always
Every piece of infrastructure we build is version-controlled and reproducible. No clicking through consoles. No mystery configurations.
Terraform or Pulumi for infrastructure. Kubernetes manifests or Helm charts for applications. CI/CD pipelines for deployments. Everything documented and handed over to your team.
Technologies We Use
Ready to scale your AI infrastructure?
Let's design an architecture that handles your growth, controls costs, and maintains the performance your users expect.
Get in touch