AI Infrastructure & Deployment
GPU infrastructure, model serving, auto-scaling and cost optimization for production AI systems.
What is AI Infrastructure & Deployment?
Getting an AI model to work in a notebook is easy. Serving it reliably at scale — with auto-scaling, failover, cost controls and monitoring — is where most teams struggle. We build the infrastructure that makes AI work in production.
We design and deploy GPU infrastructure on AWS, GCP or bare metal. We set up model serving (vLLM, TGI, Triton), load balancing, auto-scaling based on queue depth and latency, and cost optimization that can cut inference bills by 60%.
Whether you’re serving open-source models or wrapping commercial APIs with intelligent routing — we’ve built and operated these systems at scale.
Why build with us
Multi-Cloud Expertise
We deploy on AWS, GCP or bare metal. Choose the platform that fits your data privacy and cost requirements.
Cost Optimization
Quantization, batching, caching and spot instances. We reduce inference costs by 60-80% while maintaining quality.
Production Reliability
Auto-scaling, failover, health checks and monitoring. 99.9% uptime SLA for critical AI workloads.
Scale Ready
Architecture designed to scale from 100 to 10M requests/day. No rewrites needed when you grow.
What we build with AI Infrastructure & Deployment
GPU Infrastructure
NVIDIA A100/H100 deployment on AWS, GCP or bare metal with auto-scaling and cost controls.
Model Serving
vLLM, TGI or Triton Inference Server for high-throughput, low-latency model serving.
Auto-scaling
Queue-depth-based scaling with pre-warmed GPU instances and scale-to-zero for cost savings.
Monitoring & Alerts
Real-time GPU utilization, inference latency, cost tracking and anomaly detection.
How we deliver
Architecture
Week 1Design the serving infrastructure based on your model, traffic and latency requirements.
Deploy
Week 2-3Set up GPU infrastructure, model serving, load balancing and health checks.
Optimize
Week 3-4Quantization, batching, caching and auto-scaling configuration.
Monitor
Week 5Dashboards, alerts, runbooks and 30-day operational support.
How you can work with us
Fixed-Price Project
Defined scope, fixed timeline, guaranteed deliverables. Best for MVPs and well-scoped features.
- Full scope defined upfront
- Milestone-based payments
- 8-16 week delivery
- 30-day warranty
Dedicated Developer
A senior developer assigned to your team full-time. Minimum 1 month engagement.
- 160 hours/month
- Daily standups
- Weekly demos
- Flexible scaling
Team Augmentation
A full team of developers, designers and architects embedded in your organization.
- Cross-functional team
- Quarterly engagement
- Dedicated PM
- Architecture oversight
Our AI Infrastructure & Deployment technology stack
Serving
Cloud
Orchestration
Monitoring
Optimization
Frequently asked questions
It depends on your volume, latency requirements and data privacy needs. Below ~1M tokens/day, commercial APIs (OpenAI, Anthropic) are usually cheaper. Above that, self-hosting on GPU instances can save 60-80%. We’ll model the economics for your specific case.
vLLM for high-throughput LLM serving, TGI (Text Generation Inference) for HuggingFace models, Triton Inference Server for multi-model setups, and custom FastAPI wrappers for simpler deployments.
We use queue-depth-based scaling (not just CPU/memory), with pre-warmed GPU instances for cold-start reduction. Scale-to-zero during off-hours for cost savings, with warm-up hooks for traffic spikes.
Absolutely. We implement model quantization (GPTQ, AWQ), request batching, semantic caching, spot instance strategies and intelligent model routing to minimize GPU costs while maintaining quality.