What We Plan
Running AI in production is a different problem from running it in a notebook. Latency requirements, concurrent users, cost constraints, reliability targets, and compliance obligations all shape theinfrastructure decisions. We design AI infrastructure that handles real-world demands from day one.
GPU Cluster Architecture
Compute sizing, GPU selection (A100, H100, L40S, consumer-grade options), multi-GPU configurations, NVLink topology, and cluster networking. We right-size for your workload so you do not overspend on hardware you do not need.
Model Serving Pipeline
Inference engine selection (vLLM, TGI, Triton, Ollama), batching strategies, KV-cache optimization, model routing, and A/B testing infrastructure. Production serving that handles thousands of concurrent requests reliably.
Data Pipeline Design
RAG infrastructure, vector database selection and tuning, embedding pipelines, document processing at scale, and real-time data ingestion. The data layer that powers accurate, grounded AI responses.
Capacity Planning
Cost modeling across usage scenarios, scaling policies, spot/reserved instance strategies, and growth projections. We forecast your infrastructure spend at 1x, 5x, and 20x current usage so there are no surprises.
Planning Process
Profile
Characterize AI workloads
Architect
Design the full stack
Cost Model
Cloud vs on-prem vs hybrid
Roadmap
Phased implementation plan
Profile
Characterize AI workloads
Architect
Design the full stack
Cost Model
Cloud vs on-prem vs hybrid
Roadmap
Phased implementation plan
Infrastructure Planning Services
Planning Process
Infrastructure planning typically runs three to four weeks and produces a complete technical design your team can execute immediately.
- Workload characterization. We profile your AI workloads: model sizes, token throughput, latency requirements, concurrency patterns, and data volumes. This is the foundation for every sizing decision.
- Architecture design. We design the complete infrastructure stack: compute, storage, networking, model serving, data pipelines, monitoring, and security. Every component is specified with vendor, version, and configuration.
- Cost modeling. We build a detailed cost model covering hardware/cloud spend, operational costs, and scaling economics. We compare deployment options (cloud vs on-prem vs hybrid) with honest total cost of ownership analysis.
- Implementation roadmap. A phased plan for building the infrastructure with clear milestones, resource requirements, and risk mitigations. Designed so you can start serving production traffic within weeks, not months.
Technology Decisions We Help You Make
AI infrastructure involves dozens of technology choices that interact in non-obvious ways. These are the decisions where our hands-on experience prevents expensive mistakes.
Cloud vs on-premises vs hybrid. The right answer depends on your data sensitivity, usage patterns, and financial model. Cloud is faster to start but can be more expensive at scale. On-prem requires capital but delivers lower marginal costs. We model both and recommend based on your numbers.
GPU selection and sizing. An H100 is not always the right choice. For many workloads, L40S or even consumer GPUs deliver adequate performance at a fraction of the cost. We benchmark your actual models on candidate hardware before recommending a purchase.
Model hosting strategy. Self-hosted open-weight models, managed API endpoints, or a mix of both. We evaluate the tradeoffs for each of your use cases: cost per token, latency, data privacy, and model capability.
Vector database selection. Pinecone, Weaviate, Qdrant, pgvector, Milvus, and others each have different performance characteristics, operational complexity, and cost profiles. We match the database to your scale, query patterns, and team capabilities.
Orchestration and observability. Kubernetes vs simpler deployment models. Prometheus, Grafana, Datadog, or custom monitoring. We choose tools that match your team's operational maturity, not tools that require hiring a platform team to manage.
Who This Is For
AI infrastructure planning delivers the most value in these situations.
- Scaling from prototype to production. Your AI proof of concept works, but the architecture that serves one user will not serve a thousand. You need a production design before scaling.
- Evaluating private vs cloud deployment. You are considering bringing AI workloads in-house but need honest cost and complexity analysis before committing to hardware.
- Optimizing existing AI infrastructure. Your AI is in production but costs are climbing, latency is increasing, or reliability is below target. We audit and redesign.
- Planning a new AI initiative. You know what you want to build with AI and need the infrastructure to support it. We design the platform before you write the first line of application code.
Get Started
Infrastructure decisions made early compound over the life of your AI systems. Getting them right from the start saves months of rework and significant cost.
Contact us at ben@oakenai.tech to discuss your AI infrastructure needs. Describe your current setup, your scale targets, and your constraints. We will tell you whether planning work is the right investment for your stage.