Build the infrastructure behind your AI product.
Scope inference, retrieval and application hosting together. Benchmark model behavior and performance, confirm available compute and make data-use requirements explicit.
The problems we're built to solve.
GPU availability is unpredictable
public cloud availability for H100s is hit-or-miss. Lead times kill experimentation.
Hyperscaler bills are unpredictable
Egress, snapshot storage, and reserved instance math wreck founder runway.
Model hosting requires expertise
vLLM, TensorRT-LLM, sharding, quantization — most teams don't want to own this.
RAG data is sensitive
Customer documents flowing through public-cloud regions creates compliance friction with B2B customers.
Latency from inference to app
Inference in one region, app in another, customer in a third — the math doesn't work for real-time.
Multi-environment costs scale linearly
Dev, staging, prod — each a full duplicate on a hyperscaler. Bill grows faster than the team does.
What to agree upfront.
What to scope for your environment.
Define the operational foundation with your team: access, monitoring, recovery, data boundaries and any required agreements.
Bare-metal GPUs
Size accelerator memory, throughput and tenancy for the workload. Confirm hardware availability and commercial terms before planning a deployment.
managed AI integration
Evaluate model routing, prompt caching, request limits and cost reporting with representative requests. Validate savings rather than assuming a percentage.
Inference platform
vLLM, TensorRT-LLM, llama.cpp pre-tuned and supported. Bring your own weights or use a managed open-weights deployment.
Vector DB + RAG
Managed pgvector, Qdrant, or Weaviate on dedicated tenancy. Ingest, embed, and serve from one platform.
Observability built in
Token throughput, latency, cost per request, eval results — all in one dashboard. No DIY observability stack.
Customer-data protection
Agree model-provider data use, retention, access, encryption and audit requirements before processing customer information.
Starting points, not surprises.
Discuss scope and dependencies with our team. Confirm current pricing and inclusions in a written proposal.
- Dedicated tenancy
- Managed AI API + prompt caching
- pgvector or Qdrant
- GPU access via marketplace
- Dev / staging / prod
- 4× L40S dedicated inference
- BYOK encryption
- Multi-region failover
- Agreed operational coverage
- Customer-data DPA
- 8× H100 dedicated nodes
- NVLink fabric
- Customer success engineer
- Security requirements review
- Dedicated MLOps engineer
Common questions, answered.
Yes. Llama, Mistral, fine-tuned variants, custom architectures — all supported. We tune the inference runtime for your weights.
No. The our managed AI API is yours; we handle the infrastructure, prompt caching, and billing pass-through. You can leave with your code and keys anytime.
Availability depends on the hardware and deployment requirements. Request a current capacity check and confirmed delivery plan before committing to a launch date.
Data use depends on the chosen model provider, configuration and agreement. Confirm training restrictions, retention, subprocessors and required terms before sending customer data.
Plan your first inference workload with production controls from the start.
Discuss inference, retrieval and application requirements with our team, then agree the architecture review and proposal scope.
Your models and data, on dedicated infrastructure.
Run open or fine-tuned models on dedicated GPUs near your data — not shared public SaaS by default.
Ground answers in your own documentation and knowledge, with access controls and audit trails.
Send each request to the right approved model endpoint for the right cost, latency, and policy.
Agentic workflows that take action with guardrails and clear human accountability.
AI workload planning
Illustrative discovery map; model, hardware and isolation choices follow workload evaluation.
- -> Map requests through authorization to model inference.
- -> Separate approved ingestion from public retrieval.
- -> Define which usage and diagnostic data may be retained.