AI & LLM Infrastructure
GPU cost control, model serving, vector databases, and RAG pipelines — with guardrails that keep customer data out of third-party models. Ship AI without shipping your secrets.
$10k/month in GPU bills and climbing. Instances running 24/7 for batch jobs. No visibility into what's actually being used.
Customer data going to OpenAI/Anthropic APIs. Legal says no, but the product team needs AI features yesterday.
Model responses taking seconds when users expect milliseconds. Scaling is manual and error-prone.
Data scientists building infrastructure instead of models. Reinventing vector search, model serving, prompt management.
Right-size your GPU fleet. Spot instances, autoscaling, and job scheduling that cuts costs without slowing down training.
Deploy models that scale. Low-latency inference, autoscaling, A/B testing, and canary deployments.
Production vector search infrastructure. Choose the right database, scale it properly, keep it fast.
Retrieval-augmented generation done right. Document ingestion, chunking, embedding, and retrieval optimization.
Keep data in-house with self-hosted models. Llama, Mistral, and other open models on your infrastructure.
Keep customer data out of third-party models. PII detection, data masking, and audit trails.
-60%
Optimized a ML startup's training infrastructure with spot instances and job scheduling. $25k/month → $10k/month.
50ms
Deployed vLLM with autoscaling for a chatbot product. Consistent sub-50ms responses even at peak load.
0
Implemented self-hosted Llama with PII guardrails. Full AI capabilities without data leaving the VPC.
APIs (OpenAI, Anthropic) are easier and often better quality. Self-host when: data privacy is critical, you need to fine-tune, or API costs at scale exceed hosting costs. We help you make the right call.
Pinecone for managed simplicity. Weaviate or Qdrant for self-hosted control. pgvector if you're already on PostgreSQL and scale is modest. We'll recommend based on your scale and operational preferences.
Options: 1) Self-host models entirely. 2) Use PII detection to redact before API calls. 3) Use provider enterprise agreements with data protection. 4) Hybrid — route sensitive queries to self-hosted, others to APIs.
We focus on infrastructure, not model development. We'll set up your training pipelines, serving infrastructure, and data systems — your data scientists build the models.
We'll assess your AI infrastructure needs, recommend the right stack, and help you build infrastructure that scales.
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