GPU infrastructure + private cloud, on tap.
H100 and L40S inference and training, plus full-stack hosting for your app. Anthropic Claude with prompt caching included. Predictable monthly bills.
The problems we're built to solve.
GPU availability is unpredictable
AWS / Azure / GCP 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 customers measure.
What you get on day one.
Every engagement ships with the operational foundation — encryption, audit logging, monitoring, BAA / DPA — already in place.
Bare-metal GPUs
NVIDIA H100 (8×) and L40S (4×) servers available as dedicated nodes. Lease or buy via marketplace.
Anthropic Claude integration
First-class Claude API on Pro and Scale tiers, with prompt caching enabled by default — typical 50-70% token cost reduction.
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
Your customer data never trains a model. Customer-held keys, encrypted-at-rest indexes, audit logs by default.
“We replaced a $14k/month AWS bill with a $2,400/month Ultiblob bill — and we got Claude prompt caching for free. The first six months of runway came back.”
Starting points, not surprises.
Real numbers for typical engagements. The estimator returns yours in 30 seconds.
- Dedicated tenancy
- Claude API + prompt caching
- pgvector or Qdrant
- GPU access via marketplace
- Dev / staging / prod
- 4× L40S dedicated inference
- BYOK encryption
- Multi-region failover
- 24/7 NOC
- Customer-data DPA
- 8× H100 dedicated nodes
- NVLink fabric
- Customer success engineer
- SOC 2 evidence on tap
- Dedicated MLOps engineer
Common questions.
- Can I bring my own model weights?
- Yes. Llama, Mistral, fine-tuned variants, custom architectures — all supported. We tune the inference runtime for your weights.
- Is the Claude integration locked to Ultiblob?
- No. The Anthropic Claude API is yours; we handle the infrastructure, prompt caching, and billing pass-through. You can leave with your code and keys anytime.
- What's the GPU lead time?
- L40S nodes: typically 2 business days. H100 nodes: 2-4 weeks for new builds (we keep buffer capacity for short-term overflow).
- Will my customer data train any model?
- No. Customer data is never used for training. Our DPA is explicit; subprocessor list (Anthropic included) is published on /trust.
Built for ai startups and ml teams. Live this week.
Run the estimator for a real number, or book a 15-minute scoping call with a specialist.