Run AI where your sensitive data lives.
Custom, open weight, fine tuned and proprietary LLMs deployed in private cloud, VPC, on premises, dedicated GPU or hybrid. We build the serving, GPU, routing, security and observability layer behind production AI.
Sensitive data stays in your boundary
Swap models, keep the app
Costs you can forecast
Private. Portable. Production ready.
Right now your most sensitive information is leaving your company to reach a public AI.
Every time a team member pastes a contract, a customer record, or an internal memo into ChatGPT or a similar public model, that data travels to servers you do not own, operated by a company you cannot audit. You are trusting a third party with information you would never email to a stranger. That is the real problem this service solves.
Your data leaves the building
Patient records, contracts, trade secrets, financial models, legal privilege — every prompt and every attached file lands on a third party's servers. You cannot fully control where it is stored, who sees it, or whether it is used to train future models.
They set the price
Token pricing rises whenever the provider decides. As your usage grows, so does a bill you cannot forecast or negotiate down.
They control the model
The provider can change, degrade, or retire the model overnight. Your workflow breaks and you have no say in when or how.
What this service is for
CTOLabX helps you stop sending sensitive information to public models. We figure out which of your workloads genuinely need a frontier API and which should run on infrastructure you control — then we build the layer that lets both coexist. Your application talks to one endpoint. The model behind it can be public or private, and your data stays where you decide.
Keep sensitive data in
Patient records, contracts and trade secrets stay on infrastructure you control.
Pick the right model
Use frontier APIs where quality matters, private models where privacy matters.
Control your costs
Dedicated GPUs can be cheaper than per-token pricing once volume is high enough.
Swap models freely
Change provider or model without rewriting your application.
Your app should not care which model runs behind it. You change the model — the app stays the same.
Bring the model that fits the workload.
Commercial APIs, open weight models, fine tuned variants and your own models, behind one consistent interface.
Deploy Nemotron, Llama, Qwen, DeepSeek, GLM, Kimi, Mistral, or your own model, inside infrastructure you control.
Examples only, not partnerships
NVIDIA positions Nemotron as a family of open models for enterprise and agentic workloads with deployment through frameworks such as vLLM and SGLang, and Meta makes Llama available for direct access and hosting. Nothing here implies partnerships, endorsements, certifications or guaranteed compatibility with every model version. The platform is provider neutral and workload driven.
Why run your own models?
01
Data control
Sensitive data stays inside boundaries you own.
02
Model control
You decide which versions run and when they change.
03
Domain specialization
Fine tune on your terminology, workflows and internal knowledge.
04
Predictable economics
At high utilization, dedicated inference changes the math. We model it before recommending it.
05
Deployment flexibility
Private VPC, dedicated cloud, on premises, edge or hybrid.
06
No provider lock in
Swap models without rebuilding the product.
07
Operational visibility
Latency, throughput, GPU utilization and SLOs become observable.
08
Security and isolation
Auth, tenancy, quotas and policy enforced at the inference layer.
Applications integrate once. Models and infrastructure evolve independently.
Before
Changing models is an application migration.
Product → OpenAI API
Product → Anthropic API
Product → Another model
After
Changing models is an infrastructure decision.
Product
↓
CTOLabX AI Platform Layer
↓
OpenAI / Anthropic / Nemotron / Llama / Qwen / DeepSeek / GLM / Kimi / Custom
OpenAI today. Nemotron, Llama, Qwen, DeepSeek, or your own model tomorrow. Your application does not change.
The platform layer, engineered end to end.
01
Inference Platform Architecture
Gateways, backends, tenancy, topology, failure domains.
02
Private Model Serving
Open weight, fine tuned and proprietary models on vLLM, SGLang or equivalent.
03
GPU Infrastructure
GPU selection, memory, clustering, scheduling, capacity, scaling.
04
Routing & Provider Abstraction
Unified endpoints, policy based selection, fallback, model migration.
05
Performance Engineering
Batching, KV cache, quantization, concurrency, latency, throughput.
06
Observability & Reliability
Metrics, traces, SLOs, rate limits, retries, circuit breakers, incidents.
07
Security & Isolation
Auth, tenant isolation, secrets, quotas, network boundaries, data flow controls.
08
Cost & Capacity Economics
APIs vs hosted GPU vs dedicated GPU vs hybrid, on your real workload.
09
Model Lifecycle
Benchmarking, rollout, canaries, versioning, rollback, deprecation.
The decision is made workload by workload.
Commercial APIs
Keep it here
- Frontier quality is critical
- Traffic is variable
- Simplicity beats ownership
Private AI
Bring it in house
- Sensitive data needs control
- Locality or residency matters
- Custom models required
- Utilization justifies dedicated GPU
Hybrid
Split the traffic
- Workloads differ in privacy needs
- Frontier models earn their cost on some tasks
- High volume work runs private
We are not an open source LLM shop. We decide what belongs on frontier APIs, what belongs on private models, and build the infrastructure to run both safely.
Any organization handling valuable, confidential or regulated data.
Financial Services
Healthcare
Legal
Insurance
Government
Defense & Aerospace
Pharma & Life Sciences
Enterprise SaaS
Manufacturing
01
Assess
- Workloads, providers and AI spend
- Concurrency, latency, reliability
- Sensitive data boundaries
- GPU feasibility
02
Architect
- Gateway and routing design
- Private and public model split
- Serving stack and GPU capacity
- Security, SLOs and cost model
03
Build & Operate
- Gateway, serving and fallback
- Auth, quotas, telemetry
- Scaling and deployment workflows
- Benchmarking, rollout, rollback
Published prices for platform work.
Start with the assessment.
AI Platform Architecture Assessment
From $9,500 one-time
5 to 7 business days
- Current state and workload analysis
- Target architecture and routing design
- API vs GPU economics
- Phased implementation roadmap
Private AI Readiness Assessment
From $12,500 one-time
Evaluating private, VPC, dedicated GPU, on prem or hybrid
- Feasibility and risk view
- Data boundary mapping
- Deployment options compared
Private LLM Architecture
From $15,000 one-time
Custom and open weight models
- Serving stack and GPU capacity
- Networking and security boundaries
- Observability and operating model
AI Platform Build
From $30,000 phased
Production implementation
- Gateway, routing and model serving
- GPU infrastructure and auth
- Observability and deployment workflows
AI Platform Optimization
From $7,500 per month
Ongoing
- Capacity planning and cost reduction
- Inference optimization and migrations
- Benchmarking and incident reviews
Fractional Head of AI Platform
From $12,500 per month
Ongoing
- Senior ownership of production AI infrastructure
- Roadmap and vendor decisions
- Team and delivery direction
AI applications are easy to demo. Production AI infrastructure is harder.
Production AI needs disciplines that rarely sit inside one consultant. We operate at that layer, and design systems that stay reliable as traffic, models, providers, costs and security requirements change.
We do not sell you a model. We build the infrastructure that lets you choose the right model without rebuilding your product.
Your AI models will change. Your application should not have to.
Get an independent read on your model architecture, inference economics, security boundaries and GPU requirements before your next AI infrastructure decision.
Book an AI Platform Assessment