Private LLM Deployments
Host and operate private LLMs in your environment for data control, security, and compliance.
A specialized capability within DEQX AI Transformation — design and operate secure private large language model infrastructure so enterprises run AI inside controlled environments with data privacy, governance, identity, compliance, and scalable production serving.
Private LLM Infrastructure, Unified.
From VPC and on-prem hosting to identity, governance, and production inference — one architecture for enterprise-grade private language models.
Secure Private Model Hosting
Deploy foundation and fine-tuned models in your VPC, private cloud, or on-premise — with network isolation, secrets management, and clear data egress boundaries.
Enterprise Security & Identity
Wire inference behind SSO, RBAC, API keys, and service identities so every call is authenticated, authorized, and attributable.
Governance, Compliance & Production Scale
Operate private LLMs with audit logging, retention policies, evaluation gates, capacity planning, and observability — so pilots become durable production platforms.
What We Deliver
Structured private LLM capabilities that map from secure hosting to governed, production-ready inference — designed for regulated enterprises and high-stakes AI workloads.
Private Model Hosting
- check_circleVPC, dedicated cloud, on-prem, and air-gapped topologies
- check_circleOpen-weight and licensed models under your control
- check_circleIsolated networking with explicit egress policies
Data Privacy & Residency
- check_circlePrompts, embeddings, and logs stay in approved regions
- check_circlePII controls on telemetry and training spillover
- check_circleClear boundaries between model weights and enterprise data
Enterprise Security & Identity
- check_circleSSO / OIDC integration and service-to-service auth
- check_circleRole- and attribute-based access to models and tools
- check_circleSecrets, certificates, and key rotation aligned to your IAM
Governance & Compliance
- check_circlePrompt/response audit trails with retention policies
- check_circleChange control for models, prompts, and configs
- check_circleEvidence packs for security and compliance reviews
Inference Optimization
- check_circleServing stacks tuned for latency, throughput, and cost
- check_circleQuantization, batching, caching, and GPU utilization
- check_circleHardware-aware profiles for GPU fleets and hybrid CPU
Scalable Production Deployment
- check_circleAutoscaling, canary, and blue-green promotion paths
- check_circleFailover, rate limits, and multi-model routing
- check_circleRuntime monitoring, cost telemetry, and rollback
RAG & Knowledge Integration
- check_circlePrivate vector stores with ACL-aware retrieval
- check_circleGrounded generation with citation-ready sources
- check_circleConnectors to knowledge bases, CRM, and data lakes
MLOps & Platform Operations
- check_circleModel registry, environment promotion, and lineage
- check_circleEval gates before production traffic
- check_circleRunbooks for capacity, incidents, and model drift
Models that stay inside your perimeter.
Generic public APIs move proprietary prompts and context outside your control. We design private LLM platforms where model weights, embeddings, and inference traffic live where your data already does — with residency and egress rules defined up front.
- cloud
Deployment Flexibility
VPC-hosted, on-premise, hybrid, or air-gapped topologies chosen against latency, residency, and operational maturity — not vendor defaults.
- lan
Network Isolation
Private subnets, service meshes, and allow-listed egress so inference never becomes an uncontrolled data path.
- key
Secrets & Keys
Integrate with your KMS and secrets managers so credentials, tokens, and TLS material follow enterprise standards.
Every inference call is authenticated and attributable.
Enterprise private LLMs are identity systems as much as model systems. We wire SSO, RBAC, and service identities so copilots, agents, and APIs only see models and knowledge they are entitled to use.
- badge
Workforce & App Identity
Map users and applications to model endpoints with least privilege — including scoped keys for agents and backend services.
- folder_shared
Knowledge ACLs
Pair private serving with retrieval that respects document and system access controls, not flat shared indexes.
- history
Auditability
Capture who called which model, with what policy context — ready for security operations and compliance evidence.
From sandbox chatbots to governed production platforms.
Private does not mean unfinished. We harden inference for latency SLOs, capacity, failover, guardrails, and staged promotion — the same discipline used in DEQX AI Transformation programs and Avyaa product architectures.
- speed
Serving Profiles
Tune batching, caching, and quantization to hit p95 latency and cost targets on your GPU or hybrid fleets.
- fact_check
Promotion Gates
Evaluate quality, safety, and regressions before models reach end users — with canary and rollback paths.
- monitoring
Observability
Unify latency, error, cost, and quality signals so platform, security, and business teams share one operational picture.
Privacy, Governance & Compliance
Private LLM platforms must satisfy security and compliance stakeholders — not only ML teams. We bake controls into the architecture so reviews accelerate instead of blocking go-live.
Data Privacy Controls
Keep prompts, context, and embeddings in approved environments with retention, redaction, and minimized logging.
Policy & Change Control
Version models, system prompts, and guardrail configs with approvals that match your change-management process.
Compliance Evidence
Produce audit trails and architecture documentation that map to common enterprise security and regulatory questionnaires.
Deployment Patterns
Proven patterns for how enterprises place private LLMs relative to data, identity, and application tiers — aligned with DEQX AI Transformation programs.
VPC Private Cloud
Dedicated accounts and private networking in AWS, Azure, or GCP with your IAM and logging.
On-Premise / Colocation
GPU racks in your DC with enterprise identity, backup, and ops runbooks.
Air-Gapped
Disconnected environments with signed artifact promotion and offline update workflows.
Hybrid Serving
Sensitive workloads private; selective escalation to approved external models under policy.
Model, Identity & Infrastructure Stack
Cloud-agnostic private serving across open models, GPU fleets, and enterprise identity — wired into the same systems that power DEQX AI Transformation, Avyaa AI Copilot, and Avyaa AI Company OS.
Secure Serving Pipelines
Isolated inference environments with secrets management, network policies, and reproducible model promotion.
Ready for Copilots & Agents
Expose private models to enterprise copilots and agent workflows — including patterns used in Avyaa AI Copilot and Avyaa AI Company OS — with tool use, RAG, and human-in-the-loop controls.
Works With Your AI & Security Stack
Host and operate private LLMs where your identity, data, and cloud platforms already live.
How We Deliver Private LLM Programs
A disciplined path from trust-boundary design to production operations — designed to avoid insecure pilots and lock in governable, scalable private AI.
Discover & Design
Map data sensitivity, residency, identity, and compliance constraints. Choose VPC, on-prem, hybrid, or air-gapped topology and define success metrics for privacy and performance.
Build the Platform
Stand up private serving, networking, identity integration, logging, and model registry — with RAG and API surfaces ready for enterprise applications.
Harden & Validate
Run security reviews, load and latency tests, evaluation gates, and compliance evidence packs. Tune cost and capacity before broad user exposure.
Operate & Scale
Promote models through staged environments, monitor SLOs and policy health, and expand capacity as copilots, agents, and workloads grow.
Frequently Asked Questions
Secure model hosting (VPC, on-prem, or air-gapped), identity and access controls, data privacy and residency design, audit and retention, inference optimization, RAG/knowledge integration where needed, and production operations — monitoring, scaling, promotion, and rollback.
Ready to deploy private LLMs for your enterprise?
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