Cloud & On-Premise AI Operations

AI Operations Managing Hybrid Cloud and On-Premise Tech Safely

Deploy LLM workloads securely across cloud, on-premise, hybrid, and air-gapped environments.

featured AI CLIENTS

Is your cloud ready for AI workloads?

50%

of cloud compute resources will be devoted to AI workloads by 2029.

90%

of organizations will adopt hybrid cloud through 2027.

75%

of server AI infrastructure spending will be on accelerated servers by 2028.

Cloud & on-premise AI services for
secure, scalable operations

Cloud & On-Premise AI Operations

Controlled AI workload deployment

Deploy AI models and LLM workloads across cloud, on-premise, hybrid, or air-gapped environments with secure, production-ready controls.

Cloud & On-Premise AI Operations

On-premise AI solutions

Run AI workloads within your own infrastructure to support data control, low latency, and compliance needs.

Cloud & On-Premise AI Operations

Secure LLM hosting & deployment

Host LLMs within approved cloud, on-premise, or hybrid environments for copilots, RAG systems, and internal AI applications.

Cloud & On-Premise AI Operations

Self-hosted LLM solutions

Build self-hosted LLM environments with optimized model serving, private inference, and production-ready deployment workflows.

Cloud & On-Premise AI Operations

AI infrastructure services

Design and manage the compute, storage, networking, orchestration, and monitoring layer behind production AI.

Cloud & On-Premise AI Operations

GPU infrastructure management

Optimize GPU environments for better utilization, faster inference, and lower AI infrastructure costs.

Cloud & On-Premise AI Operations

Hybrid AI deployment

Run AI across cloud, on-premise, and hybrid cloud environments based on workload, security, and cost needs.

Cloud & On-Premise AI Operations

MLOps managed services

Manage model deployment, monitoring, versioning, drift detection, retraining, and rollback for production AI systems.

Cloud & On-Premise AI Operations

AI observability & model serving

Monitor model health, latency, throughput, logs, metrics, and inference performance across AI environments.

Cloud & On-Premise AI Operations

Sovereign AI & data residency solutions

Keep AI workloads aligned with data residency, access control, auditability, and compliance requirements.

Cloud & On-Premise AI Operations

Managed AI operations & optimization

Continuously monitor, secure, tune, and optimize AI systems across cloud and on-premise infrastructure.

Choose the right AI deployment model

Get a clear view of your hybrid, on-premise, air-gapped, and GPU needs before scaling private AI deployment.

How we deliver cloud & on-premise AI operations

01

01 Infrastructure readiness

Every deployment begins with a readiness review of cloud, on-premise, hybrid, or air-gapped environments to identify workload, GPU, security, and cost risks.

Deliverables:
Cloud/on-premise readiness review | AI workload requirements | Security and cost risk report

02 Hybrid AI planning

The deployment model is mapped around performance, compliance, data residency, and cost, defining which workloads belong in cloud, on-premise, hybrid, or air-gapped environments.

Deliverables:
Workload placement strategy | Hybrid deployment model | Cost and compliance plan

03 AI Workload environment setup

Secure environments are configured for AI workloads across cloud, on-premise, hybrid, or air-gapped infrastructure, including Kubernetes, GPU resources, access controls, networking, monitoring, and compliance-ready guardrails.

Deliverables:
Configured workload environment | Access control setup | Monitoring and guardrail plan

04 LLM deployment

Private and self-hosted LLMs are deployed with model serving, RAG integration, validation workflows, CI/CD automation, version control, and rollback planning.

Deliverables:
LLM deployment plan | Integration workflow | Release and monitoring setup

05 GPU optimization

GPU environments are tuned to improve utilization, inference speed, latency, throughput, and infrastructure cost efficiency across production AI workloads.

Deliverables:
Compute readiness review | Inference performance tuning | Cost optimization plan

06 MLOps management

After deployment, AI systems are monitored through drift detection, retraining workflows, incident response, security updates, governance reviews, and continuous performance optimization.

Deliverables:
Model monitoring setup | Drift detection workflow | Continuous optimization plan

How we deliver cloud & on-premise AI operations

step 1

Infrastructure readiness

Every deployment begins with a readiness review of cloud, on-premise, hybrid, or air-gapped environments to identify workload, GPU, security, and cost risks.

Deliverables:
Cloud/on-premise readiness review | AI workload requirements | Security and cost risk report

step 2

Hybrid AI planning

The deployment model is mapped around performance, compliance, data residency, and cost, defining which workloads belong in cloud, on-premise, hybrid, or air-gapped environments.

Deliverables:
Workload placement strategy | Hybrid deployment model | Cost and compliance plan

step 3

AI Workload environment setup

Deliverables:
Configured workload environment | Access control setup | Monitoring and guardrail plan

step 4

LLM deployment

Private and self-hosted LLMs are deployed with model serving, RAG integration, validation workflows, CI/CD automation, version control, and rollback planning.

Deliverables:
LLM deployment plan | Integration workflow | Release and monitoring setup

step 5

GPU optimization

GPU environments are tuned to improve utilization, inference speed, latency, throughput, and infrastructure cost efficiency across production AI workloads.

Deliverables:
Compute readiness review | Inference performance tuning | Cost optimization plan

step 6

MLOps management

After deployment, AI systems are monitored through drift detection, retraining workflows, incident response, security updates, governance reviews, and continuous performance optimization.

Deliverables:
Model monitoring setup | Drift detection workflow | Continuous optimization plan

What you can achieve with cloud and on-premise AI operations

Greater control over sensitive AI workloads

Keep data, models, inference, and audit logs within approved environments with clear access and processing controls.

Faster and more reliable AI performance

Reduce latency and improve workload stability through optimized model serving, infrastructure placement, and resource allocation.

Lower AI infrastructure costs

Improve GPU and cloud utilization while reducing unnecessary compute capacity and infrastructure waste.

Faster model releases

Move models into production more efficiently with repeatable deployment, monitoring, versioning, and rollback workflows.

Consistent performance at scale

Scale AI workloads across cloud and private infrastructure without compromising availability, security, or response times.

Build AI operations that remain secure, reliable, and cost-efficient as demand grows.

Plan Your AI Deployment

How we’ve helped businesses succeed with AI

60% reduction in response times through AI-automated email workflows

Hospitality & TravelArtificial Intelligence

View case study

WATCH TESTIMONIAL

Don Sgouridis

CIO, BBJ Latavola

50% faster discovery with an AI-powered knowledge sharing agent

AgricultureArtificial Intelligence

View case study

"I’m going over testing feedback about the chatbot; overall, testers found the product to be fantastic. We’re very happy with this."

Chris Head

Director of IT, CCOF

50% reduction in support workload with AI-powered document retrieval

AgricultureArtificial Intelligence

View case study

"tkxel built an AI assistant that delivers accurate guidance fast and reduces manual work."

Director of Operations

Embedding agentic AI in Slack to eliminate workflow bottlenecks

Media & EntertainmentArtificial Intelligence

View case study

"tkxel built a Slack AI assistant that surfaces SOPs instantly and keeps our team productive."

Head of Operations

Operate AI across cloud, on-premise, and hybrid environments

Secure deployment models

Support private AI deployment services across cloud, on-premise, hybrid, and air-gapped environments with compliance-ready controls for data residency, access, and auditability.

Cloud & on-premise expertise

Design and manage hybrid AI deployment models that support private LLM deployment, self-hosted LLM solutions, model serving, and scalable AI operations.

GPU cost optimization

Improve GPU infrastructure management with optimized NVIDIA GPU usage, inference optimization, workload efficiency, and cost control across production AI workloads.

Fully managed operations

Keep AI workloads stable with fully managed MLOps managed services, Kubernetes-based orchestration, monitoring, security updates, and continuous performance optimization.

Partnered with the World’s Top Cloud Providers

As a recognized cloud partner, we help teams apply AWS Well-Architected practices, native optimization tools, and scalable cloud patterns to support secure, cost-efficient workloads.

As a Microsoft Solutions Partner, we bring Azure-aligned guidance, modernization support, and optimization practices to help teams manage cloud and hybrid workloads with greater control.

Backed by Google Cloud expertise, tkxel supports scalable architecture, data-driven workloads, and cloud-native best practices for teams building secure and optimized cloud environments.

Our expertise and technologies with AI

LLMs
Agent frameworks
Vector stores

OPENAI

ANTHROPIC

GEMINI

DEEPSEEK

LLAMA

MISTRAL

AND MORE

lANGCHAIN

OPENAI AGENT BUILDER

MICROSOFT COPILOT STUDIO

qdrant

weaviate

pgvector

We’ve been recognized by the best, year after year

AMERICA’S FASTEST GROWING COMPANY

Top 15 inspiring workplaces for 2026

titan business PLATINUM award AI & AUTOMATION

FINANCIAL TIMES

mogul people leader

FORBES COACHES COUNCIL

ISO 27001 CERTIFIED

ISO 20000 CERTIFIED

ISO 9001 CERTIFIED

CMMI DEV 3 CERTIFIED

Scale secure AI operations across cloud and on-premise

“tkxel completely transformed the way we manage our customer relationships. Their customized CRM system streamlined our processes and improved customer satisfaction. We highly recommend their services to any business looking for real results.”

Nick Drogo

Global Director IT, Knowles

“They helped us build a docketing app with an intuitive user interface, allowing our attorneys to track over 10,000 U.S. and international patent systems.”

Robert K Burger

COO, Sterne Kessler

“tkxel has proven beyond par that they excel not just in building and integrating with our team but building at a level that is at par with any US development team. Working with tkxel is one of the best decisions we have made.”

Umair Bashir

CTO, Replenium

“tkxel shared our vision right from the get go, and helped us achieve the unthinkable through perseverance and a thorough attention to detail. Their team was highly professional and possessed a firm grasp on technicalities, a combination that is hard to find in the industry.”

Pam Chitwood

Product Manager, ABB

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Nick Drogo

Global Director IT, Knowles

“They helped us build a docketing app with an intuitive user interface, allowing our attorneys to track over 10,000 U.S. and international patent systems.”

Robert K Burger

COO, Sterne Kessler

Umair Bashir

CTO, Replenium

Pam Chitwood

Product Manager, ABB

Frequently asked questions

What are private AI deployment services?

Private AI deployment services help you run AI models, LLMs, and AI applications in controlled cloud, on-premise, hybrid, or air-gapped environments without exposing sensitive data to public platforms.

Can tkxel support on-premise AI deployment?

Yes. tkxel helps set up and manage on-premise AI deployment for workloads that require stronger data control, lower latency, compliance alignment, or private infrastructure.

What is private LLM deployment?

Private LLM deployment means hosting large language models inside your own cloud, on-premise, or hybrid setup to support secure copilots, RAG systems, internal tools, and AI workflows.

Do you offer self-hosted LLM solutions?

Yes. tkxel builds self-hosted LLM solutions with secure model serving, private inference, and deployment options using tools such as Kubernetes, vLLM, and Ollama where relevant.

Can AI workloads run in air-gapped environments?

Yes. For sensitive or restricted use cases, tkxel can help deploy AI workloads in air-gapped environments with controlled access, local model hosting, secure updates, and monitoring.

How does tkxel manage GPU infrastructure?

tkxel supports GPU infrastructure management across cloud and on-premise setups, including NVIDIA GPU configuration, utilization monitoring, inference optimization, capacity planning, and cost control.

What is hybrid AI deployment?

Hybrid AI deployment allows you to run AI workloads across cloud and on-premise environments based on security, performance, data residency, scalability, and cost needs.

How do MLOps managed services help?

MLOps managed services help keep AI models reliable after deployment through model monitoring, versioning, drift detection, retraining workflows, rollback planning, and ongoing optimization.

Can private AI support data residency requirements?

Yes. Private and hybrid AI deployments can help keep data, models, and workloads within approved regions, systems, or infrastructure to support data residency and compliance requirements.

What are sovereign AI solutions?

Sovereign AI solutions help organizations maintain greater control over where AI data, models, and infrastructure are hosted, processed, accessed, and governed.

How does tkxel optimize AI inference?

tkxel improves inference performance through model serving optimization, GPU utilization improvements, latency monitoring, throughput tuning, autoscaling, and cost-aware deployment planning.

AI Operations Managing Hybrid Cloud and On-Premise Tech Safely

featured AI CLIENTS

Is your cloud ready for AI workloads?

50%

of cloud compute resources will be devoted to AI workloads by 2029.

90%

of organizations will adopt hybrid cloud through 2027.

75%

of server AI infrastructure spending will be on accelerated servers by 2028.

Cloud & on-premise AI services for secure, scalable operations

Cloud & On-Premise AI Operations

Controlled AI workload deployment

Cloud & On-Premise AI Operations

On-premise AI solutions

Cloud & On-Premise AI Operations

Secure LLM hosting & deployment

Cloud & On-Premise AI Operations

Self-hosted LLM solutions

Cloud & On-Premise AI Operations

AI infrastructure services

Cloud & On-Premise AI Operations

GPU infrastructure management

Cloud & On-Premise AI Operations

Hybrid AI deployment

Cloud & On-Premise AI Operations

MLOps managed services

Cloud & On-Premise AI Operations

AI observability & model serving

Cloud & On-Premise AI Operations

Sovereign AI & data residency solutions

Cloud & On-Premise AI Operations

Managed AI operations & optimization

Choose the right AI deployment model

How we deliver cloud & on-premise AI operations

01

How we deliver cloud & on-premise AI operations

Infrastructure readiness

Hybrid AI planning

AI Workload environment setup

LLM deployment

GPU optimization

MLOps management

What you can achieve with cloud and on-premise AI operations

Greater control over sensitive AI workloads

Faster and more reliable AI performance

Lower AI infrastructure costs

Faster model releases

Consistent performance at scale

Build AI operations that remain secure, reliable, and cost-efficient as demand grows.

How we’ve helped businesses succeed with AI

60% reduction in response times through AI-automated email workflows

Don Sgouridis

50% faster discovery with an AI-powered knowledge sharing agent

Chris Head

50% reduction in support workload with AI-powered document retrieval

Embedding agentic AI in Slack to eliminate workflow bottlenecks

Secure deployment models

Cloud & on-premise expertise

GPU cost optimization

Fully managed operations

Partnered with the World’s Top Cloud Providers

Our expertise and technologies with AI

We’ve been recognized by the best, year after year

AMERICA’S FASTEST GROWING COMPANY

Top 15 inspiring workplaces for 2026

titan business PLATINUM award AI & AUTOMATION

FINANCIAL TIMES

mogul people leader

FORBES COACHES COUNCIL

ISO 27001 CERTIFIED

ISO 20000 CERTIFIED

ISO 9001 CERTIFIED

CMMI DEV 3 CERTIFIED

Scale secure AI operations across cloud and on-premise

Nick Drogo

Robert K Burger

Umair Bashir

Pam Chitwood

Nick Drogo

Robert K Burger

Cloud & on-premise AI services for
secure, scalable operations