Loader logo

AI and LLM Hosting for Agencies

Deploy large language models on your own infrastructure, in the cloud, or both. Maintain full data control, reduce API costs, and run models exactly where your clients need them.

[Hero Illustration]

Why LLM Hosting Matters

Understand the strategic advantages of dedicated AI infrastructure for your agency and its clients.

Data Privacy

Complete Data Sovereignty

On-premises deployments keep sensitive client data off third-party servers entirely. Maintain full control over data residency, meet compliance requirements, and enforce security policies without depending on external API providers or their changing terms of service.

Performance

Predictable Cost at Scale

Eliminate per-token API costs and rate-limiting constraints. Run unlimited inference with fixed monthly infrastructure costs, reduce latency by deploying models closer to your users, and support high-volume workloads without throttling or surprise billing.

Flexibility

Multi-Model Architecture

Run open-source models alongside commercial APIs. Combine Llama, Mistral, and other open models with OpenAI and Anthropic endpoints. Choose the right model for each task without vendor lock-in, and switch between providers as the landscape evolves.

Deployment Models

Choose the architecture that fits your infrastructure, compliance, and performance requirements.

On-Premises

Ollama, vLLM, and GPT4All runtimes
GPU provisioning and management
Local REST API endpoints
Complete data isolation and control
Air-gapped deployment options

Hybrid

Local processing for sensitive data
Cloud APIs for specialized tasks
Intelligent request routing
Unified API gateway
Cost-optimized workload distribution

Cloud

AWS Bedrock and Google Vertex AI
Cloud GPU infrastructure management
Auto-scaling and load balancing
Multi-region deployment
Managed model serving endpoints

Pricing is scoped per engagement. Contact us for a custom quote based on your infrastructure requirements.

What We Manage

End-to-end management of your LLM infrastructure so your team can focus on building products.

Infrastructure

GPU Provisioning

Configuration and optimization of GPU resources for inference workloads across NVIDIA and cloud-native accelerators.

Model Deployment

Containerized deployment of LLMs with orchestration, health checks, and zero-downtime rollouts.

Connectivity

API Gateway

RESTful and OpenAI-compatible endpoints with request routing, rate limiting, and authentication.

Integration Services

Webhooks, SDKs, and middleware for connecting your LLM endpoints to existing applications.

Security

Access Controls

Role-based access, API key management, encryption at rest and in transit, and audit logging.

Compliance

Data residency enforcement, retention policies, and documentation for SOC 2 and GDPR requirements.

Operations

Monitoring

Real-time metrics for latency, throughput, error rates, and resource utilization with alerting.

Cost Management

Per-deployment cost tracking, resource optimization recommendations, and usage reporting.

Supported Runtimes and APIs

Broad platform support for self-hosted inference and managed cloud AI services.

Self-Hosted Runtimes
Ollama vLLM GPT4All Text Gen WebUI

Ollama

Simple local model management and inference server with broad model compatibility.

vLLM

High-throughput inference engine with PagedAttention for optimized memory usage.

GPT4All

Run quantized models on consumer-grade hardware with minimal configuration.

Text Gen WebUI

Feature-rich web interface for model interaction, testing, and prompt development.

Cloud AI Platforms
OpenAI Anthropic AWS Bedrock Google Vertex AI

OpenAI

GPT-4o and o1 models via managed APIs with function calling and vision capabilities.

Anthropic

Claude models with extended context windows and enterprise-grade reliability.

AWS Bedrock

Managed foundation models with VPC integration and AWS compliance features.

Google Vertex AI

Gemini and PaLM models on Google infrastructure with MLOps tooling.

Our Support

Comprehensive management and support services for your LLM deployment.

Inference Monitoring

Real-time tracking of model performance, latency, token throughput, and error rates across all endpoints.

Performance Tuning

Optimization of inference speed, batch processing, and resource utilization to maximize cost efficiency.

Model Management

Version control, lifecycle management, A/B testing, and zero-downtime model swaps across deployments.

Security Operations

Ongoing compliance audits, vulnerability management, security patching, and access review processes.

Scaling Support

Infrastructure scaling to handle increased inference volume, new model deployments, and user growth.

Escalation Response

Priority incident handling with defined SLAs, root cause analysis, and proactive issue prevention.

Our LLM Hosting service covers infrastructure management and deployment support. For AI strategy consulting, model fine-tuning, and prompt engineering, explore our AI Services offering.

How It Works

Our proven four-step process for deploying your LLM infrastructure.

1

Define Use Case

Assess your requirements including model selection, deployment location, data privacy needs, and performance targets. We map your use cases to the right architecture.

2

Architecture Design

Design the infrastructure, networking, security layers, and integration points with your existing systems. Includes capacity planning and cost modeling.

3

Deploy and Validate

Deploy models, configure API endpoints, run load testing, and validate performance benchmarks. Security audit and compliance checks before production launch.

4

Manage and Optimize

Ongoing monitoring, model updates, cost optimization, and scaling support. Continuous improvement as your usage patterns evolve and new models become available.

Frequently Asked Questions

Common questions about LLM hosting and deployment for agencies.

What is the difference between on-premises and cloud deployment?
On-premises runs models on your own infrastructure for maximum data control and privacy. Cloud uses managed services like AWS Bedrock to reduce infrastructure overhead. Hybrid combines both, routing sensitive workloads locally and specialized tasks to cloud APIs.
How is LLM hosting priced?
Pricing depends on your deployment model, infrastructure requirements, and usage patterns. On-premises has hardware and management costs while cloud follows consumption-based pricing. We provide custom quotes scoped to your specific needs.
Can I run multiple models at the same time?
Yes. With proper GPU allocation and orchestration, you can serve multiple models concurrently. Our infrastructure supports intelligent routing to direct requests to the optimal model based on task type, latency requirements, or cost targets.
How do you handle model updates?
We use rolling updates and blue-green deployment strategies to apply patches and model version upgrades with zero downtime. All updates are tested in staging before production rollout, and rollback procedures are always in place.
Is my data secure with on-premises deployment?
On-premises provides the highest level of data security. Your data never leaves your network. We implement encryption, access controls, audit logging, and security best practices as part of every deployment. Air-gapped options are available.
How does auto-scaling work?
Cloud and hybrid deployments use metric-based auto-scaling to add inference capacity during demand spikes and scale down during quiet periods. We configure scaling policies, thresholds, and warm-up strategies to balance performance with cost.
Which open-source models do you support?
We support the major open-source model families including Llama, Mistral, Phi, Gemma, and others available through Ollama, vLLM, and Hugging Face. If you need a specific model, contact us for a compatibility assessment.
How do I integrate with my existing applications?
We provide RESTful APIs compatible with the OpenAI API format, making integration straightforward for any application that already uses OpenAI. We also support custom endpoints, webhooks, and SDK-based integrations tailored to your tech stack.

Ready to Deploy Your LLMs?

Get started with dedicated LLM hosting for your agency. Our team will design the right architecture for your use case and guide you through deployment.

Custom quotes based on your infrastructure requirements.