AI for Agencies: Multi-Client GPU Hosting Setup

The Agency AI Hosting Challenge

Agencies building AI solutions for multiple clients need infrastructure that is shared for cost efficiency but isolated for security. Running separate dedicated GPU servers per client is expensive. Running all clients on shared API providers surrenders control over data, cost, and performance.

The solution is a well-architected multi-client GPU hosting setup where one or more GPUs serve multiple clients with proper isolation, metering, and model management. This approach lets agencies offer AI-powered services at margins that API reselling cannot match. For more industry-specific approaches, explore our use cases category.

Multi-Client Architecture

A practical multi-client setup has three layers: the routing layer, the inference layer, and the monitoring layer.

Routing layer. An API gateway (nginx, Traefik, or a custom service) authenticates client requests, applies rate limits, tags requests with client IDs, and routes to the appropriate model endpoint. Each client gets a unique API key and endpoint URL.

Inference layer. One or more vLLM instances serve models. For agencies where all clients use the same model, a single vLLM instance with continuous batching handles all traffic efficiently. For agencies where clients need different models, run separate vLLM instances per model, each on its own port.

Monitoring layer. Track per-client token usage, latency, and error rates. This feeds both billing and capacity planning. See our GPU monitoring guide for infrastructure setup.

GPU Resource Allocation per Client

Agencies typically serve 5-20 clients from shared GPU infrastructure. Here is how to plan resource allocation.

A single RTX 3090 running Llama 3 8B at ~90 tok/s can serve roughly 10 Starter clients or 3-4 Professional clients simultaneously. Rate limiting at the gateway prevents any single client from monopolising the GPU.

For enterprise clients with dedicated SLAs, allocate a separate GPU or server. Explore multi-GPU clusters when total client demand exceeds single-GPU capacity.

Model Management Across Clients

Shared base model. The simplest approach: all clients use the same open-source model (e.g., Llama 3 8B). Different behaviour per client is achieved via system prompts, not different models. One model in VRAM, maximum efficiency.

Per-client LoRA adapters. For clients needing customised behaviour, train lightweight LoRA adapters and serve them via vLLM’s multi-LoRA feature. The base model stays loaded, and adapters (~100 MB each) swap per request. You can serve 10+ client-specific adapters from a single GPU.

Multiple models. If clients need fundamentally different models (e.g., one needs a coding model, another needs a general assistant), run separate vLLM instances. On a 24 GB GPU, you could run a 4-bit 7B model (~4.5 GB) alongside a 4-bit 3B model (~2 GB), though this is more complex to manage.

For comprehensive model deployment, read our self-hosted LLM guide.

Billing and Usage Metering

Accurate token metering is essential for agency profitability. Implement metering at the API gateway level.

# Example: Log per-client usage with timestamps
# Gateway middleware pseudocode
def process_request(request):
    client_id = authenticate(request.api_key)
    input_tokens = count_tokens(request.prompt)

    response = forward_to_vllm(request)
    output_tokens = count_tokens(response.text)

    log_usage(
        client_id=client_id,
        input_tokens=input_tokens,
        output_tokens=output_tokens,
        model=request.model,
        timestamp=now()
    )
    return response

Price your AI services based on token volume with healthy margins. If your infrastructure cost is $0.06 per million tokens (RTX 3090 at capacity), pricing at $0.50-2.00 per million tokens is competitive against API providers while maintaining strong margins. Compare pricing with the cost per million tokens calculator.

GPU Sizing and Cost Planning

Plan GPU capacity based on total client demand, not individual clients.

At $1 per million tokens billed to clients, a medium agency generating 50M tokens/month earns $50,000/year from $3,120/year in GPU costs. Use the GPU vs API cost comparison to see how this stacks against reselling API access.

Start with a single GPU, prove the model with your first clients, then scale as demand grows. Use the LLM cost calculator to plan your dedicated GPU hosting budget as you grow.