CrewAI Hosting
Run Multi-Agent AI Workflows on Dedicated UK GPU Servers
Deploy CrewAI crews on bare metal GPU servers with full root access. Orchestrate autonomous agents backed by self-hosted LLMs — no API fees, no token limits, complete data privacy.
What is CrewAI Hosting?
CrewAI is an open source Python framework for orchestrating teams of autonomous AI agents. Each agent is assigned a specific role, goal, and backstory, then collaborates with other agents to complete complex multi-step tasks — from research and analysis to content generation and code review.
With GigaGPU’s dedicated GPU servers you can self-host the LLMs that power your CrewAI agents using Ollama, vLLM, or any OpenAI-compatible backend. This means your entire agentic pipeline runs on hardware you control: no per-token charges, no shared resources, and no data leaving your environment.
CrewAI has quickly gained traction with over 20,000 GitHub stars, native support for Ollama and vLLM backends, and a growing ecosystem of tools and integrations — making self-hosted GPU infrastructure the natural deployment choice for production agent teams.
Trusted by AI teams deploying multi-agent workflows, RAG pipelines, and autonomous agent systems across the UK and Europe.
Why Self-Host CrewAI on Dedicated GPUs?
Multi-agent workflows involve many LLM calls per task. Self-hosting eliminates per-token costs and gives you full control over performance, privacy, and model choice.
Eliminate Per-Token Costs
CrewAI agents make dozens of LLM calls per task — researcher, analyst, writer, reviewer. With a dedicated GPU, every call is free after your flat monthly server cost. At scale, this saves thousands compared to managed API billing.
Complete Data Privacy
Agent workflows often process sensitive business data — customer records, financial reports, internal documents. Self-hosting means your data never leaves your server or passes through a third-party API provider.
Lower Latency, No Rate Limits
Multi-agent orchestration is latency-sensitive — each agent waits for others to finish. A local LLM endpoint eliminates network round trips, API rate limiting, and shared-resource queuing that slows down agent chains.
Full Model Control
Choose exactly which LLM each agent uses — a fast 7B model for the researcher, a reasoning-focused 70B model for the analyst. Fine-tune models for your domain. Swap models instantly without changing providers.
CrewAI Hosting Use Cases
Self-hosted CrewAI crews on dedicated GPUs power a wide range of production workflows.
Autonomous Research & Analysis
Deploy research crews where a planner agent decomposes queries, a researcher agent gathers data via tools, and an analyst agent synthesises findings — all backed by self-hosted LLMs for unlimited parallel runs.
Content Generation Pipelines
Build content crews with writer, editor, and SEO reviewer agents that produce, refine, and optimise articles, reports, or marketing copy at scale — with zero per-token API fees at high volume.
Code Review & Development Crews
Orchestrate coding agents that plan, implement, test, and review code changes. Pair CrewAI with code-specialist models like CodeLlama or DeepSeek-Coder for private, on-premise software development automation.
Customer Support Automation
Create support crews with specialised agents for triage, knowledge retrieval, response drafting, and quality assurance. Keep customer data fully on-premise while scaling support capacity without adding headcount.
RAG & Document Processing
Combine CrewAI’s agentic RAG capabilities with local vector databases and self-hosted embeddings. Agents retrieve, cross-reference, and summarise documents without any data leaving your infrastructure.
Security Auditing & Compliance
Build multi-agent security audit crews that scan codebases, analyse configurations, and generate compliance reports. Run sensitive security analysis entirely on private hardware with no external API dependencies.
Recommended GPUs for CrewAI Hosting
Multi-agent workflows benefit from fast inference and sufficient VRAM to run your chosen LLM. Here are our top picks for CrewAI deployments.
All servers include 128GB RAM, NVMe storage, 1 Gbps port, full root access, and any OS. View all GPU plans →
Deploy CrewAI in 4 Steps
From order to running multi-agent workflows in under an hour.
Choose Your GPU
Pick the GPU that fits your agent model size and concurrency needs. Select your OS and NVMe storage.
Install Ollama or vLLM
Run curl -fsSL https://ollama.com/install.sh | sh and pull the models your agents will use — Mistral, LLaMA, DeepSeek, or any open-weight model.
Set Up CrewAI
Install CrewAI with pip install crewai, define your agents, tasks, and tools. Point agents at your local Ollama or vLLM endpoint.
Run Your Crew
Execute your multi-agent workflow. Agents collaborate autonomously — researching, analysing, writing, and reviewing — all on your dedicated GPU hardware.
Compatible Frameworks & Tools
CrewAI integrates with the full open source AI ecosystem — all installable on your GigaGPU server.
CrewAI Hosting — Frequently Asked Questions
Everything you need to know about running CrewAI on dedicated GPU servers.
llm="ollama/mistral" in your agent definition and it connects to your local Ollama instance automatically.Available on all servers
- 1Gbps Port
- NVMe Storage
- 128GB DDR4/DDR5
- Any OS
- 99.9% Uptime
- Root/Admin Access
Our dedicated GPU servers provide full hardware resources and a dedicated GPU card, ensuring unmatched performance and privacy. Perfect for self-hosting CrewAI agent workflows, RAG pipelines, multi-agent orchestration, and any other AI workload — with no shared resources and no token fees.
Get in Touch
Have questions about which GPU is right for your CrewAI deployment? Our team can help you choose the right configuration for your agent count, model sizes, and throughput requirements.
Contact Sales →Or browse the knowledgebase for setup guides on Ollama, vLLM, and more.
Start Hosting CrewAI Today
Flat monthly pricing. Full GPU resources. UK data centre. Deploy autonomous AI agent teams in under an hour.
