Get your Hugging Face model — Llama, Mistral, Gemma, or BERT — deployed to a secure, scalable, production-ready endpoint on Google Cloud, fully containerised and API-ready.
I Will Deploy Hugging Face LLMs to GCP Vertex AI or Cloud Run
A focused 45-minute expert consultation to assess your model, map out your GCP deployment strategy, and answer your architecture questions.
- 45-minute GCP Vertex AI / Cloud Run strategy consultation
- Model assessment: RAM/VRAM requirements and GPU tier recommendation
- Architecture and cost estimation guidance
- Recommended deployment approach documented in writing
- Integration pathway advice for connecting model to your existing app
Full end-to-end deployment of your Hugging Face model to a secure GCP endpoint, plus source code so your team can own and extend it.
- Everything in the Consultation tier
- Full Dockerised model containerisation and deployment to Vertex AI or Cloud Run
- GPU provisioning (T4, L4, or A100 as appropriate)
- Secure, VPC-native private API endpoint configuration
- Python test script to verify the live endpoint
- Full deployment source code delivered to you
The complete deployment package with detailed inline code comments throughout, making the codebase fully maintainable by your engineering team.
- Everything in the Deployment tier
- Detailed inline code comments throughout all scripts and configuration files
- Documented architecture decisions explaining GPU selection and security choices
- Handover-ready codebase your team can confidently maintain and extend
- Suitable for larger or more complex LLMs (e.g. Llama 3, Mistral large variants)
- Priority order management and closer collaboration via order chat
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Key details about this service to help you decide. Generated by Zinn Hub, not the seller.
Value Position
Deployment Target
GPU Options
Security Approach
Best For
What You'll Receive
Full Description
You have found the right model. Now you need it running reliably in production — not just locally, not in a notebook, but behind a secure, scalable API your application can actually call.
That is exactly what this service delivers.
Zinn Digital are Google Cloud experts based in London. We take your chosen Hugging Face model and deploy it to a production-ready environment on Vertex AI or Cloud Run — fully containerised, GPU-provisioned, and protected behind a secure, VPC-native endpoint. When the work is done, you receive a live API you can immediately integrate into your product.
**What makes this different from "just running a script"**
Anyone can spin up a GPU instance and hope for the best. We assess your model's actual RAM and VRAM requirements before a single line of code is written, select the right GPU tier (T4, L4, or A100) to balance cost and latency, and build infrastructure that scales with your workload rather than falling over under it. Every deployment is secured by design — no public endpoints left open, no credentials hardcoded.
**How it works**
First, send us the model link and a brief description of your use case. We assess the model's resource requirements and confirm the deployment approach. We then containerise the model using Docker, deploy it to Vertex AI or Cloud Run, configure GPU provisioning and API security, and finally hand you a working Python test script so you can verify the endpoint yourself.
**What is included**
Depending on the tier you choose, you will receive some or all of: an expert consultation and architecture assessment, full model deployment to a secure GCP endpoint, source code for the containerisation and deployment pipeline, and detailed inline code comments so your team can maintain and extend the work confidently.
**Who this is for**
This service suits developers and engineering teams who have identified a Hugging Face model they want to use in production but lack the GCP infrastructure experience to deploy it safely and efficiently. It is equally well suited to technical founders who need a working AI backend without hiring a full cloud team, and to organisations already using Google Cloud who want expert hands on a specific deployment challenge.
**Before you order**
Every project is unique. Model size, GPU requirements, existing GCP architecture, and security constraints all vary. Please send a message before placing your order so we can confirm the approach is right for your situation and agree on scope. This ensures the work starts on the right footing and there are no surprises.
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Compare Packages
| Feature | Consultation | Deployment | Deployment + Comments |
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
| Revisions | 0 | 0 | 0 |
| 45-minute GCP Vertex AI / Cloud Run strategy consultation | ✓ | ✕ | ✕ |
| Model assessment: RAM/VRAM requirements and GPU tier recommendation | ✓ | ✕ | ✕ |
| Architecture and cost estimation guidance | ✓ | ✕ | ✕ |
| Recommended deployment approach documented in writing | ✓ | ✕ | ✕ |
| Integration pathway advice for connecting model to your existing app | ✓ | ✕ | ✕ |
| Everything in the Consultation tier | ✕ | ✓ | ✕ |
| Full Dockerised model containerisation and deployment to Vertex AI or Cloud Run | ✕ | ✓ | ✕ |
| GPU provisioning (T4, L4, or A100 as appropriate) | ✕ | ✓ | ✕ |
| Secure, VPC-native private API endpoint configuration | ✕ | ✓ | ✕ |
| Python test script to verify the live endpoint | ✕ | ✓ | ✕ |
| Full deployment source code delivered to you | ✕ | ✓ | ✕ |
| Everything in the Deployment tier | ✕ | ✕ | ✓ |
| Detailed inline code comments throughout all scripts and configuration files | ✕ | ✕ | ✓ |
| Documented architecture decisions explaining GPU selection and security choices | ✕ | ✕ | ✓ |
| Handover-ready codebase your team can confidently maintain and extend | ✕ | ✕ | ✓ |
| Suitable for larger or more complex LLMs (e.g. Llama 3, Mistral large variants) | ✕ | ✕ | ✓ |
| Priority order management and closer collaboration via order chat | ✕ | ✕ | ✓ |
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Deploy Hugging Face LLMs to GCP Vertex AI or Cloud Run
Deploy Hugging Face LLMs to GCP Vertex AI or Cloud Run
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Frequently Asked Questions
We work with a wide range of models including Llama 3, Mistral, Gemma, BERT, and other transformer-based models hosted on Hugging Face. Model size and GPU requirements vary, so please message us before ordering with your model link and use case so we can confirm compatibility and recommend the right tier.
Yes. You will need an active GCP account with billing enabled. We work within your environment so that you retain full ownership of all deployed infrastructure and incur GCP costs directly. If you are unsure how to set this up, the Consultation tier is a great starting point.
At a minimum, we need the Hugging Face model link and a brief description of what you intend to use it for. For deployment tiers, we will also need access credentials to your GCP project. We will guide you through exactly what to share securely via the order chat.
Larger models require more careful resource assessment, longer container build times, and more thorough testing of the live endpoint. The additional time ensures the deployment is stable, secure, and properly documented before handover.
A VPC-native (Virtual Private Cloud) deployment means your model endpoint is not exposed to the open internet. Access is restricted at the network level, which is important for keeping proprietary data and model weights secure in a production environment.
The Deployment tier includes full source code so your team has everything they need. The Deployment + Comments tier additionally provides detailed inline annotations explaining every significant decision, making the codebase straightforward for your engineers to maintain and extend over time.
Every model and every GCP environment is different. A quick conversation ensures we understand your requirements, confirm the right tier, and avoid any back-and-forth after the order starts. It also means we can flag any unusual GPU or cost considerations specific to your chosen model upfront.
Customer Reviews
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Very knowledgeable VertexAI expert that answered all my questions and even gave me insights that were useful for me but I haven't specifically asked aout.
Umair is by far the best person we have worked with on Zinn Hub. He was exceptional at understanding our project's unique requirements. He went above and beyond. I would recommend him for any AI project.
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