Migrate from Google Vertex to Dedicated GPU: Savings Calculator
How much can you save by moving from Google Vertex (Vertex AI Models) to a dedicated GPU server?
Projected Savings
Vertex AI bundles model inference with GCP infrastructure costs, making it hard to isolate what you actually spend on AI. Teams that untangle their Vertex bills often discover they are paying platform overhead on top of per-prediction fees. At £500/month Vertex spend:
- £391/month (78% reduction)
- £4,692/year in total savings
Savings by Current Google Vertex Spend
| Current Google Vertex Spend | GigaGPU RTX 5080 Cost | Monthly Savings | Annual Savings |
|---|---|---|---|
| £100/mo | £109/mo | API cheaper at this spend | — |
| £250/mo | £109/mo | £141/mo | £1,692/yr |
| £500/mo | £109/mo | £391/mo | £4,692/yr |
| £1000/mo | £109/mo | £891/mo | £10,692/yr |
| £2500/mo | £109/mo | £2391/mo | £28,692/yr |
| £5000/mo | £109/mo | £4891/mo | £58,692/yr |
GigaGPU pricing is fixed monthly. No per-token, per-image, or per-request fees.
Untangling Vertex AI From Your GCP Bill
Google Vertex AI bundles model access with GCP infrastructure costs. Per-prediction fees mix with compute, storage, and networking charges across your GCP bill, making the true cost of AI inference difficult to track. Self-hosted models on dedicated GPUs remove the platform dependency and per-prediction fees — and give you a single, transparent line item for AI compute.
Transparent AI Costs Outside GCP
- Dedicated hardware: A full RTX 5080 server exclusively for your workloads. No sharing, no noisy neighbours.
- Recommended alternative: LLaMA 3 8B or Gemma 9B delivers comparable quality to Vertex AI Models for most production use cases.
- Fixed pricing: £109/month regardless of how many tokens, images, or requests you process.
- Full control: SSH access, custom model deployment, fine-tuning capability, no vendor lock-in.
- Data sovereignty: Your data stays on your server. No third-party data processing or logging.
Extracting Your AI from Vertex
- Audit current usage: Use GCP Cost Explorer to isolate Vertex AI prediction costs from other GCP services.
- Select your GPU server: Based on your throughput needs, choose from GigaGPU dedicated plans starting at £109/month.
- Deploy your model: GigaGPU servers come with CUDA, Docker, and inference frameworks pre-installed. Deploy LLaMA 3 8B or Gemma 9B in under 15 minutes.
- Update API endpoints: Replace Vertex AI SDK calls with OpenAI-compatible endpoints supported by vLLM or TGI on your GigaGPU server.
- Run parallel testing: Run both Google Vertex and your self-hosted model in parallel for 1-2 weeks to validate quality and performance.
- Cut over: Once validated, switch fully to your dedicated server and decommission your Vertex AI endpoints.
SDK Migration
Vertex AI uses Google’s proprietary SDK with GCP-specific authentication. Migration requires replacing the Vertex AI client with a standard OpenAI-compatible client. GigaGPU servers support this format natively, so the transition involves updating your client library and endpoint configuration. Core application logic remains the same.
Simplify Your AI Cost Structure
Replace opaque Vertex AI billing with a transparent £109/month for dedicated GPU hardware.
