Migrate from Google Gemini to Dedicated GPU: Savings Calculator
How much can you save by moving from Google Gemini (Gemini Pro / Flash) to a dedicated GPU server?
Projected Savings
Google prices Gemini aggressively to lock teams into the GCP ecosystem. Once you factor in Vertex AI overhead, data egress fees, and per-token charges, the real cost is steeper than it first appears. At a typical £350/month Gemini spend:
- £261/month (75% reduction)
- £3,132/year in total savings
Savings by Current Google Gemini Spend
| Current Google Gemini Spend | GigaGPU RTX 3090 Cost | Monthly Savings | Annual Savings |
|---|---|---|---|
| £100/mo | £89/mo | £11/mo | £132/yr |
| £250/mo | £89/mo | £161/mo | £1,932/yr |
| £500/mo | £89/mo | £411/mo | £4,932/yr |
| £1000/mo | £89/mo | £911/mo | £10,932/yr |
| £2500/mo | £89/mo | £2411/mo | £28,932/yr |
| £5000/mo | £89/mo | £4911/mo | £58,932/yr |
GigaGPU pricing is fixed monthly. No per-token, per-image, or per-request fees.
The GCP Lock-In Problem
Google Gemini API pricing varies by model tier, but the deeper cost is ecosystem dependency. Your data flows through GCP, your billing ties to Google Cloud, and migrating away means untangling from Vertex AI, Cloud Storage, and IAM. Self-hosting on dedicated GPUs eliminates the platform dependency entirely — you own the infrastructure and can run Google’s own open-source Gemma alongside LLaMA or any other model.
What GigaGPU Provides
- Dedicated hardware: A full RTX 3090 server exclusively for your workloads. No sharing, no noisy neighbours.
- Recommended alternative: LLaMA 3 8B or Gemma 9B delivers comparable quality to Gemini Pro / Flash for most production use cases.
- Fixed pricing: £89/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.
Leaving the Google Ecosystem
- Audit current usage: Export your Google Gemini usage data to understand volume, peak times, and model requirements.
- Select your GPU server: Based on your throughput needs, choose from GigaGPU dedicated plans starting at £89/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: Point your application to your new server. Most inference servers (vLLM, TGI) support OpenAI-compatible API formats for drop-in migration.
- Run parallel testing: Run both Google Gemini 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 cancel your Google Gemini subscription.
API Migration Notes
GigaGPU servers support OpenAI-compatible API endpoints out of the box. Google Gemini uses a slightly different API format, but the transition is straightforward — swap the client library and update your endpoint configuration. No core application logic changes required for most integrations.
Break Free from GCP Dependency
Stop paying per-token to Google Gemini. Get a dedicated RTX 3090 server for £89/month and keep 100% of your savings.
