Migrate from Cohere to Dedicated GPU: Savings Calculator
How much can you save by moving from Cohere (Command R+ / Embed) to a dedicated GPU server?
Projected Savings
Cohere bills separately for embeddings and generation — two API costs where a single GPU can handle both. At a typical £300/month combined Cohere spend:
- £211/month (70% reduction)
- £2,532/year in total savings
Savings by Current Cohere Spend
| Current Cohere 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.
Replacing Two API Bills with One GPU
Cohere bundles LLM and embedding capabilities, but you pay for both separately. A single dedicated GPU can run both an open-source LLM and embedding model simultaneously, replacing two API bills with one fixed cost. For RAG pipeline users, this consolidation is particularly compelling — your entire retrieve-and-generate workflow runs on a single machine.
The GigaGPU Replacement Stack
- Dedicated hardware: A full RTX 3090 server exclusively for your workloads. No sharing, no noisy neighbours.
- Recommended alternative: LLaMA 3 8B + BGE Embeddings delivers comparable quality to Command R+ / Embed 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.
Migration Path from Cohere
- Audit current usage: Export your Cohere usage data — separately track Command and Embed volumes to size your GPU correctly.
- Select your GPU server: Based on your throughput needs, choose from GigaGPU dedicated plans starting at £89/month.
- Deploy your models: GigaGPU servers come with CUDA, Docker, and inference frameworks pre-installed. Deploy LLaMA 3 8B + BGE Embeddings in under 15 minutes.
- Re-embed your corpus: Switching embedding models requires re-indexing your vector database. Plan for a one-time batch re-embedding job.
- Run parallel testing: Run both Cohere and your self-hosted models in parallel for 1-2 weeks to validate quality and performance.
- Cut over: Once validated, switch fully to your dedicated server and cancel your Cohere subscription.
Embedding Migration Consideration
Switching from Cohere Embed to a self-hosted embedding model means your existing vector embeddings are incompatible — you will need to re-embed your document corpus. Plan this as a one-time migration cost. Once complete, GigaGPU servers support OpenAI-compatible API endpoints for the LLM portion, making the generation side a straightforward endpoint swap.
Consolidate Two API Bills Into One Fixed Cost
Stop paying separate per-token fees for embeddings and generation. Get a dedicated RTX 3090 server for £89/month.
