How much does an LLM cost in the UK?
Whether you’re a startup, a researcher, or an enterprise exploring large language models LLM in UK, understanding the full cost spectrum—from APIs to self-hosting to in-house development—is vital. Here’s a breakdown of what to expect.
1. API Usage Costs
Using cloud-based LLM APIs (like OpenAI, Anthropic, Google, etc.) is often the simplest route:
- OpenAI’s GPT models:
- GPT-3.5-Turbo: ~$0.0015 per 1,000 input tokens and ~$0.0020 per 1,000 output tokens.
- GPT-4 (8k window): ~$0.08 input and ~$0.16 output per 1,000 tokens.
- GPT-4 Turbo (128k window): ~$0.01 input and ~$0.03 output per 1,000 tokens.
- Anthropic’s Claude:
- Claude Instant v1.2: ~$0.0008 input / ~$0.0024 output per 1k tokens.
- Claude 2 (100k window): ~$0.008 input / ~$0.024 output per 1k tokens.
These are USD prices, but the cost impact in pounds changes only with exchange rates.
Large-scale usage—say one million 1,000-token chats per day—could cost:
- ~$1 million/year for GPT-3.5.
- Over $4.6 million/year for GPT-4.
2. Self-Hosting Open Source Models
Going the self-hosted route—e.g., with Llama 2/3, Mistral—means the model is free, but you pay for compute infrastructure.
For example:
- Running a 7 B parameter model from the cloud (like a small Llama/Mistral) might cost ~$2–$3 per hour—translating to $1,440–$2,160/month.
- A larger 70 B model could reach
$38 per hour—**$27,000/month per instance**.
Additional anecdotal insights:
- GPU costs on Azure: ~$0.75/hour. Processing 700 tokens might cost $1.87 due to resource timing vs. just ~$1.125 for GPT-3.5 API usage.
- Running on bare metal or Hugging Face-backed hardware may amount to $5/hour or $120/day.
- Hosting an LLM can be up to 30× more expensive than using GPT via API under certain scales.
- Insight: Self-hosting became more viable as costs plunged dramatically. For instance, delivering the same performance as GPT-3 once cost $60 per million tokens—but now, cheaper models like Llama 3.2 3B do it at just $0.06 per million tokens—an 1000× cost reduction over three years.
3. Development & Deployment Costs in the UK
Study in UK If you’re building or fine-tuning models in-house:
- Developer salaries:
- Entry-level LLM engineers: £37,000–£39,000/year.
- Senior specialists: £80,000–£100,000/year.
- Project-level costs:
- A basic generative AI application: £50,000–£100,000.
- Supervised fine-tuning: £500–£2,000 depending on complexity.
- Proof-of-concept ML projects: £5,000–£8,000+.
- Large-scale or production-grade LLMs: £80,000–£150,000+.
- Full-day training services via UK government frameworks can range from £300 to £1,400 per day.
4. Training Costs for State-of-the-Art Models
Creating your own LLM from scratch is extraordinarily expensive:
- GPT-2 (1.5 B parameters): ~$50,000 to train.
- PaLM (540B parameters): ~$8 million.
- Megatron-Turing NLG (530B): ~$11 million.
- Plus, new research highlights that human labor behind training data—e.g., curating and labeling—is often 10× to 1,000× more costly than the actual compute training.
A broader study warns that by 2027, training frontier LLMs could cost over $1 billion, driven by surging hardware and operational costs.
5. Tailoring to UK Needs
Though most LLM services price in USD, in the UK context:
- API costs are felt as currency conversion—so you might pay ~0.80–0.85× the USD cost in GBP.
- Self-hosting costs (compute, electricity, datacentre fees) are generally higher than many US regions.
- Salaries in the UK market are competitive—with entry roles around £37k and senior talent up to £100k/year.
Summary Table
| Approach | Approximate Cost (UK context) |
| API (e.g., GPT-3.5/4) | From ~$0.0035 per 1,000 tokens (GPT-3.5) … up to ~$0.16 for GPT-4 |
| Self-hosting | ~$2–$3/hr for small; ~$38/hr for large model |
| Salaried Development | £37k–£100k/year per engineer; £50k–£150k+ for project |
| Training from scratch | $50k (small) to $11M+ (large) + huge data costs |
Final Thoughts
- Best for most use cases: API access to GPT or Anthropic gives flexibility and useful models at predictable, pay-as-you-go pricing.
- Cost-conscious scalable deployment: Self-hosted open-source models can become more affordable at scale—but only if infrastructure is managed efficiently.
- In-house model creation: Reserved for well-funded teams; training and data costs explode rapidly.
- UK-specific implications: Expect slightly higher operational costs, competitive talent salaries, and impact of exchange rates.
Let me know if you’d like a deeper dive—say, comparing GPT-4 vs Llama 3 in GBP terms, or exploring budget-friendly cloud setups for UK-based deployment.
