AI Inference Economics: Understanding and Optimising Token Costs for Your Business

Your AI bill is probably higher than it needs to be. Not because AI is expensive — it's actually gotten remarkably cheap — but because most businesses deploy AI without understanding the economics of inference.

Every time your AI system processes a request, generates a response, or analyses a document, it's consuming tokens. Tokens cost money. And the difference between a naively deployed AI system and an optimised one can be 5-10x in cost for the same output quality.

For a UK business running AI across customer service, document processing, and internal operations, that's the difference between a £2,000/month AI bill and a £200/month one. Same results, dramatically different economics.

How AI Pricing Actually Works

Before optimising, you need to understand the pricing model. AI inference isn't like traditional software licensing — it's closer to a utility bill.

Tokens: The Unit of AI Commerce

Everything in AI is measured in tokens. A token is roughly 3/4 of a word in English, though it varies by language and content type. A typical business email is about 200-400 tokens. A full page of a contract might be 800-1,200 tokens.

There are two types of token charges:

• Input tokens: What you send to the model (your prompt, context, documents)
• Output tokens: What the model generates in response

Output tokens are typically 3-5x more expensive than input tokens. This is because generating text requires more computation than reading it — the model has to predict each token sequentially.

Current Pricing Landscape (Early 2026)

The AI pricing market has stratified into clear tiers:

Premium reasoning models (Claude Opus, GPT-4.5, Gemini Ultra): £5-15 per million input tokens, £15-75 per million output tokens. These are for tasks where quality matters more than cost — complex analysis, nuanced writing, difficult reasoning.

Standard models (Claude Sonnet, GPT-4o, Gemini Pro): £1-3 per million input tokens, £5-15 per million output tokens. The workhorses. Good enough for 80% of business tasks at a fraction of the premium cost.

Economy models (Claude Haiku, GPT-4o-mini, Gemini Flash): £0.10-0.50 per million input tokens, £0.50-2 per million output tokens. Fast, cheap, and surprisingly capable for classification, extraction, and simple generation tasks.

Open-source/self-hosted (Llama, Mistral, DeepSeek): Hardware cost only, but you're paying for GPUs instead of tokens. Makes sense at very high volume or when data must never leave your infrastructure.

The Hidden Costs

Token charges aren't the whole picture:

• API rate limits can force you to provision higher-tier plans than your average usage requires
• Fine-tuning costs for training custom models (if applicable)
• Storage for conversation histories, cached prompts, and embeddings
• Engineering time to build and maintain AI integrations
• Error costs — when the AI gets it wrong and a human has to fix it

Why Most Businesses Overspend on AI

The most common AI cost mistakes, based on what we see with UK businesses:

Using the Wrong Model for the Job

This is the single biggest waste. Businesses deploy their most expensive model for everything because "it's the best." But for many tasks, the premium model offers no measurable improvement over a model that costs 10x less.

Example: A UK recruitment firm was using GPT-4 to parse CVs and extract key information — name, experience, skills, education. They were spending £800/month on this. Switching to GPT-4o-mini for the same task produced identical results at £60/month. The extraction task was simple enough that the economy model handled it perfectly.

The principle: Match model capability to task difficulty. Use premium models only for tasks where they demonstrably outperform cheaper alternatives.

Sending Too Much Context

Every token in your prompt costs money. Many businesses include massive system prompts, entire document histories, or verbose instructions that could be compressed without losing effectiveness.

Example: A legal tech startup was sending their full 4,000-token system prompt with every customer query. Their system handled 50,000 queries/month. That's 200 million tokens/month just in repeated system prompt — roughly £600/month in input costs for context that could have been cached or reduced by 60%.

Not Caching Repeated Work

If the same or similar prompts are sent repeatedly, you're paying the model to do the same work over and over. Prompt caching — offered by most major providers now — can reduce costs by 50-90% for repeated context.

Common cacheable patterns:

• System prompts that don't change between requests
• Reference documents that multiple queries need to access
• Template instructions used across similar tasks
• Few-shot examples included in every prompt

Ignoring Batching

Real-time inference is expensive because you're paying for immediate availability. If your AI tasks don't need instant responses — overnight report generation, batch document processing, non-urgent analysis — batch APIs typically offer 50% discounts.

Over-Generating

If you need a 100-word summary, don't let the model generate 500 words and then truncate. Control output length through prompt engineering and max_tokens parameters. You're paying for every token generated, even if you throw it away.

A Practical Optimisation Framework

Here's how to systematically reduce your AI costs without sacrificing quality:

Step 1: Audit Your Current Usage

Before optimising, understand where the money goes. For each AI-powered process in your business, document:

• Which model is being used
• Average input tokens per request
• Average output tokens per request
• Number of requests per day/week/month
• Total cost per process
• Whether the task requires the current model's capability level

Most businesses have never done this audit. The numbers are usually surprising — a few processes dominate the spend while dozens of minor ones are negligible.

Step 2: Implement Model Routing

Not every request needs the same model. Build (or buy) a routing layer that directs requests to the appropriate model based on task complexity:

Tier 1 — Economy models (Haiku/Mini/Flash):

• Data extraction and formatting
• Simple classification (sentiment, category, priority)
• Template-based generation (standard emails, acknowledgments)
• Summarisation of structured data
• Translation of straightforward content

Tier 2 — Standard models (Sonnet/4o/Pro):

• Customer-facing content generation
• Document analysis requiring nuance
• Multi-step reasoning tasks
• Creative content with quality requirements
• Technical writing and documentation

Tier 3 — Premium models (Opus/4.5/Ultra):

• Complex legal or financial analysis
• Strategic planning assistance
• Handling ambiguous or conflicting information
• Tasks where errors have significant consequences
• Novel problem-solving without clear templates

A well-implemented routing system can reduce costs by 60-80% while maintaining output quality. The trick is testing thoroughly — run each task type through all three tiers and measure quality differences objectively.

Step 3: Optimise Your Prompts

Prompt engineering isn't just about getting better outputs — it's about getting the same outputs with fewer tokens:

Compress system prompts. Remove redundant instructions, merge similar rules, and eliminate hedging language. "You are an assistant that helps with customer service inquiries. You should always be polite and professional. You should provide accurate information." becomes "Customer service assistant. Be polite, accurate, professional."

Use structured outputs. When you need data extraction, ask for JSON or structured formats rather than prose. Structured outputs are typically 30-50% fewer tokens than natural language for the same information content.

Minimise few-shot examples. If you're including 5 examples in your prompt, test whether 2-3 produce equivalent results. Each example costs tokens on every single request.

Cache effectively. Use your provider's prompt caching features for static context. Anthropic, OpenAI, and Google all offer caching mechanisms that dramatically reduce costs for repeated context.

Step 4: Batch Non-Urgent Work

Identify AI tasks that don't need real-time responses:

• End-of-day report generation
• Weekly analytics summarisation
• Batch document processing
• Training data preparation
• Content scheduling and generation

Move these to batch APIs. Most providers offer batch processing at 50% of real-time prices. For a business processing 1,000 documents per week, this alone can save £500+ monthly.

Step 5: Monitor and Iterate

AI costs aren't set-and-forget. Models change, pricing changes, your usage patterns change. Set up monitoring for:

• Cost per task type — Are any processes getting more expensive over time?
• Quality per model tier — Has a cheaper model improved enough to handle tasks currently sent to expensive ones?
• Usage anomalies — Sudden spikes often indicate bugs, not genuine demand
• Provider price changes — New model releases frequently reset the price-performance curve

Real-World Cost Scenarios

Scenario 1: Customer Service AI

A UK e-commerce business handling 5,000 customer queries/day via AI:

Unoptimised: All queries through Claude Sonnet, average 500 input / 300 output tokens

• Cost: 5,000 × (500 × £3/M + 300 × £15/M) = £30/day = £900/month

Optimised: Route by complexity — 70% Haiku, 25% Sonnet, 5% Opus, with prompt caching

• Haiku: 3,500 × (300 × £0.25/M + 200 × £1.25/M) = £1.14/day
• Sonnet: 1,250 × (500 × £3/M + 300 × £15/M) = £7.50/day
• Opus: 250 × (800 × £15/M + 500 × £75/M) = £12.38/day
• Total: ~£21/day = £630/month (30% savings, better quality on complex issues)

With caching applied to the 2,000-token system prompt across all requests: additional 40% reduction on input costs.

Scenario 2: Document Processing

A legal firm processing 200 contracts/week, each averaging 8,000 tokens:

Unoptimised: Full contract through GPT-4.5 for complete analysis

• Cost: 200 × (8,000 × £10/M + 2,000 × £30/M) = £28/week = £120/month

Optimised: Two-stage pipeline — Haiku extracts key clauses, Sonnet analyses only flagged sections

• Stage 1 (Haiku, full doc): 200 × (8,000 × £0.25/M + 500 × £1.25/M) = £0.53/week
• Stage 2 (Sonnet, flagged sections ~2,000 tokens): 200 × (2,000 × £3/M + 1,000 × £15/M) = £4.20/week
• Total: ~£5/week = £20/month (83% savings)

Scenario 3: Internal Knowledge Base

A mid-sized UK business with 500 employees querying an AI-powered knowledge base 2,000 times/week:

Unoptimised: Each query includes full context retrieval (5,000 tokens of RAG context) through a standard model

• Cost: 2,000 × (5,000 × £3/M + 500 × £15/M) = £45/week = £195/month

Optimised: Semantic caching for common queries (40% hit rate), compressed context, model routing

• Cached (800 queries): ~£0 (served from cache)
• Simple (840 queries via Haiku): 840 × (3,000 × £0.25/M + 300 × £1.25/M) = £0.95/week
• Complex (360 queries via Sonnet): 360 × (5,000 × £3/M + 500 × £15/M) = £8.10/week
• Total: ~£9/week = £39/month (80% savings)

The Self-Hosted Question

At some point, growing AI usage makes self-hosting worth evaluating. The crossover point depends on your volume, data sensitivity, and technical capability.

Self-hosting makes sense when:

• Monthly API spend exceeds £5,000-10,000
• Data sensitivity prohibits cloud processing
• You need customised models (fine-tuned for your domain)
• Latency requirements demand local inference
• You have (or can hire) ML infrastructure expertise

Self-hosting doesn't make sense when:

• Volume is moderate and growing slowly
• You need access to frontier model capabilities
• Your team lacks GPU infrastructure experience
• The compliance burden of managing AI infrastructure outweighs API risks

The open-source model landscape — Llama, Mistral, DeepSeek, and others — has made self-hosting dramatically more accessible. A single high-end GPU can serve an economy-tier model for many business tasks. But the operational overhead of managing inference infrastructure is real and shouldn't be underestimated.

Building a Cost-Conscious AI Culture

The most effective cost optimisation isn't technical — it's cultural. Your team needs to understand that AI calls have costs, just like any other business resource.

Make costs visible. Dashboard showing AI spend by team, by task, by day. When people can see the numbers, behaviour changes.

Set budgets. Give each department or project an AI budget. Let them optimise within it rather than treating AI as a free resource.

Reward efficiency. Recognise teams that achieve the same outcomes at lower cost, not just teams that adopt more AI.

Review regularly. Monthly AI cost reviews, same as you'd review any significant operational expense. Identify what's growing, what's worth it, and what should be optimised.

What's Ahead

AI inference costs are dropping roughly 10x every 18-24 months. Tasks that cost £1 today will cost £0.10 in two years. This doesn't mean you should ignore optimisation — it means the volume of AI usage will grow to fill any cost reduction. Businesses that build cost-conscious AI systems now will scale more efficiently as prices drop and usage explodes.

The businesses that treat AI inference as an engineering discipline — measuring, optimising, routing, caching — will spend a fraction of what their competitors do while achieving equivalent or better results. In a market where everyone has access to the same AI models, cost efficiency becomes a genuine competitive advantage.

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Want to optimise your AI costs? Caversham Digital helps UK businesses audit their AI spend and implement cost-efficient architectures — from model routing to prompt optimisation. Talk to us.