AI API Costs & Inference Optimisation: A Practical Guide to Controlling LLM Spending

Your AI prototype cost £3 to test. Your production system costs £3,000 a month. What happened?

Scale happened. And it catches nearly every business off guard.

As AI moves from experiments to production workloads, API costs become a real line item. The good news: with the right approach, you can cut inference spending by 60-80% without sacrificing quality. This guide shows you how.

The AI API Cost Landscape in 2026

The market has matured significantly. Here's what businesses actually pay across major providers:

Cost Per Million Tokens (February 2026)

Provider / Model	Input Cost	Output Cost	Best For
OpenAI GPT-4o	$2.50	$10.00	General enterprise tasks
OpenAI GPT-4o-mini	$0.15	$0.60	High-volume, simpler tasks
Anthropic Claude Sonnet	$3.00	$15.00	Complex reasoning, analysis
Anthropic Claude Haiku	$0.25	$1.25	Fast classification, routing
Google Gemini 2.0 Flash	$0.10	$0.40	Cost-sensitive bulk processing
DeepSeek V3	$0.14	$0.28	Budget-friendly alternative
Mistral Large	$2.00	$6.00	European data residency

The key insight: Output tokens cost 3-5x more than input tokens. Every unnecessary word your AI generates is money burned.

Where Businesses Waste Money

Before optimising, you need to know where the waste is. In our consultancy experience, these are the top five cost killers:

1. Using the Wrong Model for the Job

The most expensive mistake: routing every request through your most powerful (and costly) model.

Real example: A UK customer service team was sending all enquiries through Claude Opus at £15/million output tokens. 70% of those enquiries were simple FAQs that a £1.25/million-token model handles perfectly.

Fix: Implement a model router. Simple classification first, then route to the appropriate model:

• Tier 1 (cheap, fast): FAQs, simple lookups, classification, formatting
• Tier 2 (mid-range): Summarisation, standard analysis, content generation
• Tier 3 (premium): Complex reasoning, multi-step analysis, creative strategy

A well-designed router cuts costs by 40-60% with no user-visible quality drop.

2. Bloated System Prompts

Every API call includes your system prompt. If that prompt is 2,000 tokens and you make 10,000 calls per day, that's 20 million input tokens — just on instructions.

Fix:

• Trim system prompts ruthlessly. Every sentence should earn its place
• Use prompt caching (available on Anthropic, OpenAI, and Google APIs) — cached prompts cost 75-90% less on repeat calls
• Move reference data out of the prompt and into tool calls or retrieval systems

3. Not Caching Responses

If the same question gets asked repeatedly, you're paying for the same answer every time.

Fix: Implement semantic caching:

• Exact-match cache for identical queries
• Similarity-based cache for near-identical queries (embedding distance < threshold)
• Time-based expiry for data that changes
• A good cache layer with 30%+ hit rate pays for itself in days

4. Generating Too Much Output

"Please provide a comprehensive, detailed response covering all aspects..." — this instruction is a cost multiplier.

Fix:

• Set max_tokens appropriately for each task
• Ask for concise responses in your prompts
• Use structured output (JSON mode) — it's typically 40-60% fewer tokens than prose
• Post-process and summarise rather than asking the model to be exhaustive

5. Redundant Re-Processing

Processing the same document, email, or dataset multiple times because results aren't stored.

Fix: Process once, store the result. Use a simple key-value store:

• Document hash → extracted data
• Email ID → classification + summary
• Conversation ID → running context summary (instead of sending full history every turn)

The Model Selection Framework

Choosing the right model isn't just about cost — it's about cost per unit of useful output. Here's a practical framework:

Decision Matrix

Use the cheapest model that reliably passes your quality bar:

1. Define your quality threshold — what accuracy/quality is "good enough" for this task?
2. Test across 3-4 models with 50+ representative examples
3. Measure: accuracy, latency, cost per successful completion
4. Calculate cost-adjusted quality: quality score ÷ cost per 1,000 completions

Almost always, a mid-tier model wins. The premium models justify their cost only for genuinely complex reasoning tasks.

When to Use Premium Models

• Multi-step reasoning that cheaper models get wrong >10% of the time
• Tasks where errors are costly (legal analysis, financial calculations)
• Creative strategy where nuance matters
• Agentic workflows where the model needs to make autonomous decisions

When Cheap Models Excel

• Classification and routing (sentiment, intent, category)
• Data extraction from structured/semi-structured sources
• Formatting and transformation
• Simple Q&A from provided context
• Summarisation of straightforward content

Prompt Caching: The Easiest Win

If you're not using prompt caching, you're leaving money on the table. Here's the ROI:

How Prompt Caching Works

Most providers now cache your system prompt and any static prefix. On subsequent calls with the same prefix, you pay a fraction of the full price:

Provider	Cache Write Cost	Cache Read Cost	Savings
Anthropic	1.25x base	0.1x base	90% on reads
OpenAI	1x base	0.5x base	50% on reads
Google	Free	0.25x base	75% on reads

For a typical business making 5,000+ daily API calls with a consistent system prompt, caching saves 30-50% on input token costs alone.

Implementation Tips

• Keep your system prompt and static context at the start of the message
• Vary only the user-specific portion at the end
• Batch similar requests together to maximise cache hits
• Monitor your cache hit rate — aim for 60%+

When Self-Hosting Makes Sense

Running your own models (via Ollama, vLLM, or managed solutions) can slash costs for high-volume workloads. But it's not always the right call.

Self-Host When:

• Volume exceeds £2,000/month in API costs for tasks a smaller model handles
• Data privacy is non-negotiable (sensitive data that can't leave your infrastructure)
• Latency matters and you need sub-100ms responses
• Predictable costs are more important than peak capability

Stick With APIs When:

• Volume is low to moderate (<£1,000/month)
• You need frontier capability (best reasoning, most current knowledge)
• Your team lacks ML ops experience
• Requirements change frequently — swapping API models is trivial; redeploying self-hosted ones isn't

Self-Hosting Cost Comparison

A capable open-source model (Llama 3.3 70B or Mistral Medium) on a single A100 GPU:

Deployment	Monthly Cost	Tokens/Month	Effective Cost/1M Tokens
Cloud API (mid-tier)	£2,000	~600M	£3.33
Self-hosted (AWS g5.2xlarge)	£800	~2,000M	£0.40
Self-hosted (on-prem, amortised)	£400	~2,000M	£0.20

Break-even point: Around 500M tokens/month for cloud self-hosting, lower for on-premises if you already have hardware.

Building a Cost-Optimised AI Pipeline

Here's the architecture that delivers the best cost-to-quality ratio:

Layer 1: Cache Check

Before any API call, check if you've answered this before. Semantic similarity search on previous responses.

Layer 2: Model Router

Classify the request complexity. Route to the cheapest capable model.

Layer 3: Prompt Optimisation

• Minimal system prompt (cached)
• Structured output format
• Appropriate max_tokens limit

Layer 4: Response Processing

• Store results for future cache hits
• Log cost per request for monitoring
• Flag requests where cheaper models failed (for router retraining)

Layer 5: Monitoring & Alerts

• Daily/weekly cost dashboards
• Alert on cost spikes (>2x daily average)
• Track cost per business outcome, not just cost per token

Practical Cost Monitoring

You can't optimise what you don't measure. Set up these metrics from day one:

Essential Metrics

1. Cost per conversation/task — not just per API call
2. Model utilisation split — what percentage goes to each tier?
3. Cache hit rate — should trend upward over time
4. Cost per successful outcome — the metric that actually matters
5. Token efficiency — useful output tokens ÷ total tokens generated

Tools for Monitoring

• LangSmith / LangFuse — open-source LLM observability
• Helicone — proxy that logs every API call with costs
• Provider dashboards — OpenAI, Anthropic, and Google all provide usage analytics
• Custom logging — a simple database table tracking every call's model, tokens, cost, and outcome

Quick Wins: Reduce Costs This Week

If you're running AI in production today, here are five changes you can make immediately:

1. Enable prompt caching on your provider — usually a one-line configuration change
2. Audit your model usage — are expensive models handling simple tasks?
3. Set max_tokens on every API call — don't let the model ramble
4. Switch to structured output (JSON mode) for data extraction tasks
5. Add response caching for your top 20 most common queries

The Bottom Line

AI API costs are manageable — but only if you treat them like any other infrastructure cost. Monitor, optimise, and make conscious trade-offs between quality and spend.

The businesses that get AI costs right don't use the cheapest model for everything. They use the right model for each task, cache aggressively, and measure what matters: cost per business outcome, not cost per token.

Start with monitoring. You'll be surprised where the money goes. Then optimise systematically — model routing first, then caching, then prompt engineering. Most businesses can cut AI spending by half while maintaining or improving quality.

That's not just good engineering. It's good business.

---

Need help optimising your AI costs? Get in touch — we help UK businesses build cost-effective AI systems that scale without the bill scaling with them.