How to Cut AI API Costs With Caching and Routing

AI API costs can grow fast — especially when your application handles thousands of requests, uses large contexts, or calls expensive models for every query. Most applications can cut costs 50-80% without any quality loss.

The key techniques are caching (avoid repeat work), routing (use the cheapest model that handles each query well), and prompt optimisation (send fewer tokens). This guide covers all three with practical implementation patterns.

Quick answer

Cache responses for repeated or similar queries, route simple queries to cheaper models and complex ones to expensive models, optimise prompts to use fewer tokens, and batch similar requests. These techniques can reduce costs 50-80% with minimal quality impact.

• Your AI API bill is growing faster than your revenue.
• You are using an expensive model for every request regardless of complexity.
• Many of your queries are similar or identical.

Response caching

The simplest cost reduction: if the same question comes in again, return the cached response instead of calling the API.

Exact-match caching is easy to implement. Semantic caching (matching queries that are similar but not identical) is more complex but can catch more cache hits.

• Hash the prompt to create cache keys for exact matches
• Set TTL (time-to-live) based on how quickly your data changes
• For semantic caching, embed queries and match against cached query embeddings
• Cache at the application layer, not the API layer — you control the cache key

Model routing

Not every query needs your most expensive model. Simple questions ('What is X?'), formatting tasks, and classification can be handled by cheaper, faster models.

Build a router that classifies incoming queries and sends them to the appropriate model. Route 60-70% of queries to a cheap model and only the complex ones to the expensive model.

# Simple model router
def route_query(query: str) -> str:
    # Classify query complexity
    if len(query) < 100 and is_simple_question(query):
        return "haiku"  # cheap model
    elif requires_reasoning(query):
        return "opus"   # expensive model
    else:
        return "sonnet"  # mid-tier model

Prompt optimisation

Tokens cost money. Shorter prompts that produce the same output quality are free cost savings.

Review your prompts for unnecessary repetition, verbose instructions, and context that does not help the model. A prompt that went through three rounds of 'just add this too' is usually bloated.

• Remove redundant instructions — say it once, clearly
• Use system prompts for persistent instructions instead of repeating in every user message
• Trim context to only what is relevant — do not dump entire documents when a paragraph will do
• Measure: shorter prompt must produce same quality, otherwise the savings are false

Batching requests

If you process many independent items (documents, support tickets, products), batch them into a single API call with batch pricing. Most providers offer 50% discounts for batch processing.

Even without batch pricing, combining multiple items into a single prompt reduces per-request overhead and can be cheaper than individual calls.

Monitoring and optimising

Track cost per request, per feature, and per user. Know where your money goes before trying to reduce it.

Set up alerts for cost spikes — a bug that sends the full document instead of a summary can cost hundreds in a day.

Worked example: reducing costs for a content moderation system

A content moderation system processes 100K messages daily using an expensive model at $0.03 per message ($3,000/day). After: simple messages route to a cheap model ($0.001), exact duplicates hit cache (30% of volume), and prompts are optimised (20% fewer tokens). New cost: $600/day — an 80% reduction with the same accuracy.

Common mistakes

• Routing all queries to the cheapest model — quality drops and users notice.
• Not monitoring costs until the bill arrives.
• Optimising prompts without measuring quality impact.

When to use something else

For choosing between open models and API models overall, see choosing between open and API models. For running models locally to eliminate API costs entirely, see running Gemma 4 locally.

How to apply this in a real AI project

How to Cut AI API Costs With Caching and Routing becomes much more useful once it is tied to the rest of the workflow around it. In real work, the result depends on model selection, prompt design, tool integration, evaluation, and the operational reality of shipping AI features, not only on following one local tip correctly.

That is why the biggest win rarely comes from one clever move in isolation. It comes from making the surrounding process easier to review, easier to repeat, and easier to hand over when another person inherits the workbook or codebase later.

• Test with realistic inputs before shipping, not just the examples that inspired the idea.
• Keep the human review step visible so the workflow stays trustworthy as it scales.
• Measure what matters for your use case instead of relying on general benchmarks.

How to extend the workflow after this guide

Once the core technique works, the next leverage usually comes from standardising it. That might mean naming inputs more clearly, keeping one review checklist, or pairing this page with neighbouring guides so the process becomes repeatable rather than person-dependent.

The follow-on guides below are the most natural next steps from How to Cut AI API Costs With Caching and Routing. They help move the reader from one useful page into a stronger connected system.

• Go next to How to Choose Between Open Models and API Models if you want to deepen the surrounding workflow instead of treating How to Cut AI API Costs With Caching and Routing as an isolated trick.
• Go next to How to Run Gemma 4 on Your Own Machine if you want to deepen the surrounding workflow instead of treating How to Cut AI API Costs With Caching and Routing as an isolated trick.
• Go next to How to Use Background Jobs in AI Apps for Long Tasks if you want to deepen the surrounding workflow instead of treating How to Cut AI API Costs With Caching and Routing as an isolated trick.

Related guides on this site

These guides cover model selection, local alternatives, and production AI patterns.

• How to Choose Between Open Models and API Models
• How to Run Gemma 4 on Your Own Machine
• How to Use Background Jobs in AI Apps for Long Tasks
• How to Use Reasoning Summaries in Production AI Apps
• 5 Real-World Tasks Where Gemma 4 Beats Paid AI Models