LLM Cost Optimization: Keeping AI Features Affordable at Scale
Learn practical strategies for reducing AI costs without sacrificing quality, from caching to model routing to prompt optimization.
November 26, 2024 7 min read
AI features that cost $50 per month in development can cost $5,000 per month in production. The scaling isn't linear—it's often exponential as you add users, increase usage, and discover edge cases that multiply API calls.
Most teams don't think about LLM costs until they see their first real bill. By then, the architecture decisions are baked in and fixing them requires significant rework.
This guide covers the strategies that actually move costs without destroying quality. Some are easy wins. Others require architectural changes. All of them matter if you're planning to run AI features at scale. Before optimizing costs, ensure you're not overengineering with AI.
Understanding Where Costs Come From
Before optimizing, understand the cost structure.
The Token Math
LLM pricing is per-token, with input and output priced separately:
Output tokens typically cost 2-5x more than input tokens. A 100-token response costs as much as a 200-500 token prompt.
Where Tokens Hide
Your prompt isn't just the user's message. It includes:
System prompt: Often 500-2000 tokens of instructions, persona, and constraints
Conversation history: Every previous message in the conversation
Retrieved context: RAG chunks, user data, reference information
Few-shot examples: Examples showing the desired output format
A user sending "What's the status of my order?" might trigger an API call with 3,000 input tokens once you add context.
Each factor multiplies the others. Cutting any one by half cuts total cost by half. Small improvements compound.
Strategy 1: Choose the Right Model for the Task
The most impactful cost lever is model selection. GPT-4 costs 20x what GPT-3.5 costs. Use the expensive model only when it's needed.
Task-Based Model Routing
Different tasks need different capabilities. For a detailed comparison of when to use each approach, see our chatbot build vs. buy vs. skip guide:
Implement model routing in your application:
Quality-Aware Routing
Some requests need higher quality than others:
User on free tier? Route to cheaper model.
Complex query? Upgrade to expensive model.
Retry after failure? Use better model for the retry.
Track quality metrics per model per task. If the cheap model achieves 95% accuracy on classification, the 20x cost of GPT-4 for the remaining 5% probably isn't worth it.
Cascade Pattern
Try the cheap model first. Escalate if needed:
If 80% of requests are handled by the cheap model, you've cut costs by ~75% while maintaining quality for the cases that need it.
Strategy 2: Cache Aggressively
Identical prompts produce identical results (or close enough). Caching is the easiest win.
Exact Match Caching
Hash the prompt. If you've seen it before, return the cached response:
Common questions get asked repeatedly. Documentation queries, FAQ-type questions, standard workflows—these hit the cache frequently.
Semantic Caching
For slightly different prompts with the same intent, semantic similarity can identify cache hits:
"What's the return policy?" and "How do I return something?" are semantically similar and could share a cached response.
Trade-off: Semantic caching adds an embedding call per request. That's cheap, but not free. The cache hit rate needs to justify it.
Cache Invalidation
LLM caches can be more aggressive than typical application caches:
Documentation queries? Cache for 24 hours or until docs update.
User-specific data? Shorter TTL or invalidate on data change.
Conversation context? Cache within session only.
Track your cache hit rates. If they're under 20%, caching overhead may not be worth it for your use case.
Strategy 3: Reduce Prompt Size
Every token in your prompt costs money. Cut ruthlessly.
Compress System Prompts
System prompts grow over time as teams add instructions, examples, and edge case handling. Audit them regularly:
Before (847 tokens):
After (156 tokens):
The shorter version conveys the same constraints. Test that quality doesn't degrade with compression.
Limit Conversation History
Including full conversation history for a long session creates ballooning costs:
Turn 1: 500 tokens
Turn 5: 2,500 tokens
Turn 20: 10,000 tokens
Strategies:
Sliding window: Include only the last N messages
Summarization: Periodically summarize history into a compact summary
Relevance filtering: Include only messages relevant to the current query
Reduce RAG Context
RAG pipelines often retrieve more context than needed:
Reduce K: Retrieve 3 chunks instead of 10
Compress chunks: Summarize retrieved content before including it
Selective inclusion: Only include chunks above a relevance threshold
Track which retrieved chunks actually influence the response. Often, the top 2-3 chunks provide 90% of the value.
Trim Output
Request shorter outputs:
Specify output format to avoid verbose preamble:
Strategy 4: Batch and Debounce
Request overhead matters. Fewer, larger requests are more efficient than many small ones.
Batch Similar Operations
Instead of 10 requests to classify 10 items:
Most LLMs handle batched requests in a single call. You pay for one set of system prompt tokens instead of ten.
Debounce User Input
Real-time features (autocomplete, live suggestions) can generate many requests per second:
Users type faster than you need to respond. Debouncing reduces requests by 5-10x for real-time features.
Queue Non-Urgent Work
Not everything needs immediate processing:
Queued work can be processed during off-peak hours when rate limits are less constrained and you can batch more aggressively.
Strategy 5: Build Fallbacks for Cost Overruns
Even with optimization, usage spikes happen. Build protection:
Cost Budgets and Alerts
Set per-user and system-wide cost limits:
Graceful Degradation
When costs spike, degrade gracefully:
Switch to cheaper models when near limits
Reduce output length under load
Disable non-essential AI features
Fall back to cached or heuristic responses
Circuit Breakers
If something goes wrong (runaway costs, API errors, degraded quality), stop automatically:
A circuit breaker prevents runaway costs from a bug or attack.
Strategy 6: Monitor and Measure
You can't optimize what you don't measure.
Track Cost per Feature
Not just total cost—cost by feature:
This reveals which features are expensive and where to focus optimization.
Track Cost per User Segment
Free users? Premium users? Enterprise? Each segment has different cost economics:
Free: Must be very cheap; loss leader
Premium: Can spend more; LTV justifies it
Enterprise: Custom limits; negotiated
Build Dashboards
Visualize:
Daily/weekly/monthly cost trends
Cost per user
Cost per feature
Model usage distribution
Cache hit rates
Anomalies should be visible immediately, not discovered in the monthly bill.
Putting It Together
A typical optimization roadmap:
Week 1: Measure baseline
Instrument all LLM calls with cost tracking
Establish per-feature and per-user cost baselines
Identify top 3 cost drivers
Week 2: Quick wins
Implement exact-match caching
Compress system prompts
Add debouncing to real-time features
Week 3: Model routing
Classify tasks by complexity required
Implement model selection logic
A/B test quality impact
Week 4: Architectural changes
Reduce RAG context if applicable
Implement conversation summarization
Add cost budgets and alerts
Ongoing: Monitor and iterate
Weekly cost review
Continuous optimization of high-cost features
Regular prompt compression audits
Common Mistakes
Optimizing Before Measuring
Teams implement caching without knowing their cache hit rate potential. They compress prompts without testing quality impact. Measure first, then optimize what matters.
Over-Optimizing Low-Volume Features
A feature used 100 times daily isn't worth weeks of optimization. Focus on the 80% of cost, not the 80% of features.
Sacrificing Quality for Cost
If cost optimization destroys the user experience, you've saved money on a feature people stop using. Track quality metrics alongside cost metrics.
Ignoring Output Costs
Output tokens cost 3-5x input tokens. A verbose response doubles cost compared to a concise one. Prompt for brevity.
Single-Provider Dependency
Provider outages happen. Costs change. Having fallback providers gives you negotiating leverage and resilience. For more on provider comparison, see our post on OpenAI vs. Anthropic vs. open source.
Key Takeaways
LLM costs at scale require active management. The strategies that matter:
Route by task complexity. Use expensive models only where they're needed.
Cache aggressively. Identical prompts don't need repeated API calls.
Compress prompts. Every token costs; cut ruthlessly.
Batch and debounce. Fewer, larger requests are more efficient.
Build cost guardrails. Budgets, alerts, and circuit breakers prevent surprises.
Measure per feature. Know where costs come from before optimizing.
The goal isn't minimum cost—it's appropriate cost. Pay for the AI quality your product needs, and not more.
Looking to add AI features without blowing your infrastructure budget? At NextBuild, we architect AI integrations with cost efficiency built in from day one. Let's discuss your project.
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async function semanticCachedGenerate(prompt: string): Promise<string> { const embedding = await getEmbedding(prompt); const similar = await cache.findSimilar(embedding, threshold: 0.95); if (similar) { return similar.response; } const result = await generate(prompt); await cache.set(embedding, result); return result;}
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You are Acme Corp's support assistant. Answer customer questions usingprovided documentation. Be helpful and accurate. If uncertain, say so.Never invent information.
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Answer in 2-3 sentences maximum. Be concise.
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// Expensive: 10 API callsfor (const item of items) { const category = await classify(item);}// Better: 1 API callconst categories = await classifyBatch(items);
