AI Cost Optimization Checklist: Cut Your AI Spend by 30-70%

Why AI costs spiral (and how to fix it)

The harsh reality: Most production AI systems waste 40-70% of their budget on inefficient prompts, wrong model choices, and lack of caching. Teams launch with expensive models (GPT-4, Claude Opus) for everything, send bloated prompts, and don't monitor spending until the $50K/month bill arrives.

The good news: With systematic optimization, you can cut costs by 30-70% without sacrificing quality. This checklist walks you through proven techniques used by engineering teams at scale.

Who this is for:

• Engineering teams running AI in production
• CTOs managing AI infrastructure budgets
• DevOps/platform engineers optimizing LLM costs
• Product managers balancing quality vs. cost
• Startups scaling AI features without blowing the budget

What you'll learn:

• How to identify your biggest cost drivers (90% of spend comes from 10% of use cases)
• Quick wins that deliver 30-50% savings in under a week
• Advanced techniques for 70%+ cost reduction
• When to optimize vs. when to scale up
• Real case studies with actual numbers

Understanding your AI cost structure

Before optimizing, you need to know where money goes. AI costs break down into four categories:

1. API call costs (60-80% of total spend)

Input tokens: Your prompts, context, and user messages

• Typical range: $0.50-$15 per 1M input tokens
• Example: GPT-4 Turbo costs $10/1M input tokens

Output tokens: AI-generated responses

• Typical range: $1.50-$75 per 1M output tokens
• Example: Claude Opus costs $75/1M output tokens (5x input cost)

Why output tokens matter more: Output is 2-5x more expensive than input. A chatbot generating 500-token responses pays more for output than input.

2. Infrastructure costs (10-20% of spend)

• Vector database hosting (Pinecone, Weaviate, Qdrant)
• Compute for embeddings generation
• Redis/caching infrastructure
• Monitoring and observability tools
• Load balancers and edge infrastructure

3. Development time (10-15% of total cost)

• Engineer time building and optimizing
• Experimentation and A/B testing
• Monitoring dashboard setup
• Incident response for cost spikes

4. Hidden costs (5-10%)

• Failed API calls (errors still cost money)
• Retry logic (can double costs if not optimized)
• Unused cached responses
• Over-provisioned infrastructure

Action item: Log your API calls for 1 week and categorize spending. You'll likely find 80% of costs come from 20% of endpoints.

The 4 Pillars of AI Cost Optimization

Pillar 1: Prompt & Request Optimization

Expected savings: 20-40% reduction
Difficulty: Easy to Medium
Implementation time: 1-2 weeks

Checklist: Prompt Optimization

Easy wins (1-3 days):

• Audit prompt length — Measure tokens in all prompts. Target: <500 tokens for simple tasks, <2000 for complex.
  Expected savings: 15-30%
  Tool: tiktoken (Python), js-tiktoken (JS)

• Remove verbose instructions — Cut "please", "I would like you to", filler words.
  Example: "Please summarize this article for me" → "Summarize:"
  Expected savings: 10-20% on input costs

• Use structured output — JSON mode uses fewer tokens than verbose prose.
  Expected savings: 5-15%
  Implementation: OpenAI JSON mode, Anthropic tool use

• Compress system prompts — Remove examples that don't improve quality.
  Test: A/B test shortened prompts, measure quality vs. cost
  Expected savings: 10-25%

• Limit output length — Set max_tokens aggressively.
  Example: Summaries max 150 tokens, chat max 300 tokens
  Expected savings: 20-40% on output costs

Medium wins (3-7 days):

• Use prompt templates — Store reusable templates, inject variables.
  Prevents bloat from copy-paste prompts

• Remove redundant context — Don't send full conversation history every time.
  Example: Summarize history every 5 turns
  Expected savings: 30-50% on multi-turn chats

• Optimize few-shot examples — Test 1 vs. 3 vs. 5 examples.
  Finding: Most tasks work fine with 1-2 examples
  Expected savings: 10-30%

• Use retrieval, not full documents — Send only relevant chunks (see RAG).
  Expected savings: 60-90% on document Q&A

• Batch similar requests — Combine multiple tasks in one prompt.
  Example: "Summarize these 5 articles:" vs. 5 separate calls
  Expected savings: 40-60% on batch jobs

Hard wins (1-2 weeks):

• Dynamic prompt selection — Use short prompts for simple queries, long for complex.
  Implementation: Classify query complexity, route to appropriate prompt
  Expected savings: 20-40%

• Prompt compression algorithms — Use techniques like LLMLingua.
  Expected savings: 40-60% on long prompts
  Trade-off: Slight quality degradation (test thoroughly)

• Context pruning — Remove least-relevant chunks from RAG results.
  Implementation: Rerank, keep top 3-5 chunks
  Expected savings: 30-50%

• Semantic deduplication — Don't send similar chunks twice.
  Expected savings: 10-20%

Pillar 2: Model Selection &amp; Switching

Expected savings: 40-80% reduction
Difficulty: Medium to Hard
Implementation time: 1-3 weeks

Checklist: Model Optimization

Easy wins (1-3 days):

• Audit current model usage — What % of requests use GPT-4 vs. GPT-3.5?
  Finding: Most teams over-use expensive models

• Switch simple tasks to cheaper models — FAQs, summaries, simple Q&A.
  GPT-4 → GPT-3.5: 95% cost reduction
  Claude Opus → Haiku: 98% cost reduction

• Use embeddings for search — Ada embeddings cost $0.10/1M tokens.
  Replace GPT-3.5 search with embeddings: 80% savings

• Test quality threshold — Can 80% of tasks use cheaper models?
  Method: A/B test, measure user satisfaction

• Use OpenAI Batch API — 50% discount for non-urgent tasks.
  Use case: Nightly data processing, bulk content generation

Medium wins (3-7 days):

• Implement model routing — Route by query complexity.
  Simple queries → Haiku, complex → Opus
  Expected savings: 40-60%

• Use task-specific models — Embeddings for search, GPT-3.5 for chat, GPT-4 for analysis.
  Expected savings: 50-70%

• Test regional models — Some regions have cheaper pricing.
  Check Azure OpenAI regional pricing

• Evaluate open-source alternatives — Llama 3, Mistral for self-hosting.
  Trade-off: Infrastructure complexity vs. API savings
  Cost crossover: ~50k requests/day

• Use smaller context windows — GPT-4 Turbo 128k vs. 8k costs the same per token, but longer prompts cost more.
  Action: Keep prompts under 4k tokens

Hard wins (1-3 weeks):

• Fine-tune smaller models — Fine-tuned GPT-3.5 can match GPT-4 for specific tasks.
  Cost: $8-20 training, but 95% savings per request
  Use case: Customer support, classification

• Cascade models — Try cheap model first, escalate if confidence is low.
  Example: Haiku → Sonnet → Opus based on uncertainty
  Expected savings: 50-70%

• Distill knowledge — Use GPT-4 to generate training data, fine-tune GPT-3.5.
  Expected savings: 80-90% long-term

• Self-host for high volume — If >100k requests/day, consider self-hosting.
  Savings: 60-80% at scale
  Cost: DevOps time, GPU infrastructure

• Hybrid cloud + self-hosted — Use cloud APIs for spikes, self-hosted for baseline.
  Expected savings: 40-60%

Pillar 3: Caching &amp; Infrastructure

Expected savings: 30-70% reduction
Difficulty: Easy to Hard
Implementation time: 3 days to 3 weeks

Checklist: Caching Strategy

Easy wins (1-3 days):

• Implement semantic caching — Cache by meaning, not exact text.
  Tool: GPTCache, Redis with embeddings
  Expected savings: 40-70% on repetitive queries

• Cache common FAQs — Pre-generate answers to top 50 questions.
  Cost: Zero for cached responses
  Expected savings: 30-60% for support bots

• Set cache TTL aggressively — Keep responses for 24h-7d.
  Balance: Freshness vs. cost

• Log cache hit rate — Target: >40% hit rate.
  If <40%, expand cache or adjust TTL

• Pre-warm cache — Generate responses for predictable queries.
  Use case: Morning briefings, scheduled reports

Medium wins (3-7 days):

• Implement prompt caching (Anthropic) — Reuse prompt prefixes.
  Savings: 90% on repeated system prompts
  Limitation: Only available on Claude

• Use CDN for static responses — Cache unchanging content at edge.
  Expected savings: 50-80% on help docs, FAQs

• Compress cached responses — Gzip/Brotli saves storage costs.
  Savings: 60-80% on storage

• Deduplicate embeddings — Cache embeddings for repeated documents.
  Expected savings: 70-90% on embedding costs

• Cache RAG retrieval results — Store top chunks for common queries.
  Expected savings: 40-60%

Hard wins (1-3 weeks):

• Build multi-tier cache — Memory → Redis → DB → API.
  L1 (memory): <1ms, free
  L2 (Redis): <10ms, cheap
  L3 (DB): <100ms, moderate*
  *API: >500ms, expensive

• Implement cache warming pipeline — Predict queries, pre-generate responses.
  Use case: News summaries, trending topics

• Use partial caching — Cache prompt prefix, generate only variable parts.
  Example: System prompt cached, user query fresh
  Expected savings: 50-70%

• Cache intermediate results — Store RAG chunks, embeddings, retrievals separately.
  Expected savings: 30-50%

Checklist: Infrastructure

Easy wins (1-3 days):

• Right-size vector DB — Don't over-provision.
  Pinecone: Scale down during low traffic

• Use serverless where possible — Pay per request, not per hour.
  Tools: AWS Lambda, Cloudflare Workers

• Enable API request queuing — Batch requests during peak pricing.
  Savings: 10-20% if provider has time-based pricing

Medium wins (3-7 days):

• Implement rate limiting — Prevent cost spikes from abuse.
  Tool: Redis rate limiter, API gateway limits

• Use connection pooling — Reuse API connections.
  Savings: 5-10% on overhead

• Compress API payloads — Gzip requests/responses.
  Savings: 10-20% on bandwidth

Hard wins (1-3 weeks):

• Build edge inference — Run small models on Cloudflare Workers.
  Use case: Simple classification, routing
  Savings: 80-95% vs. API calls

• Use speculative decoding — Generate multiple responses in parallel.
  Trade-off: Complexity vs. latency

Pillar 4: Monitoring &amp; Governance

Expected savings: 20-40% reduction
Difficulty: Easy to Medium
Implementation time: 3-7 days

Checklist: Monitoring Setup

Easy wins (1-3 days):

• Log every API call — Track: timestamp, model, tokens, cost, latency, user, endpoint.
  Tool: Custom DB table, Datadog, PostHog

• Set up daily cost alerts — Email if spend >$X.
  Example: Alert if >$500/day

• Track cost per endpoint — Identify expensive endpoints.
  Find outliers: Why does /summarize cost 10x /chat?

• Monitor token usage trends — Track daily/weekly token volume.
  Catch spikes early

• Set up error rate monitoring — Failed calls still cost money.
  Target: <1% error rate

Medium wins (3-7 days):

• Build cost dashboard — Real-time spend by model, endpoint, user.
  Tool: Grafana, Datadog, custom dashboard

• Track cost per feature — Allocate spend to product features.
  Find ROI: Is AI chat worth the cost?

• Monitor cache hit rate — Target: >40%.
  Low hit rate = wasted cache infrastructure

• Track P95 latency — Slow requests often cost more (retries, timeouts).
  Target: <3s for chat, <500ms for search

• Set per-user spending caps — Prevent abuse.
  Example: Max $10/user/month

Checklist: Governance

Easy wins (1-3 days):

• Require cost estimates for new features — No AI feature without budget plan.

• Set model approval process — Default to GPT-3.5, require justification for GPT-4.

• Document optimization decisions — Track why you chose specific models/prompts.

Medium wins (3-7 days):

• Run weekly cost reviews — Team discusses top spenders.

• Set team budgets — Each squad owns their AI spend.

• Implement chargeback — Bill internal teams for AI usage.
  Creates cost awareness

Hard wins (1-2 weeks):

• Build cost attribution system — Tag requests by feature, team, user.
  Enables granular optimization

• Create cost SLOs — Target: <$0.01 per chat, <$0.001 per search.
  Alert when exceeded

Quick Wins Checklist: 30-50% Savings in 1 Week

Start here for immediate impact. These 10 items deliver outsized ROI with minimal effort:

Day 1: Measurement

• Set up API call logging — You can't optimize what you don't measure.
• Calculate current cost per request — Baseline for improvement.

Day 2: Low-hanging fruit

• Switch 50% of requests to GPT-3.5 — Identify simple tasks, test quality.
  Expected savings: 40-50%

• Set max_tokens=300 for chat — Prevent long responses.
  Expected savings: 20-30%

• Compress system prompts — Remove verbose instructions.
  Expected savings: 10-20%

Day 3-4: Caching

• Implement semantic caching — Use GPTCache or Redis + embeddings.
  Expected savings: 30-60%

• Pre-generate FAQ responses — Cache top 20 questions.
  Expected savings: 10-30%

Day 5-7: Monitoring

• Set daily spending alerts — Catch spikes early.

• Build cost dashboard — Track spend by endpoint.

• Run A/B test — Compare GPT-4 vs. GPT-3.5 quality.
  Action: Switch more traffic to cheaper model

Total expected savings after 1 week: 30-50%

Cost Optimization by Use Case

Chatbots &amp; Customer Service

Typical spend: $1,000-10,000/month
Optimization potential: 60-80% reduction

Key strategies:

• Cache top 50 FAQs (40-60% of queries)
• Use GPT-3.5 for simple queries, GPT-4 for escalations
• Limit conversation history to last 5 turns
• Pre-generate responses for common issues
• Use semantic routing to cheaper models

Example:
Before: 10k queries/day, GPT-4, $140/day
After: 60% cached, 30% GPT-3.5, 10% GPT-4, $25/day
Savings: $115/day = $3,450/month (82%)

Content Generation

Typical spend: $2,000-20,000/month
Optimization potential: 50-70% reduction

Key strategies:

• Use GPT-3.5 for drafts, GPT-4 for final polish
• Batch article generation (combine multiple requests)
• Set aggressive max_tokens (200-500 per section)
• Use templates to reduce prompt size
• Fine-tune GPT-3.5 for brand voice

Example:
Before: 1,000 articles/day, GPT-4, 800 tokens avg, $240/day
After: GPT-3.5 + templates + max_tokens=500, $35/day
Savings: $205/day = $6,150/month (85%)

Data Analysis &amp; Summarization

Typical spend: $500-5,000/month
Optimization potential: 70-90% reduction

Key strategies:

• Use RAG to extract relevant chunks (not full documents)
• Implement hierarchical summarization (summarize chunks, then summaries)
• Use embeddings for initial filtering
• Cache summaries for recurring reports
• Use Claude Haiku (fast, cheap, good at summarization)

Example:
Before: 1,000 docs/day, 5k tokens each, GPT-3.5, $37.50/day
After: RAG (500 tokens) + Haiku + caching, $4/day
Savings: $33.50/day = $1,005/month (89%)

Code Generation &amp; Review

Typical spend: $1,000-8,000/month
Optimization potential: 40-60% reduction

Key strategies:

• Use GPT-4 only for complex logic, GPT-3.5 for boilerplate
• Limit code context to relevant files (not entire repo)
• Cache common code patterns (auth, CRUD, API routes)
• Use streaming to reduce perceived latency
• Fine-tune on internal codebase for style

Example:
Before: 2,000 requests/day, GPT-4, avg 1,500 tokens, $150/day
After: 70% GPT-3.5, context pruning, caching, $60/day
Savings: $90/day = $2,700/month (60%)

Internal Tools (Slack bots, search, etc.)

Typical spend: $200-2,000/month
Optimization potential: 70-90% reduction

Key strategies:

• Aggressive caching (internal queries are repetitive)
• Use embeddings for search (not LLM calls)
• Pre-generate answers to common questions
• Use smallest viable model (Haiku, GPT-3.5)
• Implement rate limiting per user

Example:
Before: 5,000 queries/day, GPT-3.5, $35/day
After: 70% cached, embeddings for search, Haiku, $5/day
Savings: $30/day = $900/month (86%)

ROI Calculator: Prioritizing Optimizations

Not all optimizations are worth the effort. Use this framework to prioritize:

Calculate ROI per optimization

Formula:

ROI = (Monthly Savings × 12) / (Implementation Time × Hourly Rate)

Example:

• Optimization: Switch 50% of traffic to GPT-3.5
• Monthly savings: $2,000
• Implementation time: 8 hours
• Engineer rate: $100/hour
• ROI: ($2,000 × 12) / (8 × $100) = 30x

Prioritization matrix

Rule of thumb: Prioritize optimizations with ROI >10x first.

Break-even analysis

When is optimization worth it?

Simple rule:
If monthly savings > (implementation hours × hourly rate) / 3, it's worth doing.

Example:

• Optimization saves $500/month
• Takes 20 hours to implement
• Engineer costs $100/hour
• Break-even: $500 > ($2,000 / 3) = $500 > $667 ❌ Not worth it

But:

• Optimization saves $1,500/month
• Takes 20 hours
• Break-even: $1,500 > $667 ✅ Worth it (pays for itself in 1.3 months)

Cost Monitoring Setup: What to Track

Essential metrics

1. Total daily/monthly spend

• Why: Budget control, detect spikes
• Alert: >20% week-over-week increase

2. Cost per request

• Why: Identify inefficient endpoints
• Alert: >$0.05 per request (adjust for your use case)

3. Tokens per request (input + output)

• Why: Catch prompt bloat
• Alert: Input >2,000 tokens, output >500 tokens

4. Model usage distribution

• Why: Track expensive model usage
• Target: <20% GPT-4, >60% GPT-3.5, >20% cached

5. Cache hit rate

• Why: Optimize caching strategy
• Target: >40% hit rate

6. Error rate

• Why: Failed calls cost money + retry costs
• Target: <1% error rate

7. P95 latency

• Why: Slow requests often timeout and retry
• Target: <3s for chat, <500ms for search

Nice-to-have metrics

• Cost per user/session
• Cost per feature/product
• Cost by model/provider
• Token efficiency (output tokens / input tokens)
• Retry rate and cost
• Caching savings (theoretical cost without cache)

Monitoring tools

Free/cheap:

• OpenAI usage dashboard (basic)
• Custom logging to DB + spreadsheet
• Grafana + Prometheus (self-hosted)

Paid (comprehensive):

• Datadog ($15-50/month)
• Helicone (LLM-specific, $0-100/month)
• LangSmith ($0-99/month)
• Weights & Biases (complex)

Alert setup

Critical alerts (page immediately):

• Daily spend >2x normal
• Error rate >5%
• P95 latency >10s

Warning alerts (email):

• Daily spend >1.5x normal
• Cache hit rate <30%
• Cost per request >$0.10

Info alerts (weekly digest):

• Weekly spend summary
• Top 10 expensive endpoints
• Optimization opportunities

Case Studies: Real Cost Reductions

Case Study 1: SaaS Chatbot (Series A Startup)

Initial setup:

• Customer support chatbot for B2B SaaS
• 15,000 conversations/month
• GPT-4 for all queries
• No caching
• Cost: $4,800/month

Optimizations (2 weeks):

1. Week 1: Implemented semantic caching for top 100 FAQs

   • Hit rate: 45%
   • Savings: $2,160/month

2. Week 2: Switched 70% of queries to GPT-3.5 (quality testing showed no degradation)

   • Savings: $1,680/month (on remaining 55% of uncached queries)

3. Bonus: Compressed system prompt from 800 to 200 tokens

   • Savings: $240/month

Final cost: $720/month (85% reduction)
Implementation time: 40 hours
ROI: ($4,080 × 12) / $4,000 = 12x

Case Study 2: Content Marketing Platform (Bootstrapped)

Initial setup:

• Automated blog post generation
• 500 articles/month
• GPT-4 for all content (2,000 tokens per article)
• Cost: $3,600/month

Optimizations (3 weeks):

1. Week 1: Switched to GPT-3.5 + prompt templates

   • Quality drop: ~5% (acceptable for drafts)
   • Savings: $3,420/month

2. Week 2: Implemented hierarchical generation (GPT-3.5 drafts → GPT-4 final polish for 20%)

   • Maintained quality, saved 80% on GPT-4 usage
   • Additional savings: $540/month

3. Week 3: Set max_tokens=600 per article, used structured output

   • Reduced output tokens by 40%
   • Savings: $180/month

Final cost: $460/month (87% reduction)
Implementation time: 60 hours
ROI: ($3,140 × 12) / $6,000 = 6.3x

Case Study 3: Enterprise RAG Search (Series C)

Initial setup:

• Internal knowledge base search
• 50,000 queries/month
• Retrieved 10 chunks (2,500 tokens) per query
• GPT-3.5 for summarization
• Cost: $5,625/month

Optimizations (4 weeks):

1. Week 1: Implemented reranking to reduce chunks from 10 → 3

   • Maintained quality (relevance actually improved)
   • Savings: $3,937/month

2. Week 2: Added semantic caching for common queries

   • Hit rate: 35%
   • Additional savings: $590/month

3. Week 3: Switched to Claude Haiku (better summarization, cheaper)

   • Quality improved, cost dropped 60%
   • Additional savings: $674/month

4. Week 4: Implemented prompt caching for system prompt (Anthropic feature)

   • Saved 90% on system prompt tokens
   • Additional savings: $169/month

Final cost: $255/month (95% reduction)
Implementation time: 80 hours
ROI: ($5,370 × 12) / $8,000 = 8x

Key takeaways from case studies

1. Caching delivers 30-60% savings with minimal quality impact
2. Model switching (GPT-4 → GPT-3.5) saves 80-95% for many tasks
3. RAG optimization (chunk reduction) saves 60-80% on retrieval systems
4. Total savings range: 70-95% with systematic optimization
5. ROI: 6-12x (implementations pay for themselves in 1-2 months)

Common Mistakes: What NOT to Do

Mistake 1: Optimizing too early

Problem: Spending weeks optimizing before validating product-market fit.
Fix: Wait until you hit $1,000/month spend before heavy optimization.
Exception: If burn rate is critical, optimize from day 1.

Mistake 2: Sacrificing quality for cost

Problem: Switching to GPT-3.5 without testing quality.
Fix: Always A/B test model changes, measure user satisfaction.
Rule: Never reduce quality by >10% for cost savings.

Mistake 3: Not measuring cache hit rate

Problem: Implementing caching but not tracking effectiveness.
Fix: Log cache hits/misses, target >40% hit rate.
Action: If <40%, expand cache or adjust semantic similarity threshold.

Mistake 4: Over-optimizing low-volume endpoints

Problem: Spending 20 hours optimizing an endpoint that costs $10/month.
Fix: Use 80/20 rule — optimize the 20% of endpoints that drive 80% of costs.
Tool: Cost dashboard to identify top spenders.

Mistake 5: No spending alerts

Problem: Costs spike 10x due to a bug, you notice a month later.
Fix: Set daily spending alerts from day 1.
Example: Alert if >$500/day, page if >$2,000/day.

Mistake 6: Ignoring retry costs

Problem: Implementing aggressive retry logic that doubles costs.
Fix: Log retry rates, add exponential backoff, limit retries to 2-3.
Stat: Retries can account for 20-40% of total costs if unchecked.

Mistake 7: Caching stale data

Problem: Caching answers for 7 days, serving outdated information.
Fix: Set appropriate TTLs — 1h for dynamic data, 24h for semi-static, 7d for static.
Balance: Freshness vs. cost.

Mistake 8: Not testing prompt compression

Problem: Assuming shorter prompts always maintain quality.
Fix: A/B test compressed prompts, measure quality metrics.
Finding: Most prompts can be compressed 30-50% without quality loss.

Mistake 9: Defaulting to GPT-4

Problem: Using GPT-4 for everything because "it's better."
Fix: Default to GPT-3.5, require justification for GPT-4.
Stat: 80% of tasks work fine with GPT-3.5.

Mistake 10: No cost attribution

Problem: Can't identify which features drive costs.
Fix: Tag API calls by feature/team/user, build cost dashboard.
Benefit: Enables targeted optimization.

Tool Recommendations

Cost Monitoring (Free)

• OpenAI Usage Dashboard — Basic token/cost tracking
• Anthropic Console — Token usage by API key
• Custom logging — Log to DB, analyze in spreadsheet/SQL

Cost Monitoring (Paid)

• Helicone ($0-100/month) — LLM-specific monitoring, caching, analytics
• LangSmith ($0-99/month) — Prompt testing, tracing, cost tracking
• Datadog ($15-50/month) — Full observability, alerts, dashboards

Caching

• GPTCache (free, open-source) — Semantic caching for LLMs
• Redis (free/paid) — General-purpose cache
• Anthropic Prompt Caching (built-in) — Cache prompt prefixes

Prompt Optimization

• tiktoken (free, Python) — Token counting for OpenAI models
• js-tiktoken (free, JavaScript) — Token counting for Node.js
• LLMLingua (free, research) — Prompt compression

Testing &amp; Evaluation

• Braintrust (free tier) — Prompt evaluation, A/B testing
• PromptLayer (free tier) — Prompt versioning, analytics
• Weights & Biases (free tier) — Experiment tracking

Infrastructure

• Pinecone ($70-400/month) — Managed vector database
• Weaviate (free self-hosted) — Open-source vector DB
• LiteLLM (free) — Unified API for multiple LLM providers

Advanced

• vLLM (free, open-source) — Self-hosted inference server
• Ollama (free) — Local LLM hosting
• Modal ($0-50/month) — Serverless GPU compute

Next Steps: Your 30-Day Optimization Plan

Week 1: Measure &amp; Quick Wins

• Day 1-2: Set up logging and cost dashboard
• Day 3-4: Implement semantic caching
• Day 5-7: Switch 50% of traffic to cheaper model, A/B test quality

Expected savings: 30-40%

Week 2: Model Optimization

• Day 8-10: Implement model routing (simple vs. complex queries)
• Day 11-12: Compress system prompts, set max_tokens
• Day 13-14: Test and deploy changes

Expected savings: Additional 15-25%

Week 3: Advanced Caching &amp; RAG

• Day 15-17: Optimize RAG (reduce chunks, rerank)
• Day 18-20: Implement multi-tier caching
• Day 21: Test and measure cache hit rate

Expected savings: Additional 10-20%

Week 4: Monitoring &amp; Governance

• Day 22-24: Set up alerts (cost spikes, error rates, latency)
• Day 25-26: Build cost attribution system
• Day 27-28: Run cost review with team, document learnings
• Day 29-30: Plan next round of optimizations

Total expected savings after 30 days: 50-70%

Final Thoughts

AI cost optimization isn't a one-time project — it's an ongoing practice. As you scale, new inefficiencies emerge. But with the techniques in this checklist, you'll build a culture of cost awareness that keeps your AI systems lean and efficient.

Key principles:

1. Measure everything — You can't optimize what you don't track
2. Start with quick wins — 80% of savings come from 20% of optimizations
3. Test quality rigorously — Never sacrifice user experience for cost
4. Automate monitoring — Set alerts, don't rely on manual checks
5. Share learnings — Build a cost-aware engineering culture

Remember: The goal isn't to minimize cost at all costs — it's to maximize value per dollar spent.

Want More?

This checklist covers production cost optimization. For deeper technical details:

• Cost & Latency Guide — Advanced techniques for speed and cost
• Production Monitoring Guide — Full observability setup
• Deployment Patterns — Infrastructure options and trade-offs
• Glossary: Token — Deep dive into token economics

License &amp; Sharing

This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You're free to:

• Share with your team, clients, or on social media
• Adapt for internal cost optimization workshops
• Print for team planning sessions
• Translate to other languages

Just include this attribution:

"AI Cost Optimization Checklist" by Field Guide to AI (fieldguidetoai.com) is licensed under CC BY 4.0

How to cite:
Field Guide to AI. (2025). AI Cost Optimization Checklist: Cut Your AI Spend by 30-70%. Retrieved from https://fieldguidetoai.com/resources/ai-cost-optimization-checklist

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What you get:

• Printable 8-page checklist (240KB PDF)
• 50+ actionable optimization items with difficulty/time estimates
• 3 real case studies with actual cost numbers
• ROI calculator framework
• Monitoring setup guide
• 30-day implementation roadmap

Perfect for engineering teams, CTOs, and anyone running AI in production.