AI Infrastructure Costs for Startups: What to Expect | HyperNest Labs

1. LLM API Costs by Provider

Current pricing (as of early 2026) for major LLM providers:

OpenAI

GPT-4 Turbo: $10/1M input, $30/1M output tokens

GPT-4o: $5/1M input, $15/1M output tokens

GPT-3.5 Turbo: $0.50/1M input, $1.50/1M output tokens

Embeddings (ada-002): $0.10/1M tokens

Anthropic

Claude 3 Opus: $15/1M input, $75/1M output tokens

Claude 3 Sonnet: $3/1M input, $15/1M output tokens

Claude 3 Haiku: $0.25/1M input, $1.25/1M output tokens

Vector Databases

Pinecone Starter: Free (up to 100K vectors)

Pinecone Standard: $70+/month

pgvector: Cost of PostgreSQL instance

Weaviate Cloud: $25+/month

2. Expected Costs by Stage

MVP / Beta (0-100 users)

$100-500/month

• Light usage, few API calls
• Use best models (GPT-4) for signal
• Free tier for vector storage
• Don't optimize yet

Early Growth (100-1K users)

$500-2,000/month

• Start caching common queries
• Consider smaller models for simple tasks
• Paid vector storage tier
• Monitor per-user costs

Scale (1K-10K users)

$2,000-15,000/month

• Multi-model routing essential
• Aggressive caching (40-60% hit rate)
• Consider fine-tuned smaller models
• Rate limiting per user tier

Enterprise (10K+ users)

$15,000-100,000+/month

• Enterprise agreements for volume discounts
• Self-hosted models for some workloads
• Dedicated infrastructure
• Cost per user becomes key metric

3. Hidden Costs to Plan For

LLM API costs are just part of the story:

Retries and failures: 5-15% of API calls fail. Budget for retries in your cost model.
Evaluation calls: Testing prompt changes against your golden dataset costs real money.
Development and testing: Your team will make thousands of calls during development.
Logging infrastructure: Storing prompts and responses for debugging adds up.
Abuse and edge cases: Some users will use AI features 100x more than average.

Rule of thumb: Budget 30-50% more than your projected API costs for these factors.

4. Cost Optimization Strategies

Caching (40-70% savings)

Semantic caching returns cached results for similar queries. Implementation cost: 1-2 days. ROI: Usually within first month.

Model Routing (30-50% savings)

Use cheaper models for simple tasks. GPT-3.5 is 20x cheaper than GPT-4 and sufficient for classification, extraction, and formatting.

Prompt Optimization (10-30% savings)

Shorter prompts = fewer tokens = lower cost. Remove redundant instructions. Use concise system prompts. Compress context.

Rate Limiting (prevents runaway costs)

Set per-user and global limits. Implement usage tiers. Alert on anomalies before they become expensive.

5. Budgeting for AI Features

How to budget AI costs as a percentage of other infrastructure:

Pre-product-market-fit: Don't optimize. Spend on the best models to learn whether AI adds value.
Post-PMF: AI costs should be 10-30% of total infrastructure. Optimize aggressively if higher.
Pricing consideration: If AI is core to value, build cost into pricing. Many AI-first products charge $20-50/user/month.
Per-user math: Calculate cost per monthly active user. Should be <10% of revenue per user.

AI Infrastructure Costs for Startups: What to Actually Expect

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