Stage	Strategy	Why
Pre-seed/Seed	Nova Micro/Lite for everything, Sonnet only for demos	Credits burn fast on token-heavy workloads; prove the product logic works with cheap models first
Series A	Route 80% to Nova Micro/Lite, 20% to Sonnet	Intelligent routing saves 60-70% vs blanket Sonnet usage
Series B+	Optimize per-endpoint with measured quality thresholds	You have traffic data now — let it drive model selection

Don’t start with the best model and optimize later. Start with Nova Micro. If it fails, you’ve learned exactly what capability gap you need — and you’ll prompt-engineer around it first. Most startups discover 70%+ of their calls work fine on the cheapest model.
Nova Pro is underrated. For most startup use cases (summarization, extraction, Q&A), Nova Pro matches Claude Sonnet quality at ~3-5x lower cost per token. Evaluate it before assuming you need Anthropic models.
Batch API saves 50% — use it aggressively. Any workload that doesn’t need real-time response (nightly processing, background enrichment, evaluation runs) should use batch. Most startups leave this on the table.

Every agent step multiplies cost: tool selection reasoning + tool execution + result synthesis = 3-5x the token cost of a single invoke.

Startup rule of thumb:

< $500/month LLM spend → Never use agents. Use InvokeModel with structured prompts.
$500-5K/month → One simple agent max. Router + specialist only if you have genuinely distinct domains.
$5K+/month → Agent architecture justified if you have evidence single-call can’t do the job.

Bedrock on-demand pricing is token-based — AWS Activate credits cover it fully (no commitment needed)
Don’t buy Provisioned Throughput with credits. Credits expire; provisioned commitments don’t. You’ll be stuck paying on-demand rates after credits burn down with capacity you may not need.
Prompt caching is automatic for supported models — structure your prompts with stable system prompts first to maximize cache hits (free repeated tokens)

OpenSearch Serverless minimum cost: ~$700/month (2 OCU indexing + 2 OCU search minimum).

Monthly queries	Vector store choice	Monthly cost
< 1,000	Skip KB entirely — stuff context into prompt	$0 (just token cost)
1K-50K	Aurora Serverless v2 with pgvector	$50-150 (scales to zero)
50K-500K	Single OpenSearch Serverless collection shared across KBs	$700 minimum
500K+	Dedicated OpenSearch Serverless per KB	$700+ per collection

When to graduate from prompt-stuffing to RAG:

Building “AI features” before product-market fit. LLM costs scale with users. If you haven’t validated demand, you’re burning credits on a product nobody wants. Validate with a Wizard-of-Oz or rules-based MVP first.
OpenSearch Serverless for a PoC. $700/month minimum for something 50 users will touch. Use pgvector on Aurora Serverless or just stuff documents into prompts until scale demands RAG.
Custom fine-tuning before exhausting prompt engineering. Fine-tuning on Bedrock costs real money (training tokens + hosting custom model). 95% of startup use cases are solved with better prompts + few-shot examples. Fine-tune only when you have 10K+ labeled examples and measurable quality gap.
Not tracking per-feature token costs. When you have 5 AI features, one will be 80% of your bill. Without per-feature cost attribution, you can’t make informed product decisions about which features to keep/kill/optimize.

references/bedrock.md