AI Cost & Architecture Planner
Answer a few questions about your workload and get a recommended provider architecture with estimated monthly cost, expected latency, potential savings, trade-offs, and migration considerations. This is the tool a chatbot can't give you in a reusable, interactive format.
Tell Us About Your Workload
Additional Needs
Monthly Cost by Architecture Option
How the Planner Works
The planner scores every model in the catalog against your needs (use case, quality, latency, budget, capabilities) and then constructs three architecture options: single-provider cheapest, single-provider best quality, and multi-provider hybrid (cheap model for routing + flagship for complex). It estimates monthly cost, expected latency, and highlights trade-offs and migration considerations.
Architecture Patterns
- Single-provider (cheapest): One economy model for everything. Lowest cost, simplest code, but quality ceiling is lower.
- Single-provider (best quality): One flagship model. Highest quality, but 5-20× the cost of economy.
- Multi-provider hybrid: Route simple queries to a cheap model (GPT-5 Nano, DeepSeek V3) and escalate complex ones to a flagship (GPT-5, Claude Sonnet 4). Typically 60-80% cheaper than flagship-only with minimal quality loss.
- RAG-augmented: Embeddings + vector DB + LLM. Dominant cost is the LLM call with retrieved context, not embeddings.
FAQ
How do I choose between single and multi-provider AI architecture?
Use single-provider for simplicity and volume discounts. Use multi-provider for failover, cost optimization (cheap model for routing, flagship for complex), and avoiding lock-in. The planner recommends the best option for your specific workload.
What is the cheapest AI architecture for high-volume chat?
Route with GPT-5 Nano or DeepSeek V3 for simple queries, escalate to GPT-4.1 Mini for complex ones. Use prompt caching for system prompts. Expect 60-80% cost savings vs flagship-only.
How much does a RAG system cost per month?
A RAG system serving 5,000 queries/day with 5 chunks of 500 tokens each costs roughly $50-200/month depending on the LLM. Embedding cost is negligible; the LLM call dominates. Use the planner with RAG enabled for a precise estimate.
What about migration costs between providers?
The planner estimates migration cost as the difference in per-request cost × monthly volume. Most providers use OpenAI-compatible APIs, so code changes are minimal. The main cost is re-testing prompts and adjusting for different tokenizers.
Does the planner consider latency?
Yes. If you select "Low" latency, the planner favors economy and balanced tiers and flags reasoning models (which have higher TTFT) as trade-offs.