The AI Workflow Planner lets you design multi-step AI pipelines visually — chain embed, retrieve, generate, classify, evaluate, and cache steps. Get per-request cost, total latency, and monthly cost estimates at your traffic volume.
Pipeline Steps
Cost Summary
Prices are estimates as of April 2026. Actual costs vary by provider tier and volume discounts.
How to Plan an AI Workflow
Planning your AI workflow before building prevents expensive surprises. A seemingly simple pipeline with multiple LLM calls can cost $0.10+ per request — which adds up to $3,000+/month at 1,000 requests/day. Use this planner to identify cost bottlenecks early.
Step 1: Choose a Preset or Build From Scratch
The four presets cover the most common architectures: Simple RAG (embed + retrieve + generate), Agent Loop (generate + evaluate + cache), Classify + Generate (classify intent, generate response), and Full RAG Pipeline (cache check + embed + retrieve + generate + evaluate). Start with the closest preset, then customize.
Step 2: Configure Each Step
Each step has configurable parameters. For Generate steps, the model choice dominates cost — GPT-4o costs 15× more than GPT-4o-mini per token. For Embed steps, all modern models are very cheap ($0.02/1M tokens). For Retrieve steps, self-hosted vector DBs (Qdrant, Chroma) cost essentially $0 per query.
Step 3: Review Monthly Estimates
Enter your expected daily request volume to see monthly projections. A pipeline costing $0.01/request at 1,000 req/day costs $300/month. If this exceeds budget, look for the largest cost line in the step breakdown and optimize: switch to a smaller model, reduce output tokens, or add caching.
Cost Optimization Tips
Add a Cache Check step before LLM generation — cached results cost ~$0. For customer support bots with repetitive questions, this alone can cut costs 50–80%. Use smaller models for classification and routing steps; only use GPT-4o for the final generation if quality demands it. Limit output tokens — most LLM cost comes from output tokens (3–5× input cost). Set a max_tokens limit appropriate for your use case.
FAQ
How do I estimate AI pipeline costs?
Break your pipeline into discrete steps: each embedding call, vector search query, LLM generation, and evaluation check has its own cost. Sum the costs per request, then multiply by daily request volume × 30 for monthly estimates. The most expensive step is usually the LLM generation step, which can account for 90%+ of total pipeline cost.
What is a RAG pipeline and how much does it cost?
A RAG (Retrieval-Augmented Generation) pipeline has three steps: embed the user query (~$0.00002 per request with text-embedding-3-small), retrieve from a vector database (~$0.001–0.01 per request for managed services), and generate with an LLM (~$0.001–0.05 depending on model). A typical Simple RAG pipeline costs $0.002–0.05 per request.
How much does vector database search cost?
Managed vector databases like Pinecone charge $0.10–0.40 per 1,000 queries at standard performance tiers, or roughly $0.0001–0.0004 per query. Self-hosted options (Qdrant, Weaviate, Chroma) have zero per-query cost but require infrastructure. At 10,000 requests/day, managed DB search costs $30–120/month.
What is the latency of a typical RAG pipeline?
A typical RAG pipeline adds 500–2,000ms of latency. Embedding takes 50–200ms, vector search takes 20–100ms, and LLM generation takes 500–5,000ms depending on output length and model. GPT-4o-mini or Claude Haiku generate faster than GPT-4o or Claude Opus for the same output length.
Can I add caching to reduce AI pipeline costs?
Yes, caching is one of the most effective cost reducers. Adding a cache check before expensive LLM calls can cut costs by 30–80% for applications with repeated queries (like customer support chatbots). Redis-based semantic caching checks for similar past queries and returns cached responses, costing ~5ms and essentially $0.
Is this workflow planner free?
Yes, completely free with no signup required. All calculations run in your browser.