Multi-Node Agent Cost Estimator

AI Workflow Cost Simulator

Add, reorder, and configure multiple AI call steps to simulate real-world workflows (like RAG chains and Agent pipelines). Account for context accumulation, cache hit ratios, and discover total billing costs.

AI Workflow Token Simulator
Design multi-step Agent pipelines to simulate token accumulation and total API bills under multi-turn chat or cascading calls
Template Presets:
1
Total Input:5,000
Output Tokens:800
Cache Savings:0%
Official Price:\$0.0012
Kie.ai Price:\$0.0007
2
Total Input:8,800
Output Tokens:4,000
Cache Savings:80%
Official Price:\$0.0043
Kie.ai Price:\$0.0026
3
Total Input:20,800
Output Tokens:1,500
Cache Savings:50%
Official Price:\$0.1022
Kie.ai Price:\$0.0613
The workflow simulation represents a single full execution. In practice, context caching duration on the model side is usually 5-60 minutes.
Total Steps3 nodes
Total Input Tokens34,600
Total Output Tokens6,300
Total Combined Tokens40,900
πŸ’‘ Running this workflow with Kie.ai Unified API saves $0.043 (40.0% reduction)
Total Official Price per Run
\$0.1077
Kie.ai Total Price per Run
\$0.0646
Configure Kie.ai API Workflow
Why Choose Kie.ai Unified API Gateway?
Kie.ai provides stable, high-concurrency, and highly competitive pricing for multimodal AI APIs, eliminating the hassle of binding cards on multiple platforms.
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Unbeatable Prices

LLM (GPT-5.5, Claude, DeepSeek) calling costs are 30% - 50% lower than official APIs. Multimodal (Veo 3.1, Flux Pro) costs are 60%+ lower!

Full Multimodal Support

Single key aggregates text, image, video generation (Runway, Veo, Kling), music generation (Suno), and speech recognition. No multiple accounts needed.

Standard Compatible

Fully compatible with OpenAI / Anthropic request formats. Simply update base_url and api_key in your code to migrate seamlessly.

Developer Integration Guides (Cursor, Claude Code, SDK)

Workflow Cost FAQ

Q: What is context accumulation in workflows?

In multi-step Agent tasks or multi-turn chats, the inputs and outputs of prior steps are typically appended to the current prompt as conversation history. This causes input tokens to snowball. Enabling 'Accumulate History Context' tells the simulator to automatically carry over preceding tokens to the current step's input, delivering a highly realistic billing estimate.

Q: How does prompt caching reduce workflow costs?

Mainstream models like DeepSeek-V4, Gemini, and Claude support caching for system prompts or long contexts (like RAG database texts). When a cache hit occurs, input tokens are charged at a fraction of the cost (e.g. DeepSeek input drops to $0.0036/M tokens on cache hits). Adjusting the 'Cache Hit Rate' in each step shows you exactly how much caching saves.

AI Agent Workflow Optimization Guide

When designing and deploying production AI workflows, you can utilize these best practices to save on overall API runtime billing:

  • Compact Intermediate Outputs: While multi-step agents handle complex logical tasks, context grows fast. We recommend periodically summarizing conversation history or removing non-critical scratchpad outputs between turns to prevent the token snowball.
  • Use Tiered Models: Use lightweight models (like GPT-5.4 Mini or Gemini 2.5 Flash-Lite) for routing, classification, or formatting tasks. Save heavy models (like GPT-5.5 Pro or Claude 3.7) strictly for critical reasoning, coding, and final summaries.
  • Leverage Kie.ai Network: Kie.ai API gateway discounts apply to both closed-source and open-source models, helping you slash overall multi-step agent runtime costs by 30% to 50% in production.