Building an Agentic AI System: Model Selection and Cost Optimization
Agentic AI systems use LLMs to take actions, use tools, and complete tasks. Here's how to build a cost-effective agentic system.
Pricing data sourced from our catalog. Check data sources for provenance and freshness.
What is agentic AI?
Agentic AI systems use LLMs to:
- Take actions: Make API calls, write code, interact with systems
- Use tools: Call functions, access databases, search the web
- Complete tasks: Break down complex tasks into steps
- Learn from feedback: Adjust behavior based on results
Key cost factors for agentic systems
Agentic systems have unique cost considerations:
- Multi-step workflows: Each task requires multiple LLM calls
- Tool use: Function calling adds overhead
- Reasoning: Complex reasoning requires more tokens
- Error handling: Failed steps may need retries
Cost estimation example
Let's estimate costs for a typical agentic workload:
Note: This is a simplified estimate. Actual costs may vary based on task complexity, model choice, and error rates.
Model selection for agentic systems
What to look for
- Tool use: Support for function calling
- Reasoning: Strong reasoning capabilities
- Reliability: Consistent, accurate outputs
- Cost: Balance quality with cost
Top agentic models by cost
| Model | Input | Output | Context |
|---|---|---|---|
| $0.0200 | $0.0200 | 131K | |
| $0.0150 | $0.0250 | 131K | |
| $0.0150 | $0.0250 | 131K | |
| $0.0100 | $0.0300 | 33K | |
| $0.0200 | $0.0300 | 131K |
Cost optimization tips
- Use smaller models: For less complex steps
- Cache results: Avoid re-computing unchanged data
- Batch processing: Process multiple tasks together
- Error handling: Implement retries and fallbacks
- Monitor usage: Track token usage to optimize costs
Architecture patterns
Simple agentic pipeline
For most applications, a simple agentic pipeline works well: plan, execute, observe, and iterate.
Advanced agentic pipeline
For complex applications, consider: multi-agent systems, human-in-the-loop, and hierarchical planning.
Compare agentic models
Ready to compare agentic models side by side? Use our tools:
Related guides
Building a coding agent
How to build a coding agent with LLM APIs.
Building a chatbot
How to choose models and optimize costs for chatbot applications.
Cross-provider pricing comparison
How pricing compares across OpenAI, Anthropic, Google, Mistral, and DeepSeek.
Hidden costs of LLM APIs
Rate limits, latency, evaluation overhead, and vendor risk beyond per-token pricing.
LLM API pricing glossary
Key terms: prompt caching, batch pricing, reasoning tokens, model routing, and more.
Frequently asked questions
Which models are the lowest-cost starting points in this catalog?
The first model in the current cost-sorted table is Llama-3.2-3B-Instruct. The table includes chat routes with function calling or reasoning and known token pricing, then sorts them by combined input and output price. Treat it as a low-cost starting point, not a quality ranking.
How much should I budget for agentic systems?
There is no fixed monthly budget. With the worked example above — 10,000 tasks, 5 steps each, 1,000 input and 500 output tokens per step — the current lowest-cost qualifying route estimates to $1.50 per month. Use the Agentic AI calculator preset to replace those assumptions with your own session volume, token counts, and cache-hit rate.
Can I use prompt caching for agentic systems?
Yes, when requests reuse prompt context and the selected model has a cached-input price. The calculator models a cache-hit share of input tokens; if the catalog has no separate cached-input price, it falls back to the standard input price instead of assuming savings.