2. Multi-Step Agent (research, data processing, workflow)

Makes 4-8 API calls per task. Uses tool calling and reasoning.

3. Coding Agent (code generation, bug fixing, refactoring)

Makes 8-15 API calls per task. Needs strong reasoning and tool use.

— See if you're overpaying for AI APIs

Build a Cheap AI Agent: Python Code Example

Here's a complete multi-step AI agent with tool calling, using the cheapest models. This agent can search the web, read documents, and synthesize findings — all for under $5/month:

import openai
import json

# DeepSeek V4 Pro — best value for agents ($0.44/$0.87 per M tokens)
agent = openai.OpenAI(
    api_key="YOUR_DEEPSEEK_KEY",
    base_url="https://api.deepseek.com/v1"
)

# Define tools for the agent
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "read_document",
            "description": "Read and analyze a document",
            "parameters": {
                "type": "object",
                "properties": {
                    "url": {"type": "string", "description": "Document URL"}
                },
                "required": ["url"]
            }
        }
    }
]

def run_agent(task, max_steps=8):
    """Run the agent on a task with limited steps."""
    messages = [
        {"role": "system", "content": "You are a research agent. Use tools to find information, then synthesize a clear answer. Be concise."},
        {"role": "user", "content": task}
    ]

    for step in range(max_steps):
        response = agent.chat.completions.create(
            model="deepseek-chat",
            messages=messages,
            tools=tools,
            max_tokens=1000
        )

        msg = response.choices[0].message

        # If no tool calls, the agent is done
        if not msg.tool_calls:
            return msg.content

        # Execute tool calls
        messages.append(msg)
        for tool_call in msg.tool_calls:
            result = execute_tool(tool_call.function.name,
                                 json.loads(tool_call.function.arguments))
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result)
            })

    return "Max steps reached"

def execute_tool(name, args):
    """Execute a tool — replace with real implementations."""
    if name == "search_web":
        return {"results": [f"Result for: {args['query']}"]}
    elif name == "read_document":
        return {"content": f"Document content from: {args['url']}"}

# Example usage — costs about $0.003 per task
result = run_agent("What are the latest pricing changes for GPT-5?")
print(result)

At 50 tasks/day, this agent costs about $4.50/month on DeepSeek V4 Pro — or $27/month on Claude Haiku 4.5 for higher quality.

6 Cost Optimization Strategies

Agent Cost by Volume

What you'll actually pay for a multi-step agent (6 steps/task, 2K input + 600 output per step):

Hidden Costs to Watch For

• Context accumulation: Each step re-sends all previous context. After 10 steps, you're paying for 10x the original input tokens. This is the #1 hidden cost for agents.
• Tool definition bloat: Each tool definition adds 100-300 tokens to every request. 20 tools = 4,000 extra input tokens per API call.
• Retry storms: Rate limits cause retries. Each retry is a full API call. Add exponential backoff and circuit breakers.
• Parallel tool calls: Some agents call 5 tools simultaneously. That's 5x the output tokens for tool definitions in a single request.
• Long-running agents: A 24/7 agent making 1 call/minute = 43,200 API calls/day. Even cheap models add up.

When to Upgrade from Budget to Premium

The Bottom Line