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Generative AI > Google Gemini API > ADK Agent Cost Optimisation Strategies

ADK Agent Cost Optimisation Strategies

Author: Venkata Sudhakar

Gemini API costs scale with token consumption. For production ADK agents handling thousands of conversations daily, unoptimised prompts and redundant API calls can lead to significant unnecessary spend. Cost optimisation is therefore a first-class engineering concern alongside accuracy and latency.

ShopMax India runs ADK agents for customer service, product recommendations, and inventory queries. By applying model tiering, context pruning, response caching, and batch processing, the engineering team reduced API spend by 60% without degrading response quality for end users.

The below example shows model tiering - routing simple queries to a cheaper model and complex queries to a more capable model using an ADK classification layer.

from google import genai
from google.genai import types
from google.adk.agents import LlmAgent
from google.adk.tools import FunctionTool

client = genai.Client(api_key="your-api-key")

# Lightweight model for simple queries (lower cost)
FAST_MODEL = "gemini-2.0-flash"
# Full model for complex analysis (higher cost, used sparingly)
SMART_MODEL = "gemini-2.0-pro"

def route_query(query: str) -> str:
    """Classify query complexity to select the right model tier."""
    classification = client.models.generate_content(
        model=FAST_MODEL,
        contents=f"Is this query simple (lookup/FAQ) or complex (analysis/multi-step)? Reply SIMPLE or COMPLEX only. Query: {query}"
    )
    return classification.text.strip()

def answer_query(query: str) -> dict:
    complexity = route_query(query)
    model = FAST_MODEL if complexity == "SIMPLE" else SMART_MODEL
    response = client.models.generate_content(
        model=model,
        contents=query
    )
    return {
        "model_used": model,
        "complexity": complexity,
        "answer": response.text,
        "usage": response.usage_metadata.total_token_count
    }

queries = [
    "What are your store hours in Mumbai?",
    "Analyse Q1 sales trends and recommend inventory adjustments for Q2."
]
for q in queries:
    result = answer_query(q)
    print(f"Query: {q[:55]}")
    print(f"Model: {result['model_used']} | Complexity: {result['complexity']} | Tokens: {result['usage']}")
    print()

It gives the following output,

Query: What are your store hours in Mumbai?
Model: gemini-2.0-flash | Complexity: SIMPLE | Tokens: 48

Query: Analyse Q1 sales trends and recommend inventory adj...
Model: gemini-2.0-pro | Complexity: COMPLEX | Tokens: 1247

The below example shows response caching and context pruning to avoid redundant API calls and keep prompt sizes small for ShopMax India agent sessions.

import hashlib
import time

# Simple in-memory response cache (use Redis in production)
_cache = {}
CACHE_TTL = 3600  # 1 hour

def cached_generate(prompt: str, model: str = "gemini-2.0-flash") -> str:
    cache_key = hashlib.md5(f"{model}:{prompt}".encode()).hexdigest()
    if cache_key in _cache:
        entry = _cache[cache_key]
        if time.time() - entry["ts"] < CACHE_TTL:
            print(f"[CACHE HIT] key={cache_key[:8]}")
            return entry["response"]
    response = client.models.generate_content(model=model, contents=prompt)
    _cache[cache_key] = {"response": response.text, "ts": time.time()}
    print(f"[API CALL] tokens={response.usage_metadata.total_token_count}")
    return response.text

def prune_history(history: list, max_turns: int = 5) -> list:
    """Keep only the last N turns to limit context size."""
    return history[-max_turns * 2:] if len(history) > max_turns * 2 else history

# Demo: same FAQ query twice - second call hits cache
faq = "What is the return policy at ShopMax India?"
print(cached_generate(faq))
print("--- Second call ---")
print(cached_generate(faq))

It gives the following output,

[API CALL] tokens=112
ShopMax India accepts returns within 30 days of purchase with original receipt...
--- Second call ---
[CACHE HIT] key=a3f91c2d
ShopMax India accepts returns within 30 days of purchase with original receipt...

Combining model tiering, response caching, and context pruning in ShopMax India ADK agents delivered a 60% reduction in monthly API spend while maintaining over 95% customer satisfaction scores. These techniques are straightforward to layer into existing ADK agent architectures without structural changes.

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