Khi xây dựng AI Agent phục vụ nghiệp vụ tài chính, chi phí gọi API bên ngoài có thể tăng vọt không kiểm soát nếu không có chiến lược tối ưu rõ ràng. Bài viết này chia sẻ kinh nghiệm thực chiến của đội ngũ kỹ sư HolySheep trong việc xây dựng hệ thống cost guardrail với Tardis Data, tối ưu model inference và đồng bộ task queue để đạt hiệu suất tối đa với chi phí tối thiểu.
Vấn Đề Thực Tế: Khi Chi Phí API Bùng Nổ Không Kiểm Soát
Trong dự án triển khai AI Agent phân tích danh mục đầu tư cho khách hàng doanh nghiệp, đội ngũ của tôi từng đối mặt với tình trạng chi phí gọi API tăng 300% chỉ trong 2 tuần. Nguyên nhân chính bao gồm:
- Agent gọi lặp lại cùng một API với dữ liệu tương tự nhiều lần
- Không có cơ chế cache hiệu quả cho dữ liệu tài chính
- Model inference không được tối ưu cho workload batch
- Task queue không phân biệt được priority và không hủy được task thừa
Sau 3 tháng tinh chỉnh, chúng tôi giảm được 78% chi phí API trong khi vẫn duy trì độ trễ dưới 200ms cho 95% request. Bài viết dưới đây là tổng hợp toàn bộ best practices mà đội ngũ đã đúc kết.
Kiến Trúc Cost Guardrail Tổng Quan
Kiến trúc cost guardrail cho AI Agent tài chính cần đảm bảo 4 tầng bảo vệ:
+------------------+ +------------------+ +------------------+
| Task Queue | --> | Cost Budget | --> | Rate Limiter |
| (Priority) | | Controller | | (Per-minute) |
+------------------+ +------------------+ +------------------+
|
v
+------------------+ +------------------+ +------------------+
| Cache Layer | <-- | Tardis Data | <-- | External API |
| (LRU + TTL) | | Transformer | | (Financial) |
+------------------+ +------------------+ +------------------+
|
v
+------------------+
| Model Inference |
| (Batch Optimize)|
+------------------+
Tầng 1: Task Queue Với Priority và Cancellation
Task queue là lớp quan trọng nhất để kiểm soát chi phí. Chúng tôi sử dụng Redis-backed priority queue với khả năng cancel task theo correlation ID.
import asyncio
import redis.asyncio as redis
import json
import hashlib
from typing import Optional, List, Dict, Any
from dataclasses import dataclass, field
from enum import IntEnum
class TaskPriority(IntEnum):
CRITICAL = 1 # Real-time trading signals
HIGH = 2 # Portfolio rebalancing
NORMAL = 3 # Daily reports
LOW = 4 # Historical analysis
BATCH = 5 # Non-urgent data sync
@dataclass
class CostTrackedTask:
task_id: str
correlation_id: str
priority: TaskPriority
estimated_cost_usd: float
actual_cost_usd: float = 0.0
status: str = "pending"
created_at: float = field(default_factory=lambda: asyncio.get_event_loop().time())
api_calls: List[Dict[str, Any]] = field(default_factory=list)
class FinancialTaskQueue:
"""
Priority queue với cost tracking và cancellation support.
Priority càng thấp (1=CRITICAL) càng được xử lý trước.
"""
def __init__(
self,
redis_url: str = "redis://localhost:6379",
cost_budget_per_minute: float = 10.0,
cost_budget_per_day: float = 500.0
):
self.redis = redis.from_url(redis_url)
self.cost_budget_per_minute = cost_budget_per_minute
self.cost_budget_per_day = cost_budget_per_day
self._daily_cost_key = "daily_cost:total"
self._minute_cost_key = "minute_cost:total"
async def enqueue(
self,
correlation_id: str,
payload: Dict[str, Any],
priority: TaskPriority = TaskPriority.NORMAL,
max_cost_estimate: float = 0.50
) -> str:
"""Enqueue task với cost guard check."""
# Check cost budget trước khi enqueue
can_proceed = await self._check_cost_budget(max_cost_estimate)
if not can_proceed:
raise RuntimeError(
f"Cost budget exceeded. Estimated: ${max_cost_estimate:.4f}"
)
task_id = hashlib.sha256(
f"{correlation_id}:{asyncio.get_event_loop().time()}".encode()
).hexdigest()[:16]
task = CostTrackedTask(
task_id=task_id,
correlation_id=correlation_id,
priority=priority,
estimated_cost_usd=max_cost_estimate
)
# Store task data
await self.redis.hset(
f"task:{task_id}",
mapping={
"data": json.dumps(payload),
"task": json.dumps({
"task_id": task.task_id,
"correlation_id": task.correlation_id,
"priority": int(task.priority),
"estimated_cost_usd": task.estimated_cost_usd,
"status": task.status,
"created_at": task.created_at
})
}
)
await self.redis.expire(f"task:{task_id}", 86400) # 24h TTL
# Add to priority sorted set (score = priority + timestamp for FIFO within same priority)
score = int(priority) + (asyncio.get_event_loop().time() / 1e6)
await self.redis.zadd("task_queue", {task_id: score})
# Track correlation for cancellation
await self.redis.sadd(f"correlation:{correlation_id}", task_id)
return task_id
async def cancel_by_correlation(self, correlation_id: str) -> int:
"""Cancel all tasks for a correlation ID (VD: user hủy request cũ)."""
task_ids = await self.redis.smembers(f"correlation:{correlation_id}")
cancelled = 0
for task_id in task_ids:
task_id = task_id.decode() if isinstance(task_id, bytes) else task_id
# Check if task chưa chạy
task_data = await self.redis.hget(f"task:{task_id}", "task")
if task_data:
task_info = json.loads(task_data)
if task_info["status"] == "pending":
await self.redis.zrem("task_queue", task_id)
await self.redis.hset(f"task:{task_id}", "status", "cancelled")
cancelled += 1
# Cleanup correlation tracking
await self.redis.delete(f"correlation:{correlation_id}")
return cancelled
async def _check_cost_budget(self, additional_cost: float) -> bool:
"""Kiểm tra xem có đủ budget để chạy task không."""
minute_cost = float(await self.redis.get(self._minute_cost_key) or 0)
daily_cost = float(await self.redis.get(self._daily_cost_key) or 0)
return (minute_cost + additional_cost <= self.cost_budget_per_minute and
daily_cost + additional_cost <= self.cost_budget_per_day)
async def record_cost(self, task_id: str, cost_usd: float, api_endpoint: str):
"""Record actual cost sau khi task hoàn thành."""
await self.redis.incrbyfloat(self._minute_cost_key, cost_usd)
await self.redis.incrbyfloat(self._daily_cost_key, cost_usd)
await self.redis.expire(self._minute_cost_key, 60) # Reset per minute
# Update task record
await self.redis.hincrbyfloat(f"task:{task_id}", "actual_cost_usd", cost_usd)
Usage example
async def main():
queue = FinancialTaskQueue(
redis_url="redis://localhost:6379",
cost_budget_per_minute=5.0, # $5/phút max
cost_budget_per_day=200.0 # $200/ngày max
)
# Enqueue với priority
task_id = await queue.enqueue(
correlation_id="user:123:portfolio:refresh",
payload={"symbols": ["AAPL", "GOOGL", "MSFT"]},
priority=TaskPriority.HIGH,
max_cost_estimate=0.15
)
print(f"Enqueued task: {task_id}")
# Cancel outdated requests (VD: user spam click)
cancelled = await queue.cancel_by_correlation("user:123:portfolio:refresh")
print(f"Cancelled {cancelled} outdated tasks")
if __name__ == "__main__":
asyncio.run(main())
Tầng 2: Tardis Data Integration Với Smart Caching
Tardis Data là nguồn dữ liệu tài chính quan trọng, nhưng gọi API liên tục sẽ tốn kém. Chúng tôi xây dựng caching layer với 3 chiến lược:
- TTL ngắn cho dữ liệu real-time: Giá, tỷ giá - 5-30 giây
- TTL dài cho dữ liệu historical: Báo cáo quý, phân tích - 1-24 giờ
- LRU eviction: Giới hạn cache size để tránh tràn bộ nhớ
import asyncio
import hashlib
import json
import time
from typing import Optional, Dict, Any, Tuple
from dataclasses import dataclass
from enum import Enum
import redis.asyncio as redis
class DataFreshness(Enum):
REALTIME = ("realtime", 5) # 5 seconds TTL
INTRA_DAY = ("intraday", 60) # 1 minute TTL
EOD = ("eod", 3600) # 1 hour TTL
HISTORICAL = ("historical", 86400) # 24 hours TTL
def __init__(self, label: str, ttl_seconds: int):
self.label = label
self.ttl_seconds = ttl_seconds
@dataclass
class CachedResponse:
data: Dict[str, Any]
cached_at: float
freshness: DataFreshness
api_cost_saved: float # Estimated cost saved by caching
hit_count: int = 1
class TardisDataCache:
"""
Multi-layer cache cho Tardis financial data.
Layer 1: Redis (distributed)
Layer 2: Local LRU (per-instance)
"""
def __init__(
self,
redis_url: str = "redis://localhost:6379",
local_cache_size: int = 1000,
tardis_api_base: str = "https://api.tardis.daily/v1"
):
self.redis = redis.from_url(redis_url)
self.tardis_base = tardis_api_base
self.local_cache: Dict[str, CachedResponse] = {}
self.local_cache_size = local_cache_size
self._lru_order: list = []
def _generate_cache_key(
self,
endpoint: str,
params: Dict[str, Any],
freshness: DataFreshness
) -> str:
"""Tạo deterministic cache key."""
param_str = json.dumps(params, sort_keys=True)
key_material = f"{endpoint}:{param_str}:{freshness.label}"
return f"tardis:{hashlib.sha256(key_material.encode()).hexdigest()[:32]}"
async def get_or_fetch(
self,
endpoint: str,
params: Dict[str, Any],
freshness: DataFreshness = DataFreshness.INTRA_DAY,
api_key: str = None,
fallback_to_stale: bool = True
) -> Tuple[Dict[str, Any], bool]:
"""
Get data từ cache hoặc fetch từ API.
Returns: (data, is_cache_hit)
"""
cache_key = self._generate_cache_key(endpoint, params, freshness)
# Layer 1: Local cache check
local_hit = self._check_local_cache(cache_key, freshness)
if local_hit:
self._update_lru(cache_key)
return local_hit.data, True
# Layer 2: Redis cache check
redis_data = await self.redis.get(cache_key)
if redis_data:
cached = json.loads(redis_data)
response = CachedResponse(
data=cached["data"],
cached_at=cached["cached_at"],
freshness=freshness,
api_cost_saved=cached.get("api_cost_saved", 0.001),
hit_count=cached.get("hit_count", 1) + 1
)
self._store_local_cache(cache_key, response)
return response.data, True
# Cache miss: fetch from API
data = await self._fetch_from_tardis(endpoint, params, api_key)
cost_saved = self._estimate_api_cost(endpoint, params)
# Store in both layers
response = CachedResponse(
data=data,
cached_at=time.time(),
freshness=freshness,
api_cost_saved=cost_saved
)
await self._store_redis(cache_key, response)
self._store_local_cache(cache_key, response)
# Handle stale-while-revalidate cho non-critical data
if fallback_to_stale and freshness != DataFreshness.REALTIME:
asyncio.create_task(self._refresh_stale(cache_key, endpoint, params, freshness, api_key))
return data, False
async def _fetch_from_tardis(
self,
endpoint: str,
params: Dict[str, Any],
api_key: str
) -> Dict[str, Any]:
"""Fetch data từ Tardis API (implementation dependent on provider)."""
# Implementation sẽ gọi actual Tardis API
# Ví dụ: https://api.tardis.daily/v1/quotes
pass
def _check_local_cache(
self,
cache_key: str,
freshness: DataFreshness
) -> Optional[CachedResponse]:
"""Check local LRU cache với TTL validation."""
if cache_key not in self.local_cache:
return None
cached = self.local_cache[cache_key]
age = time.time() - cached.cached_at
if age > cached.freshness.ttl_seconds:
del self.local_cache[cache_key]
self._lru_order.remove(cache_key)
return None
return cached
def _store_local_cache(self, cache_key: str, response: CachedResponse):
"""Store in local LRU cache với eviction."""
if cache_key in self.local_cache:
self._lru_order.remove(cache_key)
elif len(self.local_cache) >= self.local_cache_size:
# Evict least recently used
oldest = self._lru_order.pop(0)
del self.local_cache[oldest]
self.local_cache[cache_key] = response
self._lru_order.append(cache_key)
def _update_lru(self, cache_key: str):
"""Update LRU order khi có cache hit."""
self._lru_order.remove(cache_key)
self._lru_order.append(cache_key)
async def _store_redis(self, cache_key: str, response: CachedResponse):
"""Store in Redis với TTL."""
data = {
"data": response.data,
"cached_at": response.cached_at,
"api_cost_saved": response.api_cost_saved,
"hit_count": response.hit_count
}
await self.redis.setex(
cache_key,
response.freshness.ttl_seconds,
json.dumps(data)
)
async def _refresh_stale(
self,
cache_key: str,
endpoint: str,
params: Dict[str, Any],
freshness: DataFreshness,
api_key: str
):
"""Background refresh cho stale data (staleness <= 2x TTL)."""
await asyncio.sleep(freshness.ttl_seconds) # Wait for staleness
try:
new_data = await self._fetch_from_tardis(endpoint, params, api_key)
response = CachedResponse(
data=new_data,
cached_at=time.time(),
freshness=freshness,
api_cost_saved=self._estimate_api_cost(endpoint, params)
)
await self._store_redis(cache_key, response)
except Exception:
pass # Silent fail cho background refresh
def _estimate_api_cost(self, endpoint: str, params: Dict[str, Any]) -> float:
"""Estimate API cost dựa trên endpoint type."""
cost_map = {
"quotes": 0.001,
"historical": 0.005,
"options": 0.002,
"forex": 0.0005,
"news": 0.001
}
return cost_map.get(endpoint.split("/")[-1], 0.001)
async def get_cache_stats(self) -> Dict[str, Any]:
"""Get cache performance statistics."""
local_hits = sum(c.hit_count for c in self.local_cache.values())
# Get Redis stats
keys = await self.redis.keys("tardis:*")
total_cost_saved = 0.0
for key in keys[:100]: # Sample first 100
data = await self.redis.get(key)
if data:
parsed = json.loads(data)
total_cost_saved += parsed.get("api_cost_saved", 0) * parsed.get("hit_count", 1)
return {
"local_cache_size": len(self.local_cache),
"redis_keys": len(keys),
"estimated_cost_saved": total_cost_saved
}
Usage với HolySheep AI cho intelligent caching decisions
async def intelligent_caching_demo():
"""
Demo: Sử dụng HolySheep AI để quyết định freshness tier
thay vì hard-code.
"""
import aiohttp
cache = TardisDataCache()
async with aiohttp.ClientSession() as session:
# Dùng AI để classify request type
classification_prompt = """
Classify this financial data request:
- Symbol: {}
- Requested at: {} (market hours: {})
- Use case: {}
Return JSON: {{"freshness": "realtime|intraday|eod|historical", "reason": "..."}}
""".format(
"AAPL",
time.strftime("%H:%M"),
9 <= int(time.strftime("%H")) <= 16,
"intraday_trading"
)
headers = {
"Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY",
"Content-Type": "application/json"
}
# Call HolySheep API
async with session.post(
"https://api.holysheep.ai/v1/chat/completions",
headers=headers,
json={
"model": "deepseek-v3.2", # $0.42/MTok - cheap for classification
"messages": [{"role": "user", "content": classification_prompt}],
"temperature": 0.1,
"max_tokens": 100
}
) as resp:
result = await resp.json()
# Parse AI response và set appropriate TTL
pass
# Fetch với AI-determined freshness
data, cached = await cache.get_or_fetch(
endpoint="quotes",
params={"symbol": "AAPL"},
freshness=DataFreshness.REALTIME # AI decided this
)
return data
if __name__ == "__main__":
asyncio.run(intelligent_caching_demo())
Tầng 3: Model Inference Optimization - Batch Processing
Việc gọi LLM cho từng request riêng lẻ cực kỳ lãng phí. Với workload tài chính, chúng tôi áp dụng 3 kỹ thuật tối ưu:
- Request batching: Gộp nhiều request cùng loại thành 1 batch
- Prompt caching: Tái sử dụng prompt prefix cho các request tương tự
- Model routing: Chọn model phù hợp cho từng loại task
import asyncio
import aiohttp
import json
import time
from typing import List, Dict, Any, Optional, Callable
from dataclasses import dataclass, field
from collections import defaultdict
import hashlib
@dataclass
class InferenceRequest:
request_id: str
prompt: str
system_prompt: str = ""
max_tokens: int = 500
temperature: float = 0.7
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass
class InferenceResponse:
request_id: str
content: str
model: str
tokens_used: int
latency_ms: float
cost_usd: float
cached: bool = False
class InferenceOptimizer:
"""
Optimizer cho LLM inference với batching và routing.
Hỗ trợ HolySheep AI với giá cực rẻ và latency thấp.
"""
# Model pricing (USD per 1M tokens) - HolySheep 2026
MODEL_PRICING = {
"deepseek-v3.2": {"input": 0.14, "output": 0.28, "latency_p50": 45},
"gpt-4.1": {"input": 2.67, "output": 8.00, "latency_p50": 180},
"claude-sonnet-4.5": {"input": 3.00, "output": 15.00, "latency_p50": 250},
"gemini-2.5-flash": {"input": 0.35, "output": 2.50, "latency_p50": 60},
}
# Model routing rules
TASK_MODEL_MAP = {
"classification": "deepseek-v3.2",
"sentiment": "gemini-2.5-flash",
"analysis": "gpt-4.1",
"summarization": "gemini-2.5-flash",
"extraction": "deepseek-v3.2",
"reasoning": "claude-sonnet-4.5",
}
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
batch_size: int = 10,
batch_timeout_ms: int = 500,
max_concurrent: int = 20
):
self.api_key = api_key
self.base_url = base_url
self.batch_size = batch_size
self.batch_timeout = batch_timeout_ms / 1000
self.semaphore = asyncio.Semaphore(max_concurrent)
# Batch queues per model
self.batches: Dict[str, List[InferenceRequest]] = defaultdict(list)
self.batch_futures: Dict[str, List[asyncio.Future]] = defaultdict(list)
# Cache for prompt prefix
self.prompt_cache: Dict[str, str] = {}
self.prompt_cache_hits = 0
# Metrics
self.metrics = {
"total_requests": 0,
"batched_requests": 0,
"cache_hits": 0,
"total_cost": 0.0,
"total_latency_ms": 0.0
}
async def infer(
self,
prompt: str,
task_type: str = "analysis",
system_prompt: str = "",
use_cache: bool = True,
**kwargs
) -> InferenceResponse:
"""Single inference request với automatic routing."""
# Generate request ID
request_id = hashlib.sha256(
f"{prompt}:{time.time()}".encode()
).hexdigest()[:12]
request = InferenceRequest(
request_id=request_id,
prompt=prompt,
system_prompt=system_prompt,
**kwargs
)
# Check prompt cache
cache_key = self._get_cache_key(system_prompt)
if use_cache and cache_key in self.prompt_cache:
request.system_prompt = self.prompt_cache[cache_key]
self.prompt_cache_hits += 1
self.metrics["cache_hits"] += 1
elif use_cache:
self.prompt_cache[cache_key] = system_prompt
# Route to appropriate model
model = self.TASK_MODEL_MAP.get(task_type, "deepseek-v3.2")
# Try to batch with similar requests
batched = await self._try_batch(request, model)
if batched:
return batched
# Direct inference if batching not beneficial
return await self._direct_inference(request, model)
async def _try_batch(
self,
request: InferenceRequest,
model: str
) -> Optional[InferenceResponse]:
"""Attempt to batch request with similar ones."""
self.batches[model].append(request)
future = asyncio.get_event_loop().create_future()
self.batch_futures[model].append(future)
# If batch is full, process immediately
if len(self.batches[model]) >= self.batch_size:
return await self._process_batch(model)
# Otherwise wait for timeout or more requests
asyncio.create_task(self._batch_timeout_handler(model))
# Wait for result
return await asyncio.wait_for(future, timeout=self.batch_timeout)
async def _batch_timeout_handler(self, model: str):
"""Process batch after timeout."""
await asyncio.sleep(self.batch_timeout)
if self.batches[model]:
await self._process_batch(model)
async def _process_batch(self, model: str) -> Optional[InferenceResponse]:
"""Process a batch of requests."""
if not self.batches[model]:
return None
requests = self.batches[model]
futures = self.batch_futures[model]
self.batches[model] = []
self.batch_futures[model] = []
# Combine prompts with separators
combined_prompt = self._combine_batch_prompts(requests)
start_time = time.time()
async with self.semaphore:
try:
response = await self._call_api(
model=model,
messages=[
{"role": "system", "content": requests[0].system_prompt or "You are a helpful assistant."},
{"role": "user", "content": combined_prompt}
],
max_tokens=max(r.max_tokens for r in requests)
)
except Exception as e:
# Fail all futures in batch
for f in futures:
if not f.done():
f.set_exception(e)
return None
# Parse combined response
responses = self._parse_batch_response(response, requests)
latency_ms = (time.time() - start_time) * 1000
total_cost = self._calculate_cost(model, response)
# Resolve futures
for req, resp in zip(requests, responses):
cost = total_cost / len(requests) # Proportional cost
full_response = InferenceResponse(
request_id=req.request_id,
content=resp,
model=model,
tokens_used=response.get("usage", {}).get("total_tokens", 0) // len(requests),
latency_ms=latency_ms,
cost_usd=cost,
cached=False
)
self.metrics["total_requests"] += 1
self.metrics["batched_requests"] += 1
self.metrics["total_cost"] += cost
self.metrics["total_latency_ms"] += latency_ms
# Find and resolve corresponding future
for i, r in enumerate(requests):
if r.request_id == req.request_id:
futures[i].set_result(full_response)
break
return responses[0] if responses else None
async def _direct_inference(
self,
request: InferenceRequest,
model: str
) -> InferenceResponse:
"""Direct inference without batching."""
start_time = time.time()
async with self.semaphore:
response = await self._call_api(
model=model,
messages=[
{"role": "system", "content": request.system_prompt or "You are a helpful assistant."},
{"role": "user", "content": request.prompt}
],
max_tokens=request.max_tokens,
temperature=request.temperature
)
latency_ms = (time.time() - start_time) * 1000
cost = self._calculate_cost(model, response)
self.metrics["total_requests"] += 1
self.metrics["total_cost"] += cost
self.metrics["total_latency_ms"] += latency_ms
return InferenceResponse(
request_id=request.request_id,
content=response["choices"][0]["message"]["content"],
model=model,
tokens_used=response.get("usage", {}).get("total_tokens", 0),
latency_ms=latency_ms,
cost_usd=cost,
cached=False
)
async def _call_api(
self,
model: str,
messages: List[Dict],
**kwargs
) -> Dict[str, Any]:
"""Call HolySheep AI API."""
async with aiohttp.ClientSession() as session:
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages,
**kwargs
}
async with session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload
) as resp:
if resp.status != 200:
error = await resp.text()
raise RuntimeError(f"API error: {error}")
return await resp.json()
def _combine_batch_prompts(self, requests: List[InferenceRequest]) -> str:
"""Combine multiple prompts into one with separators."""
combined = []
for i, req in enumerate(requests):
combined.append(f"--- Request {i+1} ---\n{req.prompt}")
return "\n\n".join(combined)
def _parse_batch_response(
self,
response: Dict[str, Any],
requests: List[InferenceRequest]
) -> List[str]:
"""Parse combined response back into individual responses."""
content = response["choices"][0]["message"]["content"]
# Simple parsing - assume delimiter-based
parts = content.split("--- Request ")
results = []
for i, req in enumerate(requests):
if i == 0 and parts[0].strip():
results.append(parts[0].strip())
elif len(parts) > i:
# Extract response after delimiter
section = parts[i].split("---")[0].strip()
results.append(section)
else:
results.append("")
return results[:len(requests)]
def _calculate_cost(self, model: str, response: Dict[str, Any]) -> float:
"""Calculate cost for API call."""
pricing = self.MODEL_PRICING.get(model, {"input": 0.5, "output": 1.0})
usage = response.get("usage", {})
input_tokens = usage.get("prompt_tokens", 0)
output_tokens = usage.get("completion_tokens", 0)
cost = (input_tokens / 1_000_000) * pricing["input"]
cost += (output_tokens / 1_000_000) * pricing["output"]
return cost
def _get_cache_key(self, system_prompt: str) -> str:
"""Generate cache key for system prompt."""
return hashlib.sha256(system_prompt.encode()).hexdigest()[:32]
def get_metrics(self) -> Dict[str, Any]:
"""Get optimizer metrics."""
avg_latency = (
self.metrics["total_latency_ms"] / self.metrics["total_requests"]
if self.metrics["total_requests"] > 0 else 0
)
return {
**self.metrics,
"cache_hit_rate": self.prompt_cache_hits / max(1, self.metrics["total_requests"]),
"avg_latency_ms": avg_latency,
"cost_per_request": self.metrics["total_cost"] / max(1, self.metrics["total_requests"])
}
Benchmark comparison
async def benchmark_inference():
"""Benchmark: Batch vs Direct inference cost comparison."""
optimizer = InferenceOptimizer(
api_key="YOUR_HOLYSHEEP_API_KEY",
batch_size=5,
batch_timeout_ms=100
)
test_prompts = [
f"Analyze AAPL stock performance for day {i}: trends, volume, key indicators"
for i in range(20)
]
# Direct inference simulation
direct_start = time