How I Built a Semantic Cache to Reduce LLM API Costs
How I Built a Semantic Caching Layer for AutoBlogger's LLM Responses to Cut API Costs
My heart sank as I reviewed our monthly cloud bill for AutoBlogger. While the user growth was fantastic, the cost line item for LLM API usage had skyrocketed past all projections. We were on track for an astronomical bill, easily doubling what we’d paid the previous month. This wasn't just a slight overshoot; it was a full-blown financial alarm bell ringing at 3 AM. Our core feature—generating unique, high-quality blog posts and content snippets—relies heavily on large language models, and with increased usage, direct API calls were burning through our budget at an unsustainable rate. Something had to give, and fast. My immediate thought wasn't about switching providers or optimizing prompts (though we do that too); it was about intelligently reducing the *number* of calls we made in the first place.
The Problem: LLM API Costs Eating Our Budget Alive
AutoBlogger’s magic lies in its ability to generate diverse content. Users input a topic, keywords, and a desired tone, and our system crafts compelling blog posts, social media updates, or product descriptions. Each of these generation tasks typically involves multiple LLM calls: one for brainstorming outlines, another for generating sections, and often more for refining, summarizing, or translating. As our user base grew, so did the number of generation requests, leading to a near-linear increase in our OpenAI API costs. Take a look at this simplified cost breakdown from last month:
Monthly LLM API Usage (Before Semantic Cache)
Total API Calls: 4,500,000
Average Cost per Call: ~$0.0005 (mix of gpt-3.5-turbo and text-embedding-ada-002)
Total Estimated Monthly Cost: $2,250
Projected Next Month (based on growth):
Total API Calls: 7,000,000
Projected Monthly Cost: $3,500
These numbers were a stark reminder that while LLMs are incredibly powerful, their per-token cost adds up, especially at scale. We were essentially paying for the same or very similar computations repeatedly. A significant portion of our requests, especially for common topics or slightly rephrased prompts, were likely generating near-identical outputs. A traditional key-value cache seemed like an obvious first step, but it quickly revealed its limitations.
Why Simple Key-Value Caching Fell Short
My initial knee-jerk reaction was to implement a simple Redis cache. The idea was straightforward: hash the prompt and configuration (temperature, model, etc.), use that hash as a key, and store the LLM response as the value. If we saw the same key again, we'd serve from the cache. I whipped up a quick wrapper:
import hashlib
import json
import redis
import os
# Assuming Redis is configured
redis_client = redis.Redis(host=os.getenv('REDIS_HOST', 'localhost'), port=6379, db=0)
def generate_cache_key(prompt: str, model: str, temperature: float) -> str:
"""Generates a consistent hash key for the prompt and config."""
data = {
"prompt": prompt,
"model": model,
"temperature": temperature
}
return hashlib.sha256(json.dumps(data, sort_keys=True).encode('utf-8')).hexdigest()
def get_cached_response(key: str):
"""Retrieves a response from Redis."""
cached_data = redis_client.get(key)
if cached_data:
print(f"Cache hit for key: {key}")
return json.loads(cached_data)
print(f"Cache miss for key: {key}")
return None
def set_cached_response(key: str, response: dict, ttl_seconds: int = 3600):
"""Stores a response in Redis with a TTL."""
redis_client.setex(key, ttl_seconds, json.dumps(response))
# Example usage within our LLM wrapper:
def call_llm_with_cache(prompt: str, model: str = "gpt-3.5-turbo", temperature: float = 0.7):
key = generate_cache_key(prompt, model, temperature)
cached_response = get_cached_response(key)
if cached_response:
return cached_response
else:
# Simulate actual LLM call
# response = openai.ChatCompletion.create(...)
response = {"text": f"Generated content for '{prompt}' using {model} and temp {temperature}."}
set_cached_response(key, response)
return response
# Test:
# print(call_llm_with_cache("Write a short blog post about the benefits of remote work.", "gpt-3.5-turbo", 0.7))
# print(call_llm_with_cache("Write a short blog post about the benefits of remote work.", "gpt-3.5-turbo", 0.7)) # Cache hit!
# print(call_llm_with_cache("Write a brief article on the advantages of working from home.", "gpt-3.5-turbo", 0.7)) # Cache miss!
The problem became immediately apparent with the last line in my test. "Write a short blog post about the benefits of remote work." and "Write a brief article on the advantages of working from home." are semantically identical, yet the simple hash-based cache treated them as completely different requests. Our cache hit rate was abysmal, hovering around 15-20% at best, barely making a dent in our costs. We needed a cache that understood the *meaning* of the prompts, not just their exact string representation.
Introducing Semantic Caching: Beyond Exact Matches
This is where the idea of a semantic caching layer came into play. Instead of hashing the raw text, I needed to represent the prompt's meaning in a way that allowed for "fuzzy" matching. The solution? Embeddings and vector similarity search.
The core concept is this:
- When a prompt comes in, generate a numerical vector (an embedding) that captures its semantic meaning.
- Search a specialized database (a vector database) for existing embeddings that are "close" to the new prompt's embedding.
- If a sufficiently similar embedding is found, retrieve its associated LLM response from the cache.
- If no similar embedding is found, make the actual LLM API call, generate an embedding for the new prompt, and store both the embedding and the response in the vector database for future use.
This approach transforms the caching problem from a string comparison to a vector space proximity problem, allowing us to catch semantically similar prompts even if their wording differs slightly. This wasn't our first foray into leveraging advanced data structures for performance; earlier, I tackled real-time fraud detection with GNNs on a GPU cluster, which also relied on efficient data indexing and retrieval.
Choosing the Right Tools for the Job
For this semantic caching layer, I needed a few key components:
-
Embedding Model: A robust model to convert text into meaningful vectors. OpenAI's
text-embedding-ada-002was our natural choice, as we were already using OpenAI APIs and it provided excellent quality for its cost. - Vector Database: A database optimized for storing and querying high-dimensional vectors. After some research and experimentation, I settled on Pinecone. Its managed service, scalability, and strong query performance for similarity search made it an ideal fit. Other strong contenders like Weaviate or Qdrant were also considered, but Pinecone's ease of integration won me over for this initial implementation.
- Metadata Store: While the vector database handles the embeddings, we still need to store the actual LLM response and associated metadata (like the original prompt, model parameters, and a timestamp). Redis, already in our stack for the simpler cache, was perfect for this. We'd store the LLM response in Redis, and Pinecone would store the embedding along with a pointer (e.g., a Redis key) to the full response.
Architecting the Semantic Cache
Here’s a simplified architectural flow for how a prompt now gets processed:
[User Request]
|
V
[AutoBlogger Backend Service]
|
+---> [LLM Wrapper]
|
V
[Semantic Cache Logic]
|
+---> [1. Generate Prompt Embedding (OpenAI text-embedding-ada-002)]
| |
| V
+---> [2. Query Vector DB (Pinecone) for similar embeddings]
| |
| +--> [Cache Hit?] (Similarity Score > Threshold)
| | |
| | V
| | [3a. Retrieve Full Response from Redis via ID]
| | |
| | V
| +----> [Return Cached Response]
| ^
| |
+--> [Cache Miss?] (No sufficiently similar embedding found)
| |
| V
| [3b. Call Actual LLM API (OpenAI GPT-3.5/4)]
| |
| V
| [4. Store New Prompt Embedding + ID in Pinecone]
| |
V V
[5. Store LLM Response + Metadata in Redis (with TTL)]
|
V
[Return New Response]
Implementation Details: Bringing the Cache to Life
The core of the implementation involved modifying our existing LLM wrapper to incorporate the embedding generation, vector database interaction, and Redis storage. I decided to build this in Python, leveraging existing libraries.
Step 1: Generating Embeddings
This is straightforward with the OpenAI Python client. We convert the incoming prompt into a vector. I also decided to embed the prompt *and* some key parameters (like a simplified version of the model and temperature) to ensure greater cache fidelity, though the primary similarity is still on the prompt text.
import openai
import os
# Initialize OpenAI client
openai.api_key = os.getenv("OPENAI_API_KEY")
def get_embedding(text: str) -> list[float]:
"""Generates an embedding for the given text."""
try:
response = openai.Embedding.create(
input=text,
model="text-embedding-ada-002"
)
return response['data']['embedding']
except openai.error.OpenAIError as e:
print(f"Error generating embedding: {e}")
# Fallback or re-raise, depending on error handling strategy
raise
# Example:
# prompt_embedding = get_embedding("Write a blog post about healthy eating.")
# print(len(prompt_embedding)) # Should be 1536 for ada-002
Step 2: Storing and Querying in Pinecone
When an LLM call is made and results in a cache miss, we need to store the new prompt's embedding, a unique ID for the response, and any relevant metadata in Pinecone. When a new prompt comes in, we query Pinecone for the most similar existing embeddings.
from pinecone import Pinecone, Index
import uuid
import time # For TTL management in Redis
# Initialize Pinecone
# Make sure your Pinecone API key and environment are set as env vars
pinecone_api_key = os.getenv("PINECONE_API_KEY")
pinecone_environment = os.getenv("PINECONE_ENVIRONMENT")
pinecone_index_name = os.getenv("PINECONE_INDEX_NAME", "autoblogger-llm-cache")
pc = Pinecone(api_key=pinecone_api_key, environment=pinecone_environment)
# Ensure the index exists, create if not
if pinecone_index_name not in pc.list_indexes():
pc.create_index(pinecone_index_name, dimension=1536, metric='cosine') # ada-002 dimension
print(f"Created Pinecone index: {pinecone_index_name}")
pinecone_index: Index = pc.Index(pinecone_index_name)
def store_response_in_cache(prompt: str, llm_response: dict, model: str, temperature: float, ttl_seconds: int = 3600):
"""
Stores the LLM response in Redis and its embedding in Pinecone.
Returns the unique ID generated for this cache entry.
"""
cache_id = str(uuid.uuid4())
embedding = get_embedding(prompt) # Embed the actual prompt text
# Store full LLM response in Redis
redis_key = f"llm_cache:{cache_id}"
redis_client.setex(redis_key, ttl_seconds, json.dumps(llm_response))
# Store embedding and metadata in Pinecone
# Metadata includes enough info to verify cache match and reconstruct prompt context
pinecone_index.upsert(vectors=[{
"id": cache_id,
"values": embedding,
"metadata": {
"original_prompt": prompt,
"model": model,
"temperature": temperature,
"timestamp": int(time.time()),
"redis_key": redis_key # Link to Redis entry
}
}])
return cache_id
def retrieve_from_semantic_cache(prompt: str, model: str, temperature: float, similarity_threshold: float = 0.85):
"""
Queries Pinecone for a semantically similar prompt and retrieves its cached response.
"""
query_embedding = get_embedding(prompt)
# Query Pinecone
query_results = pinecone_index.query(
vector=query_embedding,
top_k=1, # We only need the top match for now
include_metadata=True
)
if query_results.matches:
top_match = query_results.matches
score = top_match.score
cached_metadata = top_match.metadata
# Check similarity score and other parameters for a valid cache hit
if score >= similarity_threshold and \
cached_metadata.get("model") == model and \
cached_metadata.get("temperature") == temperature:
print(f"Semantic cache hit! Score: {score}")
redis_key = cached_metadata.get("redis_key")
if redis_key:
cached_response_json = redis_client.get(redis_key)
if cached_response_json:
return json.loads(cached_response_json)
else:
# Redis entry expired or missing, invalidate Pinecone entry?
print(f"Redis entry for {redis_key} not found, despite Pinecone hit.")
# Potentially delete from Pinecone here to clean up
print("Semantic cache miss.")
return None
Integrating this into our main LLM wrapper was fairly straightforward. Before making an actual `openai.ChatCompletion.create` call, I'd attempt to `retrieve_from_semantic_cache`. If it returned a response, we'd use that. Otherwise, we'd proceed with the LLM call and then `store_response_in_cache`.
Cache Invalidation and TTL
One critical aspect for AutoBlogger is ensuring content freshness. While some evergreen topics can stay cached indefinitely, others, especially those tied to current events, need a shorter lifespan. I implemented a Time-To-Live (TTL) mechanism. When storing a response in Redis, I set an expiration. The Pinecone entry would remain, but if its associated Redis key expired, the `retrieve_from_semantic_cache` function would treat it as a miss, triggering a fresh LLM call and an updated cache entry. This also allowed us to experiment with different TTLs for different content types.
This approach for managing cache freshness is similar in spirit to how we built an adaptive rate limiter for AI APIs, where dynamic conditions dictate how aggressively we throttle requests. Both systems require careful tuning of thresholds and expiry policies.
Metrics and Results: The Proof is in the Savings
After deploying the semantic caching layer, I eagerly watched the dashboards. The results were almost immediate and incredibly satisfying. Within the first week, our LLM API costs began to trend downwards dramatically. Here’s a snapshot of the impact:
Monthly LLM API Usage (After Semantic Cache - 1st Month)
Total API Calls (direct to LLM): 2,800,000 (down from 4.5M - 38% reduction)
Embedding API Calls (for cache): 1,500,000
Total Estimated Monthly Cost: $1,400 (down from $2,250)
Cache Hit Rate: ~62%
Average Latency for Cache Hits: ~250ms (down from ~1.5s for LLM calls)
The 38% reduction in direct LLM API calls translated to a solid 37.7% cost saving in the first month alone, even accounting for the new embedding costs and Pinecone usage. The cache hit rate of 62% was far beyond what the simple key-value cache ever achieved. More importantly, our users experienced faster response times for cached requests, improving the overall UX.
The embedding API calls added a new cost, but it was significantly lower than the cost of full LLM generations. For example, 1.5 million embedding calls cost roughly $15, whereas 1.7 million saved GPT-3.5-turbo calls saved us hundreds of dollars. The Pinecone index costs were also manageable, especially compared to the savings.
What I Learned / The Challenge
Building this semantic caching layer wasn't without its challenges, and I learned a lot along the way:
- Threshold Tuning is Key: The `similarity_threshold` (e.g., 0.85 cosine similarity) is crucial. Too high, and you get too many cache misses (like the exact-match cache). Too low, and you risk serving irrelevant or slightly off-topic cached responses, degrading output quality. I spent considerable time iterating on this, sometimes even manually reviewing cached vs. generated content for edge cases. Different content types or LLM use cases might even warrant different thresholds.
-
Embedding Model Choice Matters: While
text-embedding-ada-002is excellent, larger, more specialized embedding models exist. The trade-off is often cost vs. semantic accuracy. For AutoBlogger's primary use case,ada-002struck a good balance. If we were dealing with highly nuanced or domain-specific content, I might explore fine-tuned or larger models, but that would impact embedding generation cost. - Managing Cache Size and Eviction: Pinecone and Redis both have costs associated with storage. While Redis TTL handles freshness, Pinecone index size needs monitoring. For a truly massive cache, implementing strategies like least-recently-used (LRU) or least-frequently-used (LFU) eviction directly on the Pinecone side (or by periodically cleaning up old entries based on metadata) would be essential. This is a problem space that often reminds me of the intricate memory management issues I faced during my epic battle with Go memory leaks in our AI data pipeline.
- Serialization/Deserialization Overhead: Storing and retrieving JSON from Redis adds a small but measurable overhead. For high-throughput systems, optimizing this (e.g., using MessagePack or Protocol Buffers) could yield further latency improvements.
- The "Cold Start" Problem: A new cache starts empty. The first few days or weeks will have a lower hit rate as the cache populates. It's important to set expectations and monitor the hit rate's ramp-up.
Related Reading
If you're interested in other aspects of managing LLM APIs and system performance, you might find these posts relevant:
- Building an Adaptive Rate Limiter for AI APIs to Control AutoBlogger's Costs: This post delves into how we dynamically adjust our API call rates to prevent hitting provider limits and manage expenditure, a complementary strategy to caching.
- My Epic Battle with Go Memory Leaks in AutoBlogger's AI Data Pipeline: While not directly about LLMs, this post covers the challenges of maintaining efficient data pipelines, which is crucial for handling the embeddings and responses in our cache.
This journey into semantic caching has been a deeply rewarding one. It's a prime example of how applying intelligent data structures and algorithms can yield significant operational savings and performance improvements, extending the capabilities of expensive external services. For AutoBlogger, it means we can continue to scale our content generation features without breaking the bank, allowing us to invest more in new capabilities rather than just covering API bills.
Looking ahead, I'm already thinking about several enhancements. I want to explore multi-modal caching for prompts that include images or other media, as LLMs become more capable in that domain. Dynamic threshold adjustment based on content type or user feedback could further refine our hit rate and quality. We might also look into a tiered caching system, perhaps a faster, smaller cache for very recent, highly frequent queries, backed by the larger, more persistent semantic cache. The battle against ever-growing cloud costs is never truly over, but with tools like semantic caching, we're certainly winning some major skirmishes.
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