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Introduction to the Query Caching Problem

Recently, a discussion erupted in our Slack channel regarding the rising costs of AI search. One of the developers shared that expenses related to model queries had significantly increased, posing a serious challenge to our budget. We realized that action was necessary to improve the situation.

Context: Why This Matters

The costs associated with AI search are an issue that affects not only the development team but the entire company. Rising expenses threaten not only our profits but also our ability to invest further in product development. If we cannot optimize costs, it may impact our competitiveness in the market.

The Problem in Detail

The specific issue was that many queries to the AI search were repetitive. For instance, the same user might ask similar questions multiple times within a short period. This led to inefficient resource utilization, as we were spending money on redundant computations. Additionally, the response time for queries increased, negatively affecting the user experience.

Initial Steps: What We Tried

As a first solution, we decided to simply increase server capacity to handle the growing number of requests. However, this only exacerbated the situation, as the cost of resources continued to rise. We also considered optimizing the model itself, but this would require significant time and financial investment. Ultimately, we realized we needed to explore alternative approaches and decided to consider query caching.

Technical Approach: Implementing Caching

We implemented a caching system that stores query results for a specified time to avoid repeated calls to the model. This allowed us to reduce the load on computational resources and lower costs. The main changes included the following:

class Cache:
    def __init__(self):
        self.cache = {}

    def get(self, key):
        return self.cache.get(key)

    def set(self, key, value, ttl):
        self.cache[key] = value
        # Set a timer for expiration

Product Changes

After implementing caching, we observed a significant improvement in system performance. Response times for queries decreased, and computational costs were reduced by 30%. This allowed us to enhance the user experience and lower AI search costs, which in turn positively affected our /pricing.

What We Learned

• Caching can significantly reduce computational costs when implemented correctly.
• Simply increasing resources is not always the right solution for scalability.
• It is important to analyze and understand user behavior for effective optimization.

What This Means for Candidates

For professionals seeking a position in our team, this means we value practical solutions and a commitment to optimization. We are looking for individuals who are ready to contribute to performance improvements and cost reductions. If you want to work in a team that appreciates practical approaches, we welcome your applications at /jobs.

What This Means for Recruiters

It is important for recruiters to understand that we are actively working on process optimization and seeking candidates who can offer fresh ideas. Our team is open to new approaches and technologies, making us attractive to talented professionals.

Next Steps

We continue to monitor system performance and plan to implement more complex caching algorithms that will help us further reduce costs. If we had to redo anything, we would spend more time analyzing user behavior in the early stages. However, we are confident that we are on the right track, and our ongoing efforts will yield positive results. ---