The One Tweak That Slashed Your LLM Bill
Large language model costs can quickly spiral out of control for businesses relying on AI-powered applications. Industry experts have identified a straightforward solution that dramatically reduces these expenses without sacrificing performance. Implementing semantic caching with Redis has proven to cut LLM bills by significant margins across organizations of all sizes.
Adopt Semantic Cache with Redis
Using Redis for our semantic cache produced the quickest $ROI of any of the adjustments that we made. Rather than every request being sent directly to the OpenAI API when a customer submits a request, we first compared the incoming request against a database that contains the vector representations of all previous requests that we have answered. As an example, we had 40% of our repetitive traffic intercepted through this process, which allowed us to reduce the cost of our monthly API bill by 35% and decrease our response latency on requests that had been cached from about 3 seconds to less than 100 milliseconds. We were able to verify the financial savings through the correlation between our reduced token consumption and our metrics collected through Datadog APM, clearly demonstrating that cost savings were not at the expense of the accuracy of responses or customer satisfaction.
Route Easy Queries to Smaller Models
Using a smaller model for everyday tasks can drop cost while keeping output good enough. A simple routing step can send easy requests to the small model and pass tricky ones to a larger one. Clear rules, like checking input length or keywords, help the system choose well.
Quality stays steady when the setup can fall back to the big model on hard cases. Speed often improves, which makes users happier and reduces retries. Add a routing step and try a compact model on routine work today.
Trim Prompts and System Messages
Short, tight prompts and system messages cut tokens and cost right away. A trimmed template that states role, task, and output format in few words guides the model just as well. Removing filler words and repeated rules reduces drift and speeds replies.
A token counter can guard the limit and flag long prompts before they go out. Clear examples can be short too, using one small case instead of many. Audit your prompts and rewrite them in plain, brief language today.
Batch Similar Requests for Higher Throughput
Sending many small requests one by one adds extra overhead that costs money. Batching groups similar requests into a single call so shared work is done once. Shared context, like the same rules or the same short passage, goes in only one time.
A small queue can hold tasks and release them in safe batch sizes to avoid token limits. Throughput rises, and rates and timeouts hit less often. Build a simple queue and start batching similar requests today.
Enforce Strict Token Caps per Job
A strict token budget controls both input and output size and keeps spend predictable. Each task type can get a set cap, and the system can trim or summarize inputs that exceed it. The model can also be told a clear max length for the answer to stop long rambles.
If a request needs more room, an approval step can raise the cap for that one case. Dashboards then show steady use and reveal waste fast. Define token caps per task and enforce them with guards today.
Use Retrieval to Minimize Context Size
Retrieval cuts cost by sending only the few lines that matter, not whole files. A small search step finds the top chunks of data tied to the question. Those chunks become the short context, so the prompt stays lean and focused.
When data changes, the search stays up to date and the context stays fresh without long prompts. Caching repeated answers and chunks saves even more tokens over time. Add a lightweight retrieval step to your workflow and measure the drop in tokens today.