Cutting LLM Expenses and Response Times by 70% Through Bifrost's Semantic Caching

Source: Dev.to

When Deploying Large Language Models in Production

Development teams encounter an “Iron Triangle” of competing priorities:

• Expense
• Speed
• Output quality

While quality standards are essential, expense and speed grow proportionally with user adoption, creating mounting challenges. Each interaction with API providers such as OpenAI, Anthropic, or Google Vertex carries both monetary and temporal costs that can span multiple seconds. Applications serving high volumes of traffic—especially those employing Retrieval‑Augmented Generation or customer‑facing chatbots—suffer primarily from duplicate processing. End users routinely pose identical or nearly identical questions, leading to wasteful and costly repeated computations.

The answer isn’t simply deploying faster models; it’s implementing more intelligent infrastructure. Semantic Caching marks a fundamental departure from conventional key‑value storage systems, allowing AI gateways to comprehend query meaning rather than merely matching text strings.

Overview of Semantic Caching in Bifrost

This piece examines the technical design of Semantic Caching as implemented in Bifrost, Maxim AI’s performance‑optimized AI gateway. We’ll explore how this middleware layer can slash LLM running costs and delays by as much as 70 %, explain the underlying vector‑based similarity matching technology, and demonstrate how to set up Bifrost for high‑throughput production environments.

Bifrost – The Fastest Way to Build AI Applications That Never Go Down

Bifrost is a high‑performance AI gateway that unifies access to 15+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, and more) through a single OpenAI‑compatible API. Deploy in seconds with zero configuration and get automatic failover, load balancing, semantic caching, and enterprise‑grade features.

Quick Start

Go from zero to production‑ready AI gateway in under a minute.

Step 1 – Start Bifrost Gateway

# Install and run locally
npx -y @maximhq/bifrost

# Or use Docker
docker run -p 8080:8080 maximhq/bifrost

Step 2 – Configure via Web UI

# Open the built‑in web interface
open http://localhost:8080

Step 3 – Make Your First API Call

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Hello, Bifrost!"}]
  }'

That’s it! Your AI gateway is running with a web interface for visual configuration, real‑time monitoring, and more.

View on GitHub

Why Semantic Caching Matters

The Limits of Traditional Caching

Conventional caching systems (Redis, Memcached) rely on exact string matches or hash functions. For a request like GET /product/123, the cache looks for that exact key. If found, data is returned instantly.

Human communication, however, isn’t that rigid. Consider a customer‑service chatbot for an online retailer. Three separate customers might ask:

1. “What is your return policy?”
2. “Can I return an item I bought?”
3. “How do I send back a product?”

Standard caching treats these as three completely different queries, causing three independent API calls to the LLM service. Each call burns tokens (cost) and incurs latency, even though all three questions seek the same information.

High‑traffic systems waste massive resources on this duplication. Production data from Maxim’s Observability platform shows that industry‑specific applications exhibit substantial semantic repetition in user queries. Relying solely on exact‑match caching misses enormous optimization opportunities.

How Semantic Caching Works

Semantic Caching handles linguistic variation by leveraging vector embeddings and similarity matching. Instead of storing the literal query text, the system preserves the query’s underlying meaning.

When a request arrives at the Bifrost AI Gateway, the following steps occur:

1. Creating Embeddings – The prompt is processed by an embedding model (e.g., OpenAI’s text-embedding-3-small or an open‑source alternative), converting the text into a dense numerical vector representing its semantic content.
2. Searching Vectors – This vector is compared against a database containing embeddings from previous queries.
3. Computing Similarity – Distance between the new query vector and existing vectors is measured using algorithms such as Cosine Similarity or Euclidean Distance.
4. Checking Thresholds – If a stored vector falls within the configured similarity boundary (e.g., cosine similarity > 0.95), the system registers a Cache Hit.
5. Fetching Results – The cached answer linked to the matching vector is returned immediately, bypassing the LLM provider.

If similarity scores fall below the threshold (Cache Miss), the request proceeds to the LLM provider (e.g., GPT‑4, Claude 3.5 Sonnet). The generated response is then embedded and added to the cache for future requests.

Performance Gains

• Standard LLM calls (e.g., GPT‑4o with moderate context) typically require 800 ms – 3 s depending on response length and provider capacity.
• Embedding generation + vector lookup usually finishes in 50 ms – 100 ms.

Thus, cache hits achieve 90 % – 95 % latency reduction. When this applies to ~70 % of traffic (common for support applications), overall system responsiveness improves dramatically, delivering noticeably faster user experiences.

Bifrost as a Seamless Middleware

Bifrost acts as a drop‑in replacement for standard LLM API endpoints, requiring no changes to application code to activate advanced capabilities like semantic caching. It operates as middleware between your application and the LLM provider, handling:

• Provider routing & failover
• Load balancing
• Automatic semantic caching
• Real‑time observability

Getting Started with Semantic Caching in Bifrost

1. Enable Semantic Caching in the Bifrost UI (Settings → Caching → Semantic).
2. Select an Embedding Model (e.g., text-embedding-3-small).
3. Configure Similarity Threshold (default 0.95; adjust based on domain specificity).
4. Set Cache TTL to control how long cached responses remain valid.
5. Monitor Hit/Miss Rates via the built‑in dashboard to fine‑tune parameters.

TL;DR

• Problem: Duplicate LLM calls inflate cost and latency.
• Solution: Semantic caching stores meaning rather than exact text.
• Result: Up to 70 % reduction in LLM spend and 90 %+ latency improvement for typical support workloads.
• Tool: Bifrost provides turnkey, zero‑code integration for production‑grade semantic caching.

Deploy Bifrost today and let intelligent caching do the heavy lifting for your LLM‑driven applications.

Semantic Caching in Bifrost

Connecting your application with 12+ providers? Activating Semantic Caching in Bifrost is done through the gateway configuration. Unlike custom solutions that require a separate vector‑database (e.g., Pinecone, Milvus) and an embedding pipeline, Bifrost bundles these components directly into request processing.

Configuration Overview

Threshold Tuning

Tip: Coding assistants usually need a strict threshold, while general chatbots can tolerate a looser one.

Multimodal Support

Bifrost’s Unified Interface handles text, images, and audio. Semantic caching currently focuses on text, but as embedding models improve (e.g., image‑to‑vector), the same concepts will extend to multimodal content—preventing redundant, expensive image‑analysis calls.

Business Case: Cost Savings

LLM providers charge per input and output token. RAG architectures often add large retrieved contexts, inflating input costs.

Example: Enterprise Knowledge Base

After Deploying Bifrost

Result: 40 % reduction in direct API expenses.
Systems with higher redundancy (FAQ bots, frontline support) often see 60‑70 % savings.

Ongoing Management

Semantic caching isn’t “set‑and‑forget.” Continuous monitoring ensures the cache stays effective and safe.

Key Metrics to Watch

• Cache Hit Rate – Low rates may indicate thresholds are too strict or queries are too diverse.
• Latency Distribution – Compare p95 latency for hits vs. misses.
• User‑Feedback Signals – Negative reactions to cached answers flag problematic hits.

Observability with Maxim

• Request Tracing – Identify whether a response came from gpt‑4 or bifrost‑cache.
• Human Evaluation – Remove offending cache entries or adjust thresholds for specific query types.
• Cache Misses – Treat them as novel queries; feed them into Maxim’s Data Engine to build high‑quality fine‑tuning datasets via the Experimentation Playground.

Governance & Security

Data‑Privacy Concerns

If User A asks a sensitive question and User B later asks a similar one, we must prevent User B from receiving User A’s cached response (which may contain PII).

Bifrost Solutions

• Segmentation – Cache keys can embed tenant IDs or user IDs, ensuring semantic matches stay within proper boundaries.
• Multi‑Tenant Safety – Enables SaaS platforms to use caching without cross‑customer data leakage.

Additional Security Features

• Vault Support – Secure API‑key management for compliance‑heavy environments.

Why Semantic Caching Matters

As AI moves from prototype to production, the focus shifts from “does it work?” to sustainability:

• Cost – Frontier models and token‑generation latency become major blockers at scale.
• Performance – Eliminating duplicate requests yields near‑instant answers for frequent queries.
• Throughput – Frees capacity for truly novel requests.

Result: Up to 70 % reduction in duplicate API calls → major cost savings, faster responses, higher overall throughput.

Take Action

Don’t let redundant queries drain your budget or degrade user experience.

Discover the Maxim stack – a complete suite of evaluation, observability, and governance tools that, together with Bifrost, gives you a reliable, economical, high‑performing AI foundation.

Get Started

• Visit the Maxim AI website.
• Follow the quick‑start guide to enable semantic caching on your gateway.
• Configure thresholds, monitor metrics, and iterate for optimal results.