Traditional caching breaks the moment someone rephrases a question. A user asks "What are your business hours?" and gets a response. Five minutes later, another user asks "When are you open?;" semantically identical, but different words; and the cache misses entirely. Another expensive API call for the same information.; This is the hidden tax on AI applications. Teams building chatbots and customer support systems watch LLM costs balloon because standard caching only catches exact string matches.

Bifrost, an open-source LLM gateway, solves this with semantic caching—understanding meaning rather than matching text. Early production deployments show cost reductions between 40-60%, with some use cases seeing savings up to 85%.

The Exact-Match Problem

Most caching systems work like this: hash the request, check if that exact hash exists in the cache, serve the cached response if it does. This works brilliantly for static assets or database queries where requests are identical.

But LLM requests are rarely identical. Consider these variations:

• "What’s the refund policy?"
• "How do I get a refund?"
• "Can I return this product?"
• "What is your return policy"

A human immediately recognizes these as asking the same thing. Traditional caching sees four completely different requests and makes four separate API calls; each costing $0.002-0.03 depending on the model and token count.

For an AI-powered customer support system handling 10,000 queries daily, the waste compounds quickly. If 30% of queries are semantic duplicates (a conservative estimate), that’s 3,000 unnecessary API calls every single day.

How Semantic Caching Actually Works

Bifrost’s approach uses vector embeddings to capture semantic meaning. Here’s the process:

When a request arrives:

1. Generate an embedding vector for the prompt using a small, fast model (like OpenAI’s text-embedding-3-small)
2. Search the vector store for cached entries with high semantic similarity
3. If similarity exceeds the configured threshold (typically 0.8-0.95), return the cached response
4. If no match exists, call the LLM and cache both the response and its embedding

The key insight: Two semantically similar prompts produce similar embedding vectors, even if the exact words differ. Vector similarity search finds these near-matches in milliseconds.

When a user asks "What are your business hours?" the system generates an embedding and caches the response. When someone later asks "When are you open?", the embedding for this new query is mathematically similar to the cached one. The system recognizes the semantic equivalence and serves the cached response in under 50ms instead of waiting 3-5 seconds for an LLM call.

Dual-Layer Architecture

Bifrost implements two caching layers:

Exact hash matching catches perfectly identical requests—the fastest path with just a hash lookup.

Semantic similarity search handles variations in phrasing, typos, and different ways of asking the same question. This is where most cache hits occur.

The dual approach maximizes hit rates while minimizing overhead.

The Cost Math

The economics of semantic caching are compelling. Consider a documentation chatbot serving 50,000 queries monthly:

Without semantic caching:

• 50,000 queries × $0.01 per query = $500/month in LLM costs
• Average response time: 2,000ms

With semantic caching (40% hit rate):

• 30,000 cache misses × $0.01 = $300/month in LLM costs
• 20,000 cache hits × $0.0001 (embedding cost) = $2/month in embedding costs
• Total: $302/month (40% cost reduction)
• Cache hits respond in 50ms instead of 2,000ms

The savings scale with query volume. Applications serving millions of requests see cost reductions in the tens of thousands of dollars annually.

Configuration Flexibility

Production teams tune caching behavior based on specific use cases:

Similarity threshold: Higher thresholds (0.95) require near-identical meaning. Lower thresholds (0.80) catch more variations but risk false positives.

TTL (Time-to-Live): Customer support FAQs might cache for hours; real-time data needs shorter TTLs.

Namespace isolation: Different applications maintain separate cache spaces with different configurations.

Teams can exclude certain queries from caching using cache control headers, maintaining flexibility for dynamic content.

Real-World Performance

Production deployments across customer support chatbots, documentation assistants, and internal knowledge bases show consistent patterns:

• Customer support: 50-70% cache hit rates on FAQ-type questions
• Documentation search: 40-60% hit rates on common how-to queries
• Code assistance: 30-50% hit rates on repeated debugging questions

The combination of cost savings and latency improvement creates a compounding benefit. Users get faster responses, infrastructure costs drop, and application throughput increases without scaling the underlying LLM provider.

The Implementation Reality

What makes Bifrost’s semantic caching particularly powerful is the implementation simplicity. Teams don’t need to build vector databases, manage embedding models, or write similarity search algorithms. The gateway handles all of this transparently.

Developers point their existing LLM client at Bifrost’s endpoint instead of directly at OpenAI or Anthropic. The cache activates automatically. There’s no application code to change, no new APIs to learn, and no infrastructure to manage beyond the gateway itself.

For AI teams spending thousands monthly on LLM APIs, semantic caching often pays for gateway deployment through cost savings alone—while simultaneously improving user experience through faster response times.

The question isn’t whether to implement semantic caching. It’s whether to build it yourself or use battle-tested infrastructure that handles the complexity for you.

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Bifrost is an open-source LLM gateway available at github.com/maxim-ai/bifrost