LLM Token Caching: Cutting GenAI Costs and Latency by 75%

Generative AI models face critical scaling challenges due to token-driven costs and latency. LLM token caching solves this by storing and reusing computations, reducing redundancy in AI workloads. This technical deep dive explores caching architectures-from semantic matching to adaptive eviction-and their real-world impact: 75% cost reductions, 10x faster responses, and enterprise-grade scalability supported by Google, AWS, and research breakthroughs.

The Token Cost Crisis in Generative AI

Token consumption is the fundamental cost and latency driver in large language models (LLMs). Each token processed requires computational resources, directly increasing expenses. As research confirms, this creates unsustainable cost inflation as applications scale. Without optimization, enterprises face prohibitive operational expenses-particularly for high-traffic services like chatbots or document processing.

Consider the compounding factors:

• Input tokens: Every word, character, or code snippet fed into the model
• Output tokens: The AI-generated response content
• Context overhead: Reused prompts or instructions reprocessed unnecessarily

As noted in AWS benchmarks, uncached responses for frequent queries create redundant computation. This waste manifests as dollar costs and user experience degradation.

Token and Context Caching: Architectural Principles

Token-level caching minimizes redundant computation by storing and reusing previously generated outputs. When identical or similar queries recur, the system retrieves cached responses instead of reprocessing requests through the LLM.

Cache Hits vs. Misses: The Performance Gulf

Efficient systems maximize cache hits-instances where requested content exists in storage:

• Cache hits deliver near-instant responses (e.g., 0.6 seconds) at dramatically lower costs
• Cache misses require full LLM processing, creating latency (e.g., 6 seconds) and higher expenses

Google Vertex AI’s implementation exemplifies this: cached input tokens are billed at a 75% discount compared to standard processing.

Semantic and Generative Caching: Beyond Exact Matches

Innovations move beyond verbatim matching to semantic equivalence:

• Semantic caching uses embeddings to identify queries with similar meaning despite differing phrasing
• Generative caching (as described in arXiv research) adapts or combines cached elements for nuanced responses

For example, queries like “how do I reset my password?” and “password reset steps” trigger identical cached responses despite syntactic differences. AWS’s semantic cache in MemoryDB achieves this using vector similarity search, enabling flexible reuse of prior computations.

Advanced Techniques: Adaptive Eviction and Threshold Tuning

Intelligent cache management balances hit rates with resource constraints and quality assurance:

Adaptive Eviction with FastGen

Systems like FastGen (AdaSci) optimize cache retention using real-time metrics:

• Evict low-value entries based on recency, frequency, and similarity patterns
• Adjust memory allocation during traffic spikes
• Prioritize high-impact responses (e.g., frequently accessed support content)

“FastGen improves scalability, allowing LLMs to handle larger workloads or multiple users simultaneously.” – FastGen Technical Overview

Quality-Guided Threshold Optimization

Feedback loops ensure cached responses meet quality standards:

1. Monitor user satisfaction scores for cache-delivered answers
2. Automatically adjust semantic similarity thresholds
3. Balance between cache utilization and accuracy requirements

The arXiv study underscores this: “We improve upon previous semantic caching methods by adaptively varying semantic similarity thresholds…to reduce monetary costs and latencies while providing desired responses.”

Enterprise Implementations and Real-World Impact

Major cloud providers now offer integrated caching for production AI workloads:

Google Vertex AI Context Caching

Vertex AI’s solution targets repetitive contexts:

• Caches system instructions (e.g., “Always respond in Spanish”)
• Optimizes document/video summarization across user sessions
• Reuses code analysis contexts in developer tools

Requires minimum cacheable inputs: 1,024 tokens for Gemini 2.5 Flash, 2,048 tokens for Gemini 2.5 Pro.

AWS MemoryDB for Generative Caching

Persistent semantic caching achieves massive scale:

• Handles 100,000+ daily queries for Anthropic Claude deployments
• Delivers responses in 0.6 seconds vs. 6 seconds for misses
• Operates at storage costs as low as $0.20 per GB

“Answers found in the cache take 0.6 seconds versus 6 seconds for generated content.” – AWS Database Blog

Additional Use Cases

• Chatbots: Cache common support queries across thousands of users
• Coding assistants: Reuse contextual “prefixes” in iterative development workflows (Gemini API)
• Personalization engines: FastGen dynamically caches user preference profiles

Benefits and Implementation Framework

Token caching transforms LLM economics through measurable gains:

Performance and Cost Metrics

• Latency: 10x faster responses with cache hits
• Costs: Up to 75% savings on cached tokens
• Scalability: Handle >100k queries/day with linear cost growth

Implementation Checklist

1. Identify cache candidates: Analyze logs for repeated queries using tools like Anthopic or LangSmith
2. Select caching layer: Choose key-value (Redis), semantic (MemoryDB), or hybrid approaches
3. Configure thresholds: Set initial similarity scores (e.g., cosine similarity >0.85)
4. Enable vendor features: Activate Vertex AI Caching or Gemini API caching modes
5. Monitor and adapt: Track hit rates, costs, and quality KPIs monthly

Conclusion

LLM token caching has evolved from niche optimization to essential architecture, delivering 10x latency improvements and 75% cost reductions. As generative AI scales, techniques like semantic matching and adaptive eviction-implemented via Google Vertex AI, AWS MemoryDB, and solutions like FastGen-enable sustainable deployment. Enterprises should immediately audit token expenses, prototype caching via cloud APIs, and implement monitoring to maximize value. The era of unchecked token costs is over; strategic caching is now the foundation of efficient AI.

Next step: Experiment with Gemini API’s caching features or test a semantic cache prototype using open-source frameworks like LanceDB.