As a developer who burns through LLM API tokens daily, I know the pain all too well: repetitive content is everywhere!

Think about it - we’re constantly re-sending chat history in multi-turn conversations, reusing the same dataset prefixes in analysis tasks, and repeating identical reference document chunks in RAG applications. These repetitive inputs don’t just drain our wallets; they make waiting times painfully long.

Until recently, DeepSeek finally built what we’ve all been waiting for - context disk caching technology!

When I saw that cache-hit portions cost only 0.1 RMB per million tokens, my first thought was: this is literally redefining the LLM API game!

My Experience with DeepSeek’s Disk Cache: It’s Amazing!

Zero Configuration, Works Out of the Box

What surprised me most: no code changes required!

I kept using the same API interface while DeepSeek automatically handled caching in the background, billing me based on actual cache hit rates. This user experience is simply world-class!

Important note: Only requests with identical prefix content (matching from the 0th token onwards) count as repetitions. Mid-sequence repetitions can’t be cached. But even this is enough to solve 90% of our pain points.

Use Cases

As a power user, I’ve found this feature to be a game-changer in two key scenarios:

1. Multi-turn Conversations

Previously, every conversation round required recomputing the entire context. Now subsequent rounds hit the cache from previous rounds. I tested this with a customer service bot - costs dropped by 70%!

2. Data Analysis Tasks

I frequently analyze the same datasets from different angles, which meant reprocessing identical data prefixes every time. With caching, my analysis efficiency has multiplied several times over.

Real-Time Monitoring: The Numbers Don’t Lie

Crystal Clear Cache Hit Metrics

DeepSeek thoughtfully added two key metrics to the API response’s usage field, letting me monitor cache performance in real-time:

  • prompt_cache_hit_tokens: Tokens in the current request that hit the cache (0.1 RMB / million tokens)
  • prompt_cache_miss_tokens: Tokens in the current request that missed the cache (1 RMB / million tokens)

Every API call shows exactly how much money I’m saving - this level of transparency is incredibly reassuring!

Performance and Cost Impact: Beyond My Expectations

Mind-Blowing Latency Improvements

For requests with lots of repetitive content, the first-token latency improvement is nothing short of transformative.

Extreme test case: I ran a 128K request (mostly repetitive content) and saw first-token latency drop from 12 seconds to 400 milliseconds! That’s a 30x performance boost!

Immediate Cost Savings

  • Maximum savings: Up to 90% cost reduction (with cache-optimized workflows)
  • Typical savings: Even if I do nothing differently, just with this feature launch alone, I expect my costs to drop by 50% - time will tell
  • Zero extra fees: Cache usage costs only 0.1 RMB per million tokens with no storage charges

Security and Privacy: Totally Peace of Mind

As a user, data security is obviously my top concern. DeepSeek’s design puts my mind completely at ease:

  • User Isolation: Each user’s cache is completely independent and logically invisible to others, ensuring data security and privacy from the ground up
  • Automatic Cleanup: Long-unused caches are automatically cleared and never repurposed
  • Data Protection: Strict isolation prevents any cross-user data exposure

Honestly, this level of security assurance exceeds even my expectations.

DeepSeek’s Technical Innovation: Why They Could Do It First

What I have to admire is that based on public information, DeepSeek appears to be the world’s first major LLM provider to implement large-scale disk caching in API services.

This breakthrough is enabled by DeepSeek V2’s MLA (Multi-head Latent Attention) architecture:

  • Improves model performance while dramatically compressing context KV Cache size
  • Reduces both transfer bandwidth and storage capacity requirements
  • Makes low-cost disk caching feasible

This is real technical innovation! Not just parameter scaling, but architectural optimization that delivers practical value.

OpenAI, Anthropic, Google: Time to Catch Up!

Frankly, seeing DeepSeek launch this feature, my first thought was: what are all the other big players doing?

OpenAI’s GPT API, Anthropic’s Claude API, Google’s Gemini API - these services we use every day - why don’t they have similar caching mechanisms yet?

The money we users spend each month on redundant computation is no small amount. DeepSeek has proven this technology is viable with excellent user experience.

I hope OpenAI, Anthropic, and Google will quickly follow suit, giving us users more choices and driving industry progress. After all, healthy competition leads to better products and services.