LLM Caching

Store and reuse LLM responses to reduce costs and latency

When developing and testing LLM applications, you often make the same requests repeatedly during debugging and iteration. Helicone caching stores complete responses on Cloudflare’s edge network, eliminating redundant API calls and reducing both latency and costs.

Looking for provider-level caching? Helicone also supports prompt caching directly on provider servers (OpenAI, Anthropic, etc.) for reduced token costs.

Why Helicone Caching

Save Money

Avoid repeated charges for identical requests while testing and debugging

Instant Responses

Serve cached responses immediately instead of waiting for LLM providers

Handle Traffic Spikes

Protect against rate limits and maintain performance during high usage

How It Works

Helicone’s caching system stores LLM responses on Cloudflare’s edge network, providing globally distributed, low-latency access to cached data.

Cache Key Generation

Helicone generates unique cache keys by hashing:

• Cache seed - Optional namespace identifier (if specified)
• Request URL - The full endpoint URL
• Request body - Complete request payload including all parameters
• Relevant headers - Authorization and cache-specific headers
• Bucket index - For multi-response caching

Any change in these components creates a new cache entry:

// ✅ Cache hit - identical requests
const request1 = { model: "gpt-4o-mini", messages: [{ role: "user", content: "Hello" }] };
const request2 = { model: "gpt-4o-mini", messages: [{ role: "user", content: "Hello" }] };

// ❌ Cache miss - different content  
const request3 = { model: "gpt-4o-mini", messages: [{ role: "user", content: "Hi" }] };

// ❌ Cache miss - different parameters
const request4 = { model: "gpt-4o-mini", messages: [{ role: "user", content: "Hello" }], temperature: 0.5 };

Cache Storage

• Responses are stored in Cloudflare Workers KV (key-value store)
• Distributed across 300+ global edge locations
• Automatic replication and failover
• No impact on your infrastructure

Quick Start

1. Enable caching

   Add the Helicone-Cache-Enabled header to your requests:

   {
     "Helicone-Cache-Enabled": "true"
   }
   
2. Make your request

   Execute your LLM request - the first call will be cached:

   import OpenAI from "openai";
   
   const client = new OpenAI({
     baseURL: "https://ai-gateway.helicone.ai",
     apiKey: process.env.HELICONE_API_KEY,
   });
   
   const response = await client.chat.completions.create(
     {
       model: "gpt-4o-mini",
       messages: [{ role: "user", content: "Hello world" }]
     },
     {
       headers: {
         "Helicone-Cache-Enabled": "true"
       }
     }
   );
   
3. Verify caching works

   Make the same request again - it should return instantly from cache:

   // This exact same request will return a cached response
   const cachedResponse = await client.chat.completions.create(
     {
       model: "gpt-4o-mini", 
       messages: [{ role: "user", content: "Hello world" }]
     },
     {
       headers: {
         "Helicone-Cache-Enabled": "true"
       }
     }
   );
   

Configuration

Helicone-Cache-Enabled · string · required

Enable or disable caching for the request.

Example: "true" to enable caching

Cache-Control · string

Set cache duration using standard HTTP cache control directives.

Default: "max-age=604800" (7 days)

Example: "max-age=3600" for 1 hour cache

Helicone-Cache-Bucket-Max-Size · string

Number of different responses to store for the same request. Useful for non-deterministic prompts.

Default: "1" (single response cached)

Example: "3" to cache up to 3 different responses

Helicone-Cache-Seed · string

Create separate cache namespaces for different users or contexts.

Example: "user-123" to maintain user-specific cache

Helicone-Cache-Ignore-Keys · string

Comma-separated JSON keys to exclude from cache key generation.

Example: "request_id,timestamp" to ignore these fields when generating cache keys

All header values must be strings. For example, "Helicone-Cache-Bucket-Max-Size": "10".

Examples

Development Testing

Avoid repeated charges while debugging and iterating on prompts:

Node.js

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://ai-gateway.helicone.ai",
  apiKey: process.env.HELICONE_API_KEY,
  defaultHeaders: {
    "Helicone-Cache-Enabled": "true",
    "Cache-Control": "max-age=86400" // Cache for 1 day during development
  },
});

// This request will be cached - works with any model
const response = await client.chat.completions.create({
  model: "gpt-4o-mini",  // or "claude-3.5-sonnet", "gemini-2.5-flash", etc.
  messages: [{ role: "user", content: "Explain quantum computing" }]
});

// Subsequent identical requests return cached response instantly

User-Specific Caching

Cache responses separately for different users or contexts:

const userId = "user-123";

const response = await client.chat.completions.create(
  {
    model: "claude-3.5-sonnet",
    messages: [{ 
      role: "user", 
      content: "What are my account settings?" 
    }]
  },
  {
    headers: {
      "Helicone-Cache-Enabled": "true",
      "Helicone-Cache-Seed": userId,           // User-specific cache
      "Cache-Control": "max-age=3600"          // Cache for 1 hour
    }
  }
);

// Each user gets their own cached responses

Understanding Caching

Cache Response Headers

Check cache status by examining response headers:

const response = await client.chat.completions.create(
  { /* your request */ },
  { 
    headers: { "Helicone-Cache-Enabled": "true" }
  }
);

// Access raw response to check headers
const chatCompletion = await client.chat.completions.with_raw_response.create(
  { /* your request */ },
  { 
    headers: { "Helicone-Cache-Enabled": "true" }
  }
);

const cacheStatus = chatCompletion.http_response.headers.get('Helicone-Cache');
console.log(cacheStatus); // "HIT" or "MISS"

const bucketIndex = chatCompletion.http_response.headers.get('Helicone-Cache-Bucket-Idx');
console.log(bucketIndex); // Index of cached response used

Cache Duration

Set how long responses stay cached using the Cache-Control header:

{
  "Cache-Control": "max-age=3600"  // 1 hour
}

Common durations:

• 1 hour: max-age=3600
• 1 day: max-age=86400
• 7 days: max-age=604800 (default)
• 30 days: max-age=2592000

Maximum cache duration is 365 days (max-age=31536000)

Cache Buckets

Control how many different responses are stored for the same request:

{
  "Helicone-Cache-Bucket-Max-Size": "3"
}

With bucket size 3, the same request can return one of 3 different cached responses randomly:

openai.completion("give me a random number") -> "42"  # Cache Miss
openai.completion("give me a random number") -> "47"  # Cache Miss  
openai.completion("give me a random number") -> "17"  # Cache Miss

openai.completion("give me a random number") -> "42" | "47" | "17"  # Cache Hit

Behavior by bucket size:

• Size 1 (default): Same request always returns same cached response (deterministic)
• Size > 1: Same request can return different cached responses (useful for creative prompts)
• Response chosen randomly from bucket

Maximum bucket size is 20. Enterprise plans support larger buckets.

Cache Seeds

Create separate cache namespaces using seeds:

{
  "Helicone-Cache-Seed": "user-123"
}

Different seeds maintain separate cache states:

# Seed: "user-123"
openai.completion("random number") -> "42"
openai.completion("random number") -> "42"  # Same response

# Seed: "user-456"  
openai.completion("random number") -> "17"  # Different response
openai.completion("random number") -> "17"  # Consistent per seed

Change the seed value to effectively clear your cache for testing.

Cost Savings

Caching can dramatically reduce your LLM costs:

Example Savings

Development

Scenario: Testing a feature 100 times during development

• Without caching: 100 requests × $0.002 = **$0.20**
• With caching: 1 request × $0.002 + 99 cached = **$0.002**
• Savings: 99%

FAQ Bot

Scenario: FAQ bot with 50 common questions, 1000 requests/day

• Without caching: 1000 requests × $0.002 = **$2.00/day**
• With caching: ~50 unique × $0.002 + 950 cached = **$0.10/day**
• Savings: 95% ($58/month →$3/month)

Testing Suite

Scenario: Running automated tests with 200 LLM calls

• Without caching: 200 requests × $0.02 = **$4.00 per run**
• With caching: First run $4.00, subsequent runs **$0.00**
• Savings: 100% after first run

Monitoring Savings

Track your cache performance in the Helicone dashboard:

• Cache hit rate - Percentage of requests served from cache
• Cost savings - Total dollars saved from cached responses
• Time saved - Cumulative latency reduction
• Top cached requests - Most frequently cached queries

Related Features

Prompt Caching

Cache prompts on provider servers for reduced token costs and faster processing

Custom Properties

Add metadata to cached requests for better filtering and analysis

Rate Limiting

Control request frequency and combine with caching for cost optimization

User Metrics

Track cache hit rates and savings per user or application

Questions?

If you have any questions or need help, please reach out to us:

• Join our Discord community
• Email us at [e-mail]
• Check out our GitHub repository