In August 2024, Anthropic rolled out prompt caching for its Claude models. This feature allows developers to store frequently used context between API calls, making interactions faster and more cost‑effective.
For large enterprises building AI‑driven products, prompt caching represents a practical step toward more efficient and scalable generative AI.
What Is Prompt Caching?
Prompt caching lets you store “background” information – like long instructions, examples, or large documents – in a cache. Instead of sending the same context with every API call, you load it once and reference it on subsequent requests.
This reduces token usage and latency for lengthy prompts, creating a more responsive experience.
According to Anthropic’s public beta release, prompt caching is available on the Anthropic API for Claude 3.5 Sonnet, Opus, and Haiku models.
It’s in preview on Amazon Bedrock and Google Cloud Vertex AI, making it accessible across multiple cloud platforms. The feature is designed for calls where the context remains largely static across interactions, such as:
Conversation Agents: multistep dialogues that involve long instructions or uploaded documents.
Coding Assistants: Summaries of codebases or libraries to speed up autocomplete and code navigation.
Large Document Processing: Loading books, PDFs, transcripts, or any long content for Q&A and summarisation.
Detailed Instruction Sets: Sharing extensive lists of guidelines so the model adheres to specific tone, format, or logic.
Agentic Search and Tool Use: Reusing instructions across multiple rounds of tool calls.
“Talk to Books” or Podcasts: Embedding entire works so users can ask questions and have conversations about them.
Performance Benefits
Anthropic reports that early customers have seen up to 90 % cost reduction and 85 % latency improvement for long prompts.
For example, a cached conversation about a 100 000‑token book reduced latency from 11.5 seconds to 2.4 seconds and slashed cost by 90 %.
Many‑shot prompting scenarios showed a 31% latency decrease and an 86% cost decrease, while multi‑turn conversations saw a 75% latency decrease and a 53% cost decrease.
Writing to the cache costs roughly 25 % more than the base input token price because it involves storage. Reading cached context is only 10 % of the base input cost, enabling significant savings for repeated calls.
Enterprise Use Cases – Accelerai
Prompt caching unlocks several benefits for large organisations:
Lower Total Cost of Ownership: By caching long prompts, you minimise ongoing token charges and reduce compute load.
Better User Experiences: Faster responses improve chatbot satisfaction and ai developer productivity.
Scalable Architectures: Long contexts (up to 100 K tokens) can be cached, turning one‑off exploratory queries into persistent conversations.
Flexible Integration: Use cached prompts on Anthropic’s API today, then extend to Bedrock or Vertex AI as support matures.
Preview of Persistent “Memory”: Prompt caching is an early step toward more persistent memory in AI assistants, enabling context to survive across sessions.
Implementation Considerations
Enterprises should:
Identify High‑Impact Prompts: Cache prompts that recur often, such as codebase summaries, legal instructions or knowledge‑base documents.
Cache Management: Decide when to update or invalidate cached prompts to reflect new information.
Cost Modelling: Forecast potential savings by analysing prompt length, call frequency and model selection.
Security & Compliance: Ensure that cached content complies with data governance policies and encryption requirements.
Integration with Platform Services: Leverage existing caching tools within Amazon Bedrock or Vertex AI to standardise workflows.
What’s Next with Accelerai
Prompt caching may not sound glamorous, but it offers tangible savings for enterprises adopting Claude models.
By reducing the overhead of long prompts and improving latency, it accelerates deployment of AI agents, coding assistants, document analysers and more.
As Anthropic extends support across cloud platforms, prompt caching represents a key step toward scalable, memory‑efficient generative AI.
Get in touch today to see how we can help scale your business using the latest AI models.
