Claude just slashed the cost of building AI applications
ClaudeAI's new Prompt Caching feature allows developers to reuse text, potentially reducing input API costs by up to 90%, benefiting applications like AI assistants and prompting competitors to consider similar innovations.
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ClaudeAI has introduced a new feature called Prompt Caching, which significantly reduces the cost of building AI applications. This feature allows developers to reuse text across multiple prompts, enabling them to cache lengthy examples and only send the essential part of the prompt. This can lead to a reduction of up to 90% in input API costs, which is particularly beneficial for applications that rely heavily on long prompts, such as AI assistants, code generation, code reviews, and processing large documents. By lowering API costs, developers can either reduce their pricing or increase profit margins for their software as a service (SaaS) applications. The introduction of Prompt Caching raises the question of whether competitors like OpenAI will implement similar features in the future.
- ClaudeAI's Prompt Caching can reduce input API costs by up to 90%.
- The feature allows developers to reuse lengthy prompts, saving time and money.
- It is particularly useful for AI assistants, code generation, and document processing.
- Developers can lower pricing or increase profit margins due to reduced costs.
- The move may prompt competitors like OpenAI to consider similar features.
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13 commentshttps://ai.google.dev/gemini-api/docs/caching?lang=python
Costs $1 / 1M / 1h
My understanding is that an LLM will take in the stream of text, tokenize it (can be faster with caching, sure, but it's a minor drop in the bucket), then run a transformer on the entire sequence. You can't just cache the output of a transformer on a prefix to reduce workload.
Comments suggest that caching the state of the network might also reduce processing.
I wonder if it also permits better A/B-style testing by reducing the effect of cross-domain errors. If the AI service providers made it easy to provide feedback on post-cache responses, the providers could incorporate the quality-enhancement loop accelerating time to product-market fit (at the risk of increasing dependency and reducing ability to switch).
Far less "in the realm of", "in today's fast-moving...", multifaceted, delve or other pretentious wank.
There is still some though so they obviously used the same dataset that's overweight in academic papers. Still, I'm hopeful I can finally get it to write stuff that doesn't sound like AI garbage.
Kind of weird there's no moderation API though. Will they just cut me off if my customers try to write about things they don't like?
Are contexts included in the prompt cache? Are they identified as the same or not? What happens if we approach the 10k token range? 128k? 1M?
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