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Apify's compact JSON slashes LLM agent token costs

By PulseAugur Editorial · [1 sources] ·

A blog post on dev.to details a new compact JSON output format designed to reduce token usage for LLM agents. The format, implemented by Apify, showed significant savings, using 33.8% fewer tokens than pretty JSON and 18.3% fewer than minified JSON with OpenAI's o200k_base tokenizer. These savings can translate to substantial cost reductions for agents processing large datasets, with potential monthly savings of around $58 for a high-volume user. The format maintains data integrity, ensuring all values and field presence are preserved, and is compatible with standard JSON parsers. AI

IMPACT Reduces operational costs for LLM agents by optimizing data transfer and processing.

RANK_REASON Blog post detailing a new output format for LLM agents that reduces token usage and costs.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Apify's compact JSON slashes LLM agent token costs

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Blog post detailing a new output format for LLM agents that reduces token usage and costs.
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  1. dev.to — LLM tag TIER_1 English(EN) · Integration Yard ·

    Compact, Lossless JSON Output for LLM Agents: How Much Do You Actually Save?

    <p>When you hand scraped data to an LLM as an array of JSON objects, every row repeats every key name. For a 400-row export that's thousands of tokens spent on <code>"title"</code>, <code>"view_count"</code> and <code>"author_name"</code> over and over before the model ever sees …

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