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MuleSoft Inference Connector – integration of large-language model (LLM) inference into the MuleSoft

The MuleSoft Inference Connector enables developers to embed AI inference capabilities—such as text generation, chat, image generation, and moderation—into their MuleSoft applications. It acts as a unified interface to multiple model providers (OpenAI, Azure OpenAI, Hugging Face, Groq, etc.), reducing the need to write provider-specific code and allowing seamless integration within the Anypoint Pl

🚀 Efficient Large Data Handling in MuleSoft: Batch Processing vs. For Each & Parallel For Each

MuleSoft enables efficient large-scale data handling using Batch Processing, For Each, and Parallel For Each. Batch Processing handles massive data sets in chunks for scalability, while For Each iterates sequentially for smaller loads. Parallel For Each boosts performance by processing items concurrently across multiple threads.