Publié : 8 October 2025
Actualisé : 1 month ago
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The era of Large Language Models (LLMs) has revolutionized many sectors, offering unprecedented capabilities in natural language processing, text generation and complex analysis. However, the intensive use of these technologies is often accompanied by a major challenge: cost. API calls to LLM providers can become onerous, particularly for applications requiring the processing of vast quantities of data or frequent queries. Fortunately, there is a solution for budget-conscious companies: batch inference . This approach can significantly reduce costs in exchange for a slightly longer response time, an acceptable concession for many business use cases. In this article, we’ll explore how to effectively implement this technique, particularly with Mistral and the LangGraph framework, to optimize costs without sacrificing the power of LLM
⭐ What is Batch Inference and why is it crucial?
The concept of batch inference is simple but powerful. Instead of sending each query individually to an LLM’s API and expecting an immediate response, you group several queries into a single “batch”. This batch is then sent to the supplier, who processes it at his own pace. By accepting a delayed response time – which can range from a few minutes to a few hours, depending on batch size and server load – companies can benefit from a significant reduction in cost per call, often of the order of 50%. This strategy is particularly advantageous for massive data processing operations, off-peak analysis, weekly or monthly report generation, and any task where immediacy is not a critical requirement. The value of batch inference is all the more relevant as language models continue to improve and find new applications. Whether for document classification, long-text summarization, translation of large volumes of content or extraction of structured information from unstructured data, the ability to handle these tasks economically opens the door to wider, more ambitious LLM deployments
⭐ Implementation challenges and the LangGraph solution
Despite its financial advantages, implementing batch inference is not without its complexities. Unlike a simple synchronous API call, batch processing requires asynchronous management of requests, tracking of batch status, and retrieval of results once calculations are complete. This involves managing batch identifiers, periodically checking progress, and potentially re-running queries if results are not yet available. This is where frameworks such as LangChain and its LangGraph overlay come in, greatly simplifying this task. LangGraph, built on LangChain, enables the creation of complex agents and chains with state and memory management. It provides a framework for orchestrating workflows that can include asynchronous steps, verification loops and data retrieval. This capability is essential for managing the lifecycle of a batching process, transforming a series of potentially tedious manual operations into an automated, resilient execution graph.
“Optimizing the cost of AI infrastructures is no longer an option, but a strategic necessity for any company wishing to deploy LLM-based solutions on a large scale.” – Dr. Éloïse Dubois, Expert in Distributed Architectures
⭐ How does batching work with LangGraph and Mistral?
To implement batching with Mistral (or other providers such as OpenAI or Claude) via LangGraph, a Python environment (>=3.11) is required, as well as installation of the LangGraph CLI and configuration of a valid API key. The process involves the creation of a LangGraph agent capable of handling batching-specific steps, taking advantage of the framework’s state management features
- A state variable for the batch identifier: LangGraph uses checkpointers to store the state of the graph, including the unique identifier of each batch, acting as a valuable “parking ticket”.
- A node to trigger the batch: This node is responsible for sending the initial batch requests to the LLM batch API.
- An edge to check progress: A conditional edge probes the supplier API to determine whether batch processing has been completed.
- A node to retrieve results: Once the batch has been processed, this node collects the complete responses, including LLM messages, any errors and metadata (such as tokens used).
Interaction with the LangGraph agent takes place in two stages: an initial call to submit the batch and obtain its identifier, followed by subsequent calls (potentially repeated) to check the status and, finally, retrieve the results. The LangGraph CLI automatically handles checkpointing, saving the status between executions, while tools such as LangChain Studio can provide a graphical interface for viewing and interacting with the agent. Dedicated GitHub repositories (such as LBKE’s) offer comprehensive code examples to get you started
⭐ Cost comparison and alternatives
The 50% reduction on API costs is a strong argument for batch inference, especially for companies with large volumes of data to process or stringent budget requirements. This approach is recommended for business uses where the performance of advanced models is crucial, and where a slight delay can be tolerated without impacting the user experience or business processes. For those looking to experiment, or who have very limited needs but no budget, free alternatives exist. Platforms such as OpenRouter offer access to a list of LLM models at no charge, although often with usage limitations (query rate, I/O size). These options are excellent for learning, prototyping or personal projects, but are generally not suited to the production requirements of a company
⭐ Conclusion: Optimizing LLM Costs is Essential
The integration of LLMs into enterprise workflows is an irreversible trend, but their cost of operation remains a limiting factor. Batch inference, especially when facilitated by frameworks such as LangGraph, offers a concrete path towards a more economical and sustainable use of these technologies. By understanding its mechanisms and applying it to relevant use cases, organizations can unlock the full potential of LLMs, even with budget constraints. The future of AI lies not only in higher-performance models, but also in smarter, more cost-effective architectures. Adopting these optimization strategies is an essential step towards responsible, scalable enterprise AI. Not only does this help keep costs under control, it also encourages experimentation and innovation by reducing financial barriers. Batching is not a one-size-fits-all solution, but a valuable tool in the toolbox of any architect or developer working with LLMs
❓ Frequently asked questions
What is the main advantage of batch inference over unitary LLM calls?
In which application scenarios is batch inference most appropriate?
What is the main trade-off when using batch inference?
What kind of cost reduction can we expect with this approach?
🎥 Explanatory Video
Video automatically selected to enrich your reading
📋 Sommaire
- ⭐ What is Batch Inference and why is it crucial?
- ⭐ Implementation challenges and the LangGraph solution
- ⭐ How does batching work with LangGraph and Mistral?
- ⭐ Cost comparison and alternatives
- ⭐ Conclusion: Optimizing LLM Costs is Essential
- ❓ Frequently asked questions
- What is the main advantage of batch inference over unitary LLM calls?
- In which application scenarios is batch inference most appropriate?
- What is the main trade-off when using batch inference?
- What kind of cost reduction can we expect with this approach?
- 🎥 Explanatory Video
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