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Build LLM features into your product

Develop and deploy custom LLM features for your application with deepset Cloud. We support enterprise knowledge search, RAG, summarization, and beyond.

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The end-to-end platform to integrate LLMs with your data

  • Semantic Search

    Build a search that uses vector embeddings to understand your query.

  • Question Answering

    Pinpoint the exact location of an answer or generate human-like responses to questions in natural language with RAG.

  • Summarization

    Get the most important information from a document at a glance by summarizing it with an LLM.

  • Document Similarity

    Find all the documents that are similar to the one you are looking at.

  • Information Extraction

    Extract named entities or recurring information from unstructured text data.

  • Content Generation

    Generate marketing copy, documentation, or any other content you need based on your data and input.

  • Start building now

    You’ll have your first prototype at breakfast, feedback by lunch, and an integrated NLP service before dinner. The deepset Cloud platform solves problems like infrastructure, evaluation, demo UI, and feedback mechanisms so that you can focus on what truly drives your business forward.

  • Scale without the hassle

    From vector storage to GPU inference, things become hard when you do them at scale. deepset Cloud manages all the underlying infrastructure for you so that you can focus on developing the NLP features that your product needs.

  • Choose what’s best for you, no vendor lock-in

    NLP is advancing at an unprecedented pace. New and better models emerge all the time. If you’re using NLP in your app, you don’t want to be stuck with a single-model vendor. We know that. That’s why in deepset Cloud, you can easily use and compare models like GPT-4, Llama-v2 or Claude.

  • Build for enterprise

    We know what it takes to build an application in an enterprise context. In deepset Cloud, you can manage access with MFA and SSO, and your data layer can stay in your VPC. If you need that little extra boost, our NLP experts will support you with professional services.

Supporting you every step of the way


  • Define your feature as simple pipelines
  • Rely on extensive documentation
  • Start from the pipeline and prompt templates
  • Build with a friendly UI, REST API, or SDK


  • Pick models like GPT-4, Llama-v2, Falcon, Claude or bring your own LLM
  • Use dozens of pipeline nodes, from pre-processing to hybrid retrieval
  • Optimize your prompts in the prompt engineering playground
  • Fine-tune models in GPU Notebooks


  • Run structured experiments that you can quickly configure and compare
  • Analyze traditional and model-based metrics
  • Share prototypes to collect feedback


  • Connect your own application to deepset Cloud
  • Query your pipeline from any app via REST API
  • Sync your data with our intelligent file management system


  • Deploy to production with one click
  • Rely on auto-scaling infrastructure adjusting to your traffic
  • Monitor requests, latency, and usage
  • Detect model or data drift

Discover deepset Cloud

From data to API-driven NLP backend services in no time.

Why our users love our product

We chose to add document similarity to our flagship product, because it's all about speed and efficiency — if lawyers need less time to research their cases, they have more time to acquire new clients. With deepset Cloud the advantage of using a pipeline with a fine-tuned language model was very clear to us.

Alexander Feldinger
Product Manager

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Frequently asked questions

  • Natural language processing (NLP) is a branch of AI that enables machines to process and interpret human language. In general, by implementing NLP, companies can leverage human language to interact with computers and data. Areas of NLP include semantic search, question answering (QA), conversational AI (chatbots), text summarization, document similarity, question generation, text generation, machine translation, text mining, speech recognition — to name a few use cases.

  • The ever-increasing volume of unstructured data in the enterprise, such as corporate documents, financial reports, research papers, and legal contracts, presents a challenging problem for enterprise product teams. Typically, this type of data is difficult for enterprise software to process. NLP allows developers to apply the latest research to industry-relevant, real-world use cases, such as semantic search and question answering. By streamlining approximately 80% of mundane processing tasks, NLP technology ensures better efficiency in data processing, data analysis, reporting, as well as improved customer experience, reduced operational costs, and increased customer satisfaction. Leveraging natural language processing, companies can create intelligent solutions for common business problems.

  • Modern enterprises have been rapidly integrating NLP into their internal products, processes and workflows. It is not only Amazon AWS or Microsoft who implemented NLP, but also enterprises such as Airbus, Infineon, Alcatel-Lucent, government agencies across the globe, as well as startup tech companies who are utilizing NLP to create amazing new products and services. NLP can be used in the financial industry, legal field, science, manufacturing, and many other verticals. Practical applications of NLP usually involve implementing semantic search and question answering to automate data analysis, decision-making process, fraud monitoring, claim management, regulatory actions, to reduce costs and improve customer satisfaction.

  • Natural language processing solutions (NLP) offer a variety of benefits of using artificial intelligence and machine learning tools to solve a range of common business and technology problems related to processing, sorting and making sense of data. We have helped the largest European companies and public sector organizations to instrument semantic search and question answering (QA) to automate data processing, legal analysis, regulatory compliance, and decision making. We've also built an enterprise SaaS product to complement our open source NLP framework (Haystack).

  • At deepset we believe in open NLP. This includes open language models, open source tools to build neural search and question answering, open communication and discussion, sharing experiences, as well as educating the developers and the users of NLP-enabled solutions. We believe that only through transparency and openness it is possible to apply natural language processing to various problems that enterprises and governments are facing.