Build Applications with Generative AI

Develop and deploy sophisticated LLM-powered products with deepset Cloud.

AI development lifecycle | Build with LLMs

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Unlock any generative AI use case with deepset Cloud

  • Retrieval Augmented Generation

    Retrieve relevant documents, use them to inform responses, and generate accurate and contextually rich answers to complex queries.

  • Agents

    Automate and streamline tasks, workflows, and insight generation with a compound AI system capable of complex reasoning and decision making.

  • Conversational BI (Text to SQL)

    Transform natural language queries into SQL commands, ask questions of complex datasets, and make data analysis more accessible and intuitive.

  • Question Answering

    Pinpoint the exact location of an answer, efficiently extract and cite information, and generate human-like responses to questions.

  • Vector-Based Search

    Understand and retrieve information based on semantic similarity, deliver highly accurate and relevant search results, and provide an advanced recommendation service.

  • Multimodal

    Integrate and process multiple forms of data including text, PDFs, images, audio, and video for an enriched user experience.

  • Start building now

    You’ll have your first prototype at breakfast, feedback by lunch, and an integrated LLM feature before dinner. The deepset Cloud platform provides the tools to quickly iterate, evaluate, demo, and get feedback 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 spend your time developing, testing, and launching.

  • Future-proof your AI stack

    Generative AI is advancing at an unprecedented pace. New and better models emerge all the time. If you’re using LLMs in your app, you don’t want to be locked in with a single-model or specific vendor. With deepset Cloud, you can easily use and compare and swap models like GPT-4, Llama-v2 or Claude.

  • Deliver enterprise-grade products

    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. To extend your team's expertise, our AI Engineers are here to support you with professional services.

The Platform for the Entire AI Application Development Cycle

From early prototyping to large-scale production


  • Architect your application with state-of-the-art compound AI pipelines.
  • Get started quickly with templates for the most common setups and prompts.
  • Learn with our comprehensive documentation.
  • Develop with an intuitive UI, REST API, and SDK.


  • Connect to popular model providers or bring your own LLM.
  • Experiment with different setups by adjusting your pipeline configuration.
  • Tweak your prompts with Prompt Studio.
  • Fine-tune models in GPU-powered notebooks.


  • Conduct experiments to test different versions of your app.
  • Work with traditional word-based or advanced model-based metrics.
  • Share prototypes with all your stakeholders to collect feedback.
  • Track the performance of your application over time.


  • Deploy to production with one click.
  • Auto-scale infrastructure according to your traffic.
  • Query your pipeline via our REST API.
  • Streamline data synchronization and indexing with our SDK.


  • Observe requests, latency, and usage.
  • Detect model or data drift.
  • Measure accuracy and Groundedness over time.
  • Use feedback to identify trends and improve models.

Discover deepset Cloud

Launch your AI-powered product, feature, or process in record time.

Why 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

Talk to us about deepset Cloud

Frequently Asked Questions

  • Compound AI takes advantage of the diversity of AI models, both commercial and open-source. It capitalizes on their unique strengths and capabilities to create modular applications that are more powerful than their parts. This approach has many notable advantages, including the ability to easily swap and update models, and independence from any one model vendor or model type. Virtually all advanced AI applications in production today use this composite approach.

  • In compound AI, components and pipelines are the building blocks that allow teams to quickly and easily assemble a working system. Components are typically responsible for a single task, such as retrieval or LLM prompting. They are then combined into larger building blocks to form pipelines. deepset Cloud provides templates for the most common pipelines. AI teams can easily customize these templates to fit their unique use cases by adding and removing components as needed.

  • LLMs "hallucinate", that is, they make up facts that are not supported by any data. Because of the eloquence of these models, hallucinations can be difficult to detect, creating a volatile factor that is a barrier to using LLMs in production. However, using a combination of prevention techniques, teams can reduce the number of hallucinations to a minimum. These include effective prompting, grounding the LLM's response in fact-checked data through RAG, and monitoring the Groundedness of responses.

  • Judging the veracity of an LLM's answer is not an easy task, it requires a lot of knowledge about the world. But in RAG, our application chooses the ground truth from a database of documents that we control. deepset's Groundedness metric takes advantage of this property. It uses a language model to scan both the underlying documents and the generated answer, sentence by sentence. It then outputs a score that measures how well the answer is grounded in the data. The Groundedness Observability Dashboard tracks this score over time, allowing users to monitor the semantic accuracy of their system at a glance.

  • New technologies require new skills and new ways of working together. AI teams are cross-functional teams that build products or tools using AI technology. They bring together technical people – software developers, AI engineers, and data scientists – with non-technical domain experts and business stakeholders to create a well-rounded product that addresses real-world needs. Led by a visionary and pragmatic team leader, AI teams work in a cyclical fashion, iterating on their product both before and after launch.