# [Anchor](https://qdrant.tech/documentation/cloud-pricing-payments/\#qdrant-cloud-billing--payments) Qdrant Cloud Billing & Payments

Qdrant database clusters in Qdrant Cloud are priced based on CPU, memory, and disk storage usage. To get a clearer idea for the pricing structure, based on the amounts of vectors you want to store, please use our [Pricing Calculator](https://cloud.qdrant.io/calculator).

## [Anchor](https://qdrant.tech/documentation/cloud-pricing-payments/\#billing) Billing

You can pay for your Qdrant Cloud database clusters either with a credit card or through an AWS, GCP, or Azure Marketplace subscription.

Your payment method is charged at the beginning of each month for the previous month’s usage. There is no difference in pricing between the different payment methods.

If you choose to pay through a marketplace, the Qdrant Cloud usage costs are added as usage units to your existing billing for your cloud provider services. A detailed breakdown of your usage is available in the Qdrant Cloud Console.

Note: Even if you pay using a marketplace subscription, your database clusters will still be deployed into Qdrant-owned infrastructure. The setup and management of Qdrant database clusters will also still be done via the Qdrant Cloud Console UI.

If you wish to deploy Qdrant database clusters into your own environment from Qdrant Cloud then we recommend our [Hybrid Cloud](https://qdrant.tech/documentation/hybrid-cloud/) solution.

![Payment Options](https://qdrant.tech/documentation/cloud/payment-options.png)

### [Anchor](https://qdrant.tech/documentation/cloud-pricing-payments/\#credit-card) Credit Card

Credit card payments are processed through Stripe. To set up a credit card, go to the Billing Details screen in the [Qdrant Cloud Console](https://cloud.qdrant.io/), select **Stripe** as the payment method, and enter your credit card details.

### [Anchor](https://qdrant.tech/documentation/cloud-pricing-payments/\#aws-marketplace) AWS Marketplace

Our [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-rtphb42tydtzg) listing streamlines access to Qdrant for users who rely on Amazon Web Services for hosting and application development.

To subscribe:

1. Go to Billing Details screen in the [Qdrant Cloud Console](https://cloud.qdrant.io/)
2. Select **AWS Marketplace** as the payment method. You will be redirected to the AWS Marketplace listing for Qdrant.
3. Click the bright orange button - **View purchase options**.
4. On the next screen, under Purchase, click **Subscribe**.
5. Up top, on the green banner, click **Set up your account**.

You will be redirected to the Billing Details screen in the [Qdrant Cloud Console](https://cloud.qdrant.io/). From there you can start to create Qdrant database clusters.

### [Anchor](https://qdrant.tech/documentation/cloud-pricing-payments/\#gcp-marketplace) GCP Marketplace

Our [GCP Marketplace](https://console.cloud.google.com/marketplace/product/qdrant-public/qdrant) listing streamlines access to Qdrant for users who rely on the Google Cloud Platform for hosting and application development.

To subscribe:

1. Go to Billing Details screen in the [Qdrant Cloud Console](https://cloud.qdrant.io/)
2. Select **GCP Marketplace** as the payment method. You will be redirected to the GCP Marketplace listing for Qdrant.
3. Select **Subscribe**. (If you have already subscribed, select **Manage on Provider**.)
4. On the next screen, choose options as required, and select **Subscribe**.
5. On the pop-up window that appers, select **Sign up with Qdrant**.

You will be redirected to the Billing Details screen in the [Qdrant Cloud Console](https://cloud.qdrant.io/). From there you can start to create Qdrant database clusters.

### [Anchor](https://qdrant.tech/documentation/cloud-pricing-payments/\#azure-marketplace) Azure Marketplace

Our [Azure Marketplace](https://portal.azure.com/#view/Microsoft_Azure_Marketplace/GalleryItemDetailsBladeNopdl/id/qdrantsolutionsgmbh1698769709989.qdrant-db/selectionMode~/false/resourceGroupId//resourceGroupLocation//dontDiscardJourney~/false/selectedMenuId/home/launchingContext~/%7B%22galleryItemId%22%3A%22qdrantsolutionsgmbh1698769709989.qdrant-dbqdrant_cloud_unit%22%2C%22source%22%3A%5B%22GalleryFeaturedMenuItemPart%22%2C%22VirtualizedTileDetails%22%5D%2C%22menuItemId%22%3A%22home%22%2C%22subMenuItemId%22%3A%22Search%20results%22%2C%22telemetryId%22%3A%221df5537b-8b29-4200-80ce-0cd38c7e0e56%22%7D/searchTelemetryId/6b44fb90-7b9c-4286-aad8-59f88f3cc2ff) listing streamlines access to Qdrant for users who rely on Microsoft Azure for hosting and application development.

To subscribe:

1. Go to Billing Details screen in the [Qdrant Cloud Console](https://cloud.qdrant.io/)
2. Select **Azure Marketplace** as the payment method. You will be redirected to the Azure Marketplace listing for Qdrant.
3. Select **Subscribe**.
4. On the next screen, choose options as required, and select **Review + Subscribe**.
5. After reviewing all settings, select **Subscribe**.
6. Once the SaaS subscription is created, select **Configure account now**.

You will be redirected to the Billing Details screen in the [Qdrant Cloud Console](https://cloud.qdrant.io/). From there you can start to create Qdrant database clusters.

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## frameworks
- [Documentation](https://qdrant.tech/documentation/)
- Frameworks

## [Anchor](https://qdrant.tech/documentation/frameworks/\#framework-integrations) Framework Integrations

| Framework | Description |
| --- | --- |
| [AutoGen](https://qdrant.tech/documentation/frameworks/autogen/) | Framework from Microsoft building LLM applications using multiple conversational agents. |
| [Camel](https://qdrant.tech/documentation/frameworks/camel/) | Framework to build and use LLM-based agents for real-world task solving |
| [Canopy](https://qdrant.tech/documentation/frameworks/canopy/) | Framework from Pinecone for building RAG applications using LLMs and knowledge bases. |
| [Cheshire Cat](https://qdrant.tech/documentation/frameworks/cheshire-cat/) | Framework to create personalized AI assistants using custom data. |
| [CrewAI](https://qdrant.tech/documentation/frameworks/crewai/) | CrewAI is a framework to build automated workflows using multiple AI agents that perform complex tasks. |
| [Dagster](https://qdrant.tech/documentation/frameworks/dagster/) | Python framework for data orchestration with integrated lineage, observability. |
| [DeepEval](https://qdrant.tech/documentation/frameworks/deepeval/) | Python framework for testing large language model systems. |
| [DocArray](https://qdrant.tech/documentation/frameworks/docarray/) | Python library for managing data in multi-modal AI applications. |
| [DSPy](https://qdrant.tech/documentation/frameworks/dspy/) | Framework for algorithmically optimizing LM prompts and weights. |
| [dsRAG](https://qdrant.tech/documentation/frameworks/dsrag/) | High-performance Python retrieval engine for unstructured data. |
| [Dynamiq](https://qdrant.tech/documentation/frameworks/dynamiq/) | Dynamiq is all-in-one Gen AI framework, designed to streamline the development of AI-powered applications. |
| [Feast](https://qdrant.tech/documentation/frameworks/feast/) | Open-source feature store to operate production ML systems at scale as a set of features. |
| [Fifty-One](https://qdrant.tech/documentation/frameworks/fifty-one/) | Toolkit for building high-quality datasets and computer vision models. |
| [Genkit](https://qdrant.tech/documentation/frameworks/genkit/) | Framework to build, deploy, and monitor production-ready AI-powered apps. |
| [Haystack](https://qdrant.tech/documentation/frameworks/haystack/) | LLM orchestration framework to build customizable, production-ready LLM applications. |
| [HoneyHive](https://qdrant.tech/documentation/frameworks/honeyhive/) | AI observability and evaluation platform that provides tracing and monitoring tools for GenAI pipelines. |
| [Lakechain](https://qdrant.tech/documentation/frameworks/lakechain/) | Python framework for deploying document processing pipelines on AWS using infrastructure-as-code. |
| [Langchain](https://qdrant.tech/documentation/frameworks/langchain/) | Python framework for building context-aware, reasoning applications using LLMs. |
| [Langchain-Go](https://qdrant.tech/documentation/frameworks/langchain-go/) | Go framework for building context-aware, reasoning applications using LLMs. |
| [Langchain4j](https://qdrant.tech/documentation/frameworks/langchain4j/) | Java framework for building context-aware, reasoning applications using LLMs. |
| [LangGraph](https://qdrant.tech/documentation/frameworks/langgraph/) | Python, Javascript libraries for building stateful, multi-actor applications. |
| [LlamaIndex](https://qdrant.tech/documentation/frameworks/llama-index/) | A data framework for building LLM applications with modular integrations. |
| [Mastra](https://qdrant.tech/documentation/frameworks/mastra/) | Typescript framework to build AI applications and features quickly. |
| [Mirror Security](https://qdrant.tech/documentation/frameworks/mirror-security/) | Python framework for vector encryption and access control. |
| [Mem0](https://qdrant.tech/documentation/frameworks/mem0/) | Self-improving memory layer for LLM applications, enabling personalized AI experiences. |
| [Neo4j GraphRAG](https://qdrant.tech/documentation/frameworks/neo4j-graphrag/) | Package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python. |
| [NLWeb](https://qdrant.tech/documentation/frameworks/nlweb/) | A framework to turn websites into chat-ready data using schema.org and associated data formats. |
| [OpenAI Agents](https://qdrant.tech/documentation/frameworks/openai-agents/) | Python framework for managing multiple AI agents that can work together. |
| [Pandas-AI](https://qdrant.tech/documentation/frameworks/pandas-ai/) | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language |
| [Ragbits](https://qdrant.tech/documentation/frameworks/ragbits/) | Python package that offers essential “bits” for building powerful Retrieval-Augmented Generation (RAG) applications. |
| [Rig-rs](https://qdrant.tech/documentation/frameworks/rig-rs/) | Rust library for building scalable, modular, and ergonomic LLM-powered applications. |
| [Semantic Router](https://qdrant.tech/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search. |
| [SmolAgents](https://qdrant.tech/documentation/frameworks/smolagents/) | Barebones library for agents. Agents write python code to call tools and orchestrate other agent. |
| [Solon](https://qdrant.tech/documentation/frameworks/solon/) | A lightweight, high-performance Java enterprise framework |
| [Spring AI](https://qdrant.tech/documentation/frameworks/spring-ai/) | Java AI framework for building with Spring design principles such as portability and modular design. |
| [Superduper](https://qdrant.tech/documentation/frameworks/superduper/) | Framework for building flexible, compositional AI apps which may be applied directly to databases. |
| [Sycamore](https://qdrant.tech/documentation/frameworks/sycamore/) | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
| [Testcontainers](https://qdrant.tech/documentation/frameworks/testcontainers/) | Framework for providing throwaway, lightweight instances of systems for testing |
| [txtai](https://qdrant.tech/documentation/frameworks/txtai/) | Python library for semantic search, LLM orchestration and language model workflows. |
| [Vanna AI](https://qdrant.tech/documentation/frameworks/vanna-ai/) | Python RAG framework for SQL generation and querying. |

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Yes
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Thank you for your feedback! 🙏

We are sorry to hear that. 😔 You can [edit](https://qdrant.tech/github.com/qdrant/landing_page/tree/master/qdrant-landing/content/documentation/frameworks/_index.md) this page on GitHub, or [create](https://github.com/qdrant/landing_page/issues/new/choose) a GitHub issue.

On this page:

- [Edit on Github](https://github.com/qdrant/landing_page/tree/master/qdrant-landing/content/documentation/frameworks/_index.md)
- [Create an issue](https://github.com/qdrant/landing_page/issues/new/choose)

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<|page-138-lllmstxt|>
## rag-is-dead
- [Articles](https://qdrant.tech/articles/)
- Is RAG Dead? The Role of Vector Databases in Vector Search \| Qdrant

[Back to RAG & GenAI](https://qdrant.tech/articles/rag-and-genai/)