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    Dify Premium

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    Sold by: LangGenius 
    Deployed on AWS
    Free Trial
    A cloud-native AI application development platform, empowering mid-sized teams to rapidly design, deploy, and manage scalable AI applications on AWS.
    3.8

    Overview

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    Dify Premium is a ready-to-use, cloud-native edition of Dify, built exclusively for AWS environments to help organizations accelerate innovation. Through an intuitive interface, Dify seamlessly integrates AI workflow orchestration, RAG pipelines, agent capabilities, model management, observability and more, enabling teams to efficiently design, launch, and manage AI-powered applications.

    With Dify Premium, you can deploy the platform on your chosen AWS EC2 instance after purchase via AWS Marketplace, giving you full control over your deployment while leveraging scalable AWS cloud resources. Compared to Dify Community, Dify Premium includes priority email support and branding customization, making it an ideal choice for mid-sized teams seeking greater flexibility and a more polished, professional experience.

    For organizations that require advanced customization, enterprise grade security, multi-tenant management, or deployment within private infrastructure, Dify Enterprise is available as an upgrade.

    Highlights

    • Model Management & Flexibility: Access 1,000+ models, including AWS Bedrock and SageMaker, with centralized management and side-by-side performance comparison. Empower teams to flexibly select and integrate the best models into AI applications, all within Dify intuitive no-code/low-code environment.
    • Agentic Workflows & RAG: Design advanced agentic workflows with multi-step logic, context-aware agents, and cross-modal integration (LLM, TTS, STT). Leverage robust built-in RAG pipelines for seamless data extraction, transformation, and indexing across diverse sources and knowledge bases.
    • Distribution, Branding & LLMOps: Publish AI applications as WebApps, embed into websites, or integrate via API. Apply custom branding for a professional user experience. Monitor performance, analyze metrics, and use LLMOps tools for ongoing experimentation, evaluation, and optimization.

    Details

    Delivery method

    Delivery option
    64-bit (x86) Amazon Machine Image (AMI)

    Latest version

    Operating system
    Ubuntu 22

    Deployed on AWS
    New

    Introducing multi-product solutions

    You can now purchase comprehensive solutions tailored to use cases and industries.

    Multi-product solutions

    Features and programs

    Financing for AWS Marketplace purchases

    AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
    Financing for AWS Marketplace purchases

    Pricing

    Free trial

    Try this product free for 7 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (14)

     Info
    Dimension
    Cost/hour
    c5.2xlarge
    Recommended
    $0.30
    m7a.4xlarge
    $0.30
    m5a.2xlarge
    $0.30
    x1e.xlarge
    $0.30
    r5ad.xlarge
    $0.30
    m5n.xlarge
    $0.30
    m6id.xlarge
    $0.30
    t3.large
    $0.30
    r5.xlarge
    $0.30
    m6i.xlarge
    $0.30

    Vendor refund policy

    No refund is available.

    Custom pricing options

    Request a private offer to receive a custom quote.

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    Legal

    Vendor terms and conditions

    Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .

    Content disclaimer

    Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.

    Usage information

     Info

    Delivery details

    64-bit (x86) Amazon Machine Image (AMI)

    Amazon Machine Image (AMI)

    An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.

    Additional details

    Usage instructions

    [First-time Setup]: If this is your first time accessing Dify, enter the Admin initialization password (set to your EC2's instance ID) to start the set up process. [Accessing Dify Premium]: After the AMI is deployed, access Dify via the instance's public IP found in th EC2 console (HTTP port 80 is used by default) [Upgrading Dify Premium]: In the EC2 instance, run the following commands: 1.git clone <https://github.com/langgenius/dify.git> /tmp/dify 2.mv -f /tmp/dify/docker/* /dify/ 3. rm -rf /tmp/dify 4. docker-compose down 5. docker-compose pull 6. docker-compose -f docker-compose.yaml -f docker-compose.override.yaml up -d [Customizing Dify Premium]: Refer to the help documentation: https://docs.dify.ai/en/self-host/quick-start/docker-compose#customize-dify .

    Support

    Vendor support

    Priority email support is included with your subscription.

    For faster resolution, we recommend reviewing the Dify Premium guide first: https://docs.dify.ai/en/self-host/platform-guides/dify-premium 

    If you still need technical assistance, please contact support@dify.ai . To help us assist you efficiently, please mention that you are a Dify Premium subscriber and include your AWS Account ID in your email.

    *Note: The default Dify Premium deployment may not run the latest version. To upgrade to the latest release, please refer to: https://docs.dify.ai/en/self-host/platform-guides/dify-premium#upgrading 

    You can find version details in our GitHub releases: https://github.com/langgenius/dify/releases 

    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

    Product comparison

     Info
    Updated weekly
    By LangGenius
    By Dataloop AI/GenAI development platform

    Accolades

     Info
    Top
    10
    In AIOps, Generative AI
    Top
    50
    In Data Preparation
    Top
    10
    In Edit/Processing-Text, Natural Language Processing, Generative AI

    Overview

     Info
    AI generated from product descriptions
    Model Integration and Management
    Access to 1,000+ models including AWS Bedrock and SageMaker with centralized management and side-by-side performance comparison capabilities.
    Agentic Workflows and RAG Pipelines
    Design advanced agentic workflows with multi-step logic, context-aware agents, and cross-modal integration (LLM, TTS, STT) combined with built-in RAG pipelines for data extraction, transformation, and indexing.
    AI Application Orchestration
    Seamless integration of AI workflow orchestration, agent capabilities, model management, and observability through an intuitive no-code/low-code interface.
    Application Distribution and Deployment
    Publish AI applications as WebApps, embed into websites, or integrate via API with custom branding options for professional user experience.
    Performance Monitoring and Optimization
    LLMOps tools for performance monitoring, metrics analysis, experimentation, and ongoing optimization of deployed AI applications.
    Generative AI and RAG Pipeline Deployment
    Deploy RAG pipelines for GenAI solutions including summarization, chatbots, and data preparation with support for RLHF and RLAIF incorporation.
    Unstructured Data Management and Analysis
    Explore and analyze unstructured data from diverse sources with automated preprocessing, embeddings generation, and similarity identification capabilities.
    Model Versioning and Experimentation
    Version, experiment, compare, and fine-tune AI models with production deployment capabilities without requiring external tool integration.
    Workflow Orchestration with Drag-and-Drop Interface
    Orchestrate data, models, applications, and human feedback using a drag-and-drop interface or Python SDK with pre-created pipeline templates.
    Enterprise Security and Compliance
    Implement GDPR, ISO 27001, ISO 27701, and SOC 2 Type II compliance with RBAC, SSO, 2FA, AES-256 encryption, and granular audit trail capabilities.
    Multi-Agent Orchestration
    Build and manage multiple agentic generative AI solutions from a single platform with ability to assign Large Language Models to agents and switch between LLMs for optimization and cost savings
    Retrieval Augmented Generation with Governance
    Point and click governance and guardrails for Retrieval Augmented Generation outputs with Provenance, Coverage and Confidence Scores, and Semantic Analytics visibility
    Hallucination and PII Mitigation
    Enterprise observability and control mechanisms to mitigate risks of Large Language Model hallucination, misinformation, and Personal Identifiable Information loss
    Dynamic Prompt Generation
    Fully customized Retrieval Augmented Generation prompt generation capability to create and run dynamic generative AI prompts against proprietary data without code
    LLM Performance Monitoring
    Real-time monitoring of Large Language Model token usage, operational costs, and cost comparison capabilities across multiple Large Language Models

    Contract

     Info
    Standard contract
    No
    No

    Customer reviews

    Ratings and reviews

     Info
    3.8
    2 ratings
    5 star
    4 star
    3 star
    2 star
    1 star
    50%
    0%
    50%
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    2 AWS reviews
    Mihir Jadhav

    Automation has transformed HR, CRM, and ERP workflows and now saves significant time and effort

    Reviewed on Mar 22, 2026
    Review from a verified AWS customer

    What is our primary use case?

    My main use case for Dify  is automation like HR automation, CRM  automation, and ERP  level management, which have been integrated into SaaS applications.

    I have used Dify for HR automation and specific examples related to that can be provided.

    What is most valuable?

    Dify offers several best features including being very seamless, open source, having multiple plugins and a marketplace to use, and allowing integration of multiple AI models with full flexibility.

    Dify's flexibility helps with automations or integrations since it is very seamless and has a very simple UI so anyone can use it, without anything too complex.

    Dify stands out because it supports all the major AI platforms and plugins, and it has a marketplace where there are multiple things that can be integrated, which are official partners as well.

    Dify has positively impacted the organization because accuracy has been improved, and the time and complexity in flows that were manual are now automated, from HR automation to ERP  level transactions, including subscription management in the SaaS application, monitoring, and analytics.

    There are no exact numbers available, but the time has drastically been reduced and the performance has improved since Dify was introduced in the system.

    What needs improvement?

    No features are needed that Dify doesn't have as it has all the features required.

    Everything is working well with Dify. The only improvement would be if Dify provided an SMTP server that could be connected to automate Dify workflow management, as that would be a great option.

    For how long have I used the solution?

    I have been using Dify for the past one year.

    What do I think about the stability of the solution?

    Dify is stable and scalable, and no issues or problems have been encountered as of now, as it is in production.

    What do I think about the scalability of the solution?

    Dify's scalability is good and it handles growth or increased workloads effectively, depending upon the resources available. If server capacity is increased, Dify scales accordingly.

    How are customer service and support?

    Dify's customer support has not been directly contacted as GitHub  issues and the community have helped with any issues faced.

    How would you rate customer service and support?

    Negative

    Which solution did I use previously and why did I switch?

    N8n was previously used before Dify.

    Dify was chosen over n8n  because Dify has more marketplace, plugins, and integrations than n8n , and it is lightweight compared to n8n, which is resource-heavy.

    How was the initial setup?

    Dify was not purchased through the AWS Marketplace  but rather the Git  repository was cloned.

    Dify is free to use and has a free license from GitHub  under a Dify open-source license based on Apache 2.0, so no hurdles have been felt in deploying it or using it.

    What was our ROI?

    The automation work has saved time. For example, if a task would require an hour, it can now be done in seconds, so the time saved varies depending on the task. Exact numbers are not available, but it has saved a lot of time and also money, reducing the manual work that was done earlier.

    What's my experience with pricing, setup cost, and licensing?

    Dify is free to use and has a free license from GitHub under a Dify open-source license based on Apache 2.0, so no hurdles have been felt in deploying it or using it.

    Which other solutions did I evaluate?

    N8n, Make .com, and Zapier  were evaluated before choosing Dify. Make .com and Zapier  are paid and Dify is absolutely free. Since n8n is also free but a little more complex than Dify, Dify was chosen over any other platform.

    What other advice do I have?

    Dify should be considered by others looking into using it if they want a lightweight platform with feature-rich plugins to integrate their workflow and manage multiple automation tasks using a single platform. This is the best option available. The overall review rating for Dify is 9 out of 10.

    Which deployment model are you using for this solution?

    Private Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

    Amazon Web Services (AWS)
    Rusira Sathnindu

    Visual workflows have accelerated our agent POC while better UI and observability still need work

    Reviewed on Feb 17, 2026
    Review from a verified AWS customer

    What is our primary use case?

    We used Dify  to create and test an agentic workflow and an AI agent model with some of the tools and RAG models. We used it to test how it works and how to implement it for part of our core product in our company.

    We created an AI marketing agent using Dify , and the idea was that it can look into your marketing platforms, for example, Facebook, Google Ads , and Google Analytics. Those are the marketing platforms that we targeted, and instead of manually moving through dashboards, the idea was an agent will have access to your marketing data and can go through this data and provide you with reports and insights and suggestions. We used Dify's visual workflow builder to build this.

    I tested Dify about six months ago for some of the tasks that we had for building some kind of a product, and I used it for two months and then did not use it very much after that.

    What is most valuable?

    The visual workflow builder Dify offers is really helpful, and you do not have to code everything. You can use it to connect nodes and make a flow of how your agent should work. Dify has RAG functionality that is also in-built, and those are the features that we used, and those two features were very good.

    The visual workflow builder made my work easier because it saved us time since we did not have to code everything. One of the other interesting things was that it was really easy to show or present to a non-technical person. Our CEO was non-technical, so for him, it was really easy to show it as a diagram and explain how it works, and he could even do some edits. The ability for non-technical people to look into it is a really great use case.

    Using Dify has positively impacted our organization because we were able to cut down on some development time and do a lot of testing in a very small time period. Initially, we had about two weeks of time to implement the whole thing, but that was cut down to two days of time through using Dify.

    What needs improvement?

    My personal experience with Dify's UI is that it is not my favorite, as it can be improved a little bit, and sometimes the UI feels a little bit buggy. I am not sure if that is because it was a self-hosted version. The documentation can also be improved a little bit more. I think not a lot of people are using Dify currently, so that is why the documentation is not very great. If the documentation was improved, that would also be a really good thing.

    Currently, Dify could improve by offering better observability like other platforms. We currently use OpenAI Agents SDK, which requires you to build everything by code, but the observability is really good. It has OpenAI Traces, and you can basically trace everything for a conversation. If Dify had that kind of tracing functionality, that would be great.

    For how long have I used the solution?

    I actually tested Dify about six months ago for some of the tasks that we had, it was for building some kind of a product, And, I used it for like two months and then, did not use it very much after that.

    What do I think about the stability of the solution?

    Dify is stable.

    What do I think about the scalability of the solution?

    We used Dify only for the POC, so we did not expose it to a lot of workload. One of the main concerns that we had is that it might not be very scalable because we are hosting it in a self-hosted environment, and we have to configure the architecture and everything. Rather than using a cloud-hosted platform, using a self-hosted platform means there can be scalability issues. We anticipated there would be scalability issues, but we did not go for that scale. While testing, sometimes because of the limitations of the server, it crashed, stopped working, or got delayed, so it has a little bit of scalability problems.

    What was our ROI?

    We used Dify for testing out a POC and different ways of how to implement the agent, so there is no direct return on investment, as the investment was zero, so there is no return.

    What's my experience with pricing, setup cost, and licensing?

    My experience with pricing, setup cost, and licensing is that it was free to use. We were able to get the free license from the GitHub  release and then deploy it in our organization.

    Which other solutions did I evaluate?

    We evaluated another option called Chatbot Kit, but I am not very sure about that because I do not remember everything. We also used another product, either Chatbot Kit or ManyChat, and then after Dify, we switched back to OpenAI Agents SDK.

    What other advice do I have?

    The visual workflow builder made my work easier because it saved us time since we did not have to code everything. One of the other interesting things was that it was really easy to show or present to a non-technical person. Our CEO was non-technical, so for him, it was really easy to show it as a diagram and explain how it works, and he could even do some edits. The ability for non-technical people to look into it is a really great use case.

    Dify is self-hostable, so we did not have to pay anything. We just had to host it, and we really own the whole thing, and we can see the code as well. Self-hostability is another great feature.

    If you are a startup or someone who is trying to run a POC related to agents, you should use Dify. It is a really good alternative that you can use to test things out and build a POC. After that, make sure to move to a different platform because if you need to scale it up and if you need custom steps, that is the advice I would have. I would rate this review a six out of ten.

    Which deployment model are you using for this solution?

    Private Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

    Amazon Web Services (AWS)
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