Sold by: Code Creator
Deployed on AWS
This is a repackaged open source software product wherein additional charges apply for Code Creator integration of Dify and Langfuse, Docker based deployment, private AWS runtime configuration, browser based administrator onboarding, model provider connectivity, and integrated AI observability for traces metrics evaluations latency and cost. Build and monitor AI applications agents and workflows inside your own AWS account. Self hosted Dify and Langfuse AI app studio on Ubuntu that lets you build AI workflows and agents in the browser and observe every request in your own AWS account using your own LLM provider keys.
Overview
Code Creator charges apply for the integrated Dify and Langfuse AI application environment, Docker based deployment, private AWS runtime configuration, browser based onboarding, model provider connectivity, and unified AI workflow observability included with this offering. This product launches a complete AI application studio on a single Ubuntu 24.04 instance powered by Dify and Langfuse. Dify gives you a visual workspace in the browser to design and run AI chatbots agents tools and document workflows without having to manage complex infrastructure. Langfuse adds deep observability so you can trace every request measure cost and latency and compare different prompts and models.
Everything runs with Docker on your EC2 instance inside your AWS account so your data and logs remain under your control. After launch you visit the public IP address to create the first Dify admin account then open the Langfuse URL to create your first observability project. From there you can connect your preferred model providers by adding API keys for services such as OpenAI Anthropic AWS Bedrock Azure OpenAI or other supported LLM and embedding providers.
Use this studio to prototype internal assistants knowledge base search tools and custom AI workflows then monitor how they behave in real time. Dify lets you define prompts tools data sources and knowledge bases through a friendly web interface while Langfuse records traces metrics and evaluations so you can debug prompts tune quality and manage cost.
This AMI is ideal for teams that want an easy starting point for private AI app development and observability without using external SaaS platforms. You bring your own EC2 instance VPC and model API keys and this image provides the ready to use AI studio environment on top.
Highlights
- Build AI apps agents and workflows entirely in the browser with Dify using your own model API keys in your AWS account
- Gain deep visibility into every request with Langfuse including traces metrics and evaluations to debug prompts and control cost
- Runs fully self hosted on a single Ubuntu EC2 instance using Docker so your data logs and configurations remain under your control
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Pricing
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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
t2.xlarge Recommended | $0.02 |
m6i.4xlarge | $0.02 |
m6i.8xlarge | $0.02 |
t3.large | $0.02 |
m6i.2xlarge | $0.02 |
t3.2xlarge | $0.02 |
m6i.xlarge | $0.02 |
m6i.32xlarge | $0.02 |
m6i.24xlarge | $0.02 |
m6i.12xlarge | $0.02 |
Dimensions summary
You pay by the hour based on the EC2 instance size you pick to run this pre-built software. All dimensions bill the same software; they differ only by the compute capacity underneath. The t2 and t3 options (t2.xlarge, t3.large, t3.xlarge, t3.2xlarge) suit lighter or burstable workloads. The m6i options run from m6i.xlarge up through m6i.32xlarge, adding CPU and memory as you move to bigger sizes. Your hourly rate rises with the instance size you choose. Select the size that matches your expected workload, and scale by switching instances.
Top-of-mind questions for buyers
Each dimension maps to one running EC2 instance of the named size. Larger sizes add CPU and memory. You are billed per hour the instance runs. For example, m6i.4xlarge offers more compute than m6i.xlarge, so pick the size matching your workload.
The software meters running instance-hours. A stopped instance does not accrue software charges. You may still pay AWS for attached storage while the instance is stopped, but the hourly software rate applies only while the instance runs.
Each running instance bills on its own hourly rate for its size. Charges add together across all active instances. The largest driver is the size and count of instances you keep running, not the software itself, which stays the same across every dimension.
www.codecreator.com
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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.
Version release notes
n/a
Additional details
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Support
Vendor support
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