Posts Tagged aws

Building an EKS cluster requires choosing how your containers will actually run - either on EC2 instances you manage or through AWS Fargate’s pod-by-pod approach. The differences can be pretty dramatic in practice. I’m setting up a demo cluster right now using Pulumi, so let me show you what I mean.

1. Bin Packing
2. Pros and Cons
3. Workload Example: Static Analysis
4. Example: Go Services for E-commerce
5. Fargate vs EC2 Pricing
6. Misconceptions About Fargate
7. Managing Container Orchestration with Pulumi
8. Why Not Both

Here is my Fargate cluster:

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The latest major release of the Pulumi EKS Provider is available now!

This release delivers significant improvements in flexibility, security and introduces new features to enhance your Kubernetes experience on AWS. AWS recently announced the deprecation of two features used by default in Pulumi EKS: the aws-auth ConfigMap and the AL2 operating system. Pulumi EKS v3 addresses these deprecations, enhances the maintainability of the provider, and aligns it with EKS best practices.

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We’re excited to announce the general availability of the new AWS Cloud Control Provider for Pulumi (previously “AWS Native”). AWS is one of the most-used cloud providers across the Pulumi ecosystem, and we are committed to providing the fastest possible access to new AWS capabilities to all Pulumi users. This provider delivers on that promise by providing native support for all resources in the AWS Cloud Control APIs. As part of this launch, we are also renaming the provider from “AWS Native” to “AWS Cloud Control Provider,” based on customer feedback during the preview phase, to provide users with a broader range of advanced cloud management tools exposed by the AWS Cloud Control API. This provider includes the following features and benefits,

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In the introductory blog post, we learned to Create an AI Slack Bot to Chat with Your Data Using Embedchain, Pulumi on AWS, and continued with Adding data to Pinecone using S3, Embedchain, and Pulumi on AWS for an AI Slack bot.

For reference, here’s what our architecture looked like at the end of the second blog post.

To follow along, clone the project, git clone https://github.com/catmeme/arti.git or view it on GitHub.

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In the introductory blog post, we learned to Create an AI Slack Bot to Chat with Your Data Using Embedchain, Pulumi on AWS. However, we made a few concessions in the application logic to illustrate the broader picture of what we were able to achieve combining these three technologies. Now that we have a solid foundation for deploying our Slack bot and querying our data, lets begin moving from proof-of-concept to production-ready, iteratively.

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The integration of artificial intelligence (AI) to improve user experiences is gaining popularity in today’s world. One fascinating application of AI is the creation of chatbots, which can engage users in conversation and provide helpful information or services.

In this blog post, we’ll explore the process of building an AI-powered Slack bot using Embedchain, a Retrieval-Augmented Generation (RAG) framework powered by LangChain. Additionally, we’ll deploy our bot on AWS using Pulumi, a modern infrastructure as code (IaC) platform.

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Building low-code LLM applications just got easier! With Flowise, LocalAI, and Pulumi, you can quickly create, deploy, and manage AI-powered workflows on AWS EKS—without writing complex code.

In this guide, you’ll learn how to:

1. Set up a low-code LLM application using Flowise & LocalAI
2. Deploy it on AWS EKS with Pulumi & TypeScript
3. Create a chatbot workflow and expose it via API

Flowise simplifies LLM orchestration with a drag-and-drop interface, while LocalAI enables local inference without requiring a GPU. Pulumi streamlines cloud infrastructure deployment with just a few lines of code.

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We all know how easy it is to create, deploy, and manage any cloud infrastructure with Pulumi using your favorite programming language. With the rise of artificial intelligence (AI) more and more developers are working on LLM-powered applications and services. And with this, the need to have the same ease of use for creating, deploying, and managing the infrastructure for these applications is growing.

In this blog post, we will show you how to this can be achieved with combining Pulumi and LangServe.

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At AWS re:Invent 2023, AWS announced the new Amazon S3 Express One Zone storage class. This new service provides incredible performance by collocating your S3 buckets closer to the workloads that use the data. Compared to the S3 Standard storage class, the Express One Zone storage class is up to 10x faster, handles 100,000s of requests per second, offers single-digit millisecond latency, and can reduce request costs by 50%. This can be extremely beneficial for data-intensive workloads such as AI/ML, media, finance, realtime, and high-performance computing scenarios. This blog post shows how to get started with Amazon S3 Express One Zone using Pulumi infrastructure as code.

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Redis Enterprise Cloud makes apps faster by providing an in-memory database that enables teams to create a real-time data platform. In this post, you’ll learn how to enable connectivity between Redis Enterprise Cloud and your workloads using Pulumi and the Redis Cloud provider.

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