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AI

The Next Generation of the Heroku Platform

Over the past year, Heroku has been on a journey of reflection as we rebase the platform to address the changing needs of app teams toward the future without disrupting your business. In the Heroku way, we want to be thoughtful about your experience as we evolve.

When we started Heroku, it was the early days of cloud computing, before Docker and Kubernetes were household names in IT. We launched Heroku (and the platform-as-a-service category) to help teams get to the cloud easily with an elegant user experience in front of a powerful platform that automated a lot of the manual work that slowed teams down. To do that then, we had to build a lot of the tooling ourselves, like orchestration and self-hosting the databases in AWS. ‌The platform delivered customers the outcomes they needed to deploy apps quickly and scale effortlessly in the cloud‌—all without having to worry about how the platform worked.

Heroku AI | Managed Inference and Agents

Over the last couple of years, we’ve repeatedly heard the question “who will build the Heroku of AI?”. The answer to that question is that Heroku will, of course.

We are excited to bring AI to the Heroku platform with the pilot of Managed Inference and Agents, delivered with the graceful developer and operational experience and composability that are the heart of Heroku.

Heroku’s Managed Inference and Agents provide access to leading AI models from …

How to Create an AI Agent With Heroku and Agentforce

Heroku is a powerful general-purpose PaaS offering, but when combined with the broader Salesforce portfolio, it excels in unlocking and unifying customer data, regardless of its age, location, size, or structure. Salesforce customers turn to Heroku when they need to leverage high data volumes from sources such as consumer web or mobile apps or when they need scalable compute resources to access and analyze complex data in real time. In this blog, we’ll explore how …

Heroku Integration with Amazon Q Developer Command Line

Today, we’re announcing the integration of the Heroku CLI with Amazon Q Developer. This integration, a result of our expanded Salesforce/AWS partnership, enables Amazon Q Developer command line suggestions of Heroku commands. This integration empowers Heroku users to auto-complete commands, thereby saving time and eliminating error-prone manual configurations of apps.

Developers configure and manage their applications through a command line interface (CLI), especially during development when working within their integrated development environment (IDE). …

Building a GPT Backed by a Heroku-Deployed API

How to connect your GPT on OpenAI to a backend Node.js app

Late in 2023, OpenAI introduced GPTs, a way for developers to build customized versions of ChatGPT that can bundle in specialized knowledge, follow preset instructions, or perform actions like reaching out to external APIs. As more and more businesses and individuals use ChatGPT, developers are racing to build powerful GPTs to ride the wave of ChatGPT adoption.

Working with ChatGPT Functions on Heroku

How to Build and Deploy a Node.js App That Uses OpenAI’s APIs

Near the end of 2023, ChatGPT announced that it had 100M weekly users. That’s a massive base of users who want to take advantage of the convenience and power of intelligent question answering with natural language.

With this level of popularity for ChatGPT, it’s no wonder that software developers are joining the ChatGPT app gold rush, building tools on top of OpenAI’s APIs. Building and deploying a GenAI-based app is quite easy to do—and we’re going to show you how!

Enhancing Heroku Postgres with pgvector: Generating AI Insights

We’re pleased to introduce the pgvector extension on Heroku Postgres. In an era where large language models (LLMs) and AI applications are paramount, pgvector provides the essential capability for performing high-dimensional vector similarity searches. This allows Heroku Postgres to quickly find similar data points in complex data, which is great for applications like recommendation systems and prompt engineering for LLMs. As of today, pgvector is fully compatible with all Production-tier databases running Postgres 15 …

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