A Guide to the Future of AI Development.
As AI capabilities explode across modalities (think: text to image to audio), the developer experience hasn’t kept pace. Teams are duct-taping solutions together across cloud providers, large language models (LLMs) APIs, vector databases and brittle control loops. Even companies with strong engineering muscle struggle to maintain velocity.
What’s missing is a platform-level solution that abstracts these AI concerns the same way traditional Platform as a Service (PaaS) abstracts infrastructure.
This is the space AI Platform as a Service (AI PaaS) aims to fill. It brings the core PaaS principles of simplicity, scalability and developer-first tooling to modern AI building blocks.
Let’s explore what an AI PaaS is and how it lets you ship production-grade AI applications without reinventing your entire stack.
What Is an AI PaaS and Why Is It Necessary?
An AI PaaS does exactly what it says: It’s a platform that helps developers build, deploy and operate AI-powered applications in the cloud without needing to manage models, orchestration, pipelines or infrastructure themselves. It builds on the foundation of traditional PaaS but extends it with AI-native features like model access, retrieval pipelines, agent orchestration and evaluation tools.
These platforms fill a critical gap, as many AI projects never make it to production. Gartner predicts that up to 40% of agentic AI initiatives will fail by 2027, often due to integration costs, lack of observability or deployment complexity. An AI PaaS addresses these challenges by providing opinionated, scalable defaults.
Core Foundations of A Modern PaaS
Every PaaS needs to get a few core things right, whether you’re building a CRUD app or a conversational agent. They are:
Scalability: The infrastructure can easily scale to handle changes in compute-intensive AI workloads.
Security: All tenants are isolated with proper access controls to ensure models, data and agents remain secure. Secrets are all held to the principle of least privilege and managed securely.
Containerization: Agents and tooling are in containers for consistent deployments.
Orchestration: No manual configuration of infrastructure. Code is built and deployed automatically.
Data: Databases are automatically provisioned, scalable and provide secure access. This can mean vector databases, customer data or any other content required by the AI.
Observability: Latency, usage patterns and error management are visible through OpenTelemetry or a similar tool. AI workflows also need observability in prompt flows and results for debugging LLM results.
These are the table stakes. But building with AI introduces a new layer of complexity. Let’s look at specific features required for an AI PaaS.
Security: All tenants are isolated with proper access controls to ensure models, data and agents remain secure. Secrets are all held to the principle of least privilege and managed securely.
Containerization: Agents and tooling are in containers for consistent deployments.
Orchestration: No manual configuration of infrastructure. Code is built and deployed automatically.
Data: Databases are automatically provisioned, scalable and provide secure access. This can mean vector databases, customer data or any other content required by the AI.
Observability: Latency, usage patterns and error management are visible through OpenTelemetry or a similar tool. AI workflows also need observability in prompt flows and results for debugging LLM results.
These are the table stakes. But building with AI introduces a new layer of complexity. Let’s look at specific features required for an AI PaaS.
Essential Features for a Minimum-Viable AI PaaS
To begin building an AI PaaS, the minimal tools required include model inference, retrieval pipelines and Model Context Protocol (MCP) scaffolding.
AI-powered features are centered around LLMs. An LLM offers conversational generative AI, which has become commonplace since the launch of ChatGPT in 2022. An AI PaaS should provide seamless access to various machine learning (ML) models. Models all have different strengths and weaknesses, so having access to multiple models provides the most flexibility for teams building AI agents.
When an LLM provides a response, a control loop should be in place to monitor the responses and verify their quality. Developers can create customer-defined heuristics and rules that will be used to evaluate the response. This could involve hard-coded guardrails or comparing the results of multiple LLMs to achieve consensus.
control loop will pass the response on to the next step of the model.
Model Context Protocol for Connecting Data and Tools
LLMs are powerful tools and can converse with users on many disparate topics. To power a generative AI that is useful for an organization, additional data must be constantly supplied to ensure timely and accurate responses.
MCP is a standardized approach to connect external tools into an AI system to provide additional data or knowledge. MCP servers make it easy to securely connect existing data tools (both internal and external) to incorporate new data.
Additionally, the MCP acts as a service directory. When a query is sent to the AI, it formulates its response based on knowing where the data is located and how it can be retrieved and formatted into a response. This allows existing applications and agents to connect to the MCP.
What To Expect from Enterprise-Grade AI PaaS
A credible AI PaaS goes beyond inference. It helps teams build responsibly, iterate quickly and scale with confidence. Here’s what to expect from platforms that can support long-term, production-grade AI use:
Retrieval Augmented Generation
One common data storage tool for external knowledge is retrieval augmented generation (RAG). The RAG database is generally a vector database containing enterprise data encoded specifically to interact quickly with the LLM. For example, Heroku’s Postgres pgvector provides seamless vector database support without the need for additional database tooling.
What To Expect from Enterprise-Grade AI PaaS
A credible AI PaaS goes beyond inference. It helps teams build responsibly, iterate quickly and scale with confidence. Here’s what to expect from platforms that can support long-term, production-grade AI use:
One common data storage tool for external knowledge is retrieval augmented generation (RAG). The RAG database is generally a vector database containing enterprise data encoded specifically to interact quickly with the LLM. For example, Heroku’s Postgres pgvector provides seamless vector database support without the need for additional database tooling.
For instance, loan processing and approvals at 1West were a slow and manual process. After training a machine learning model using Heroku’s AI PaaS to work with a vast number of data sources, loan processing was cut from days to minutes.
RAG Data Pipelines for Updating RAG Databases
Just as LLMs can quickly become outdated and provide incorrect or stale responses on their own, the same can happen with data in the RAG database. To maintain accuracy in the AI application, the RAG database must be continually refreshed to reflect new or changing data. This requires automated workflows for document processing. These workflows should integrate seamlessly with existing systems and handle all processing steps efficiently.
As companies integrate AI-powered tools into their stacks, many development teams lack the MLOps, governance and orchestration skills necessary to deploy AI in production. Using Heroku’s AI PaaS jumpstarts the process of building, deploying, operating and scaling AI-powered applications.
Heroku Vibes AI code generation allows you to create and deploy to Heroku with natural language. Heroku’s Managed Inference and Agents provides curated AI models to build upon. The Heroku MCP Server makes it straightforward for agents to access Heroku resources like logs, provision add-ons and scale applications. A custom MCP server deployed on Heroku can give access to existing systems to your AI service.
- LLM support is provided by Heroku Managed Inference and Agents, with access to multiple LLM inference models.
- Heroku AppLink provides secure connections to Agentforce (the agentic layer of the Salesforce platform), with connections to Salesforce Flows, Apex and Data Cloud.
- Heroku’s AI native tools integration enables developers to build new apps, enhance existing ones and create new AI agents using AI-generated code. This means that an AI agent running on Heroku can securely interact with sensitive enterprise data, leveraging state-of-the-art AI while keeping your data secure.
Deploying AI apps should be as easy as pushing a web app. With opinionated defaults and managed services, Heroku continues to evolve alongside developers, providing a streamlined, integrated platform experience.
Heroku is bringing its decades of expertise in deploying applications in the cloud to help developers quickly launch AI technologies. To learn more about Heroku and AI PaaS, watch the demo on YouTube or follow on LinkedIn for updates.
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