A practical guide to the Azure AI search Confluence connector in 2025

So, you’re looking to connect your Confluence knowledge base to an AI agent using Microsoft’s tools. It’s a common goal these days: wiring up internal docs to a chatbot that can spit out instant, accurate answers for your team or customers. This whole process is usually part of a bigger plan involving Retrieval-Augmented Generation (RAG), which is a fancy way of saying the AI looks things up before it talks.

But here’s the catch: creating a solid Azure AI Search Confluence connector is a lot trickier than it sounds. It’s not a simple plug-and-play situation.

This guide is here to walk you through the weeds. We’ll look at the different ways you can tackle this, the hidden difficulties, and what it’s really going to cost you. We’ll also show you a much more straightforward path to get the same result, minus the engineering headaches.

What is an Azure AI Search Confluence connector?

First, let’s get on the same page. Azure AI Search is Microsoft’s heavy-duty cloud search service. Think of it as a powerful engine you can use to index huge amounts of data and search through it using all sorts of advanced methods, like keyword, vector, and hybrid search. It’s a key building block for custom AI apps, especially chatbots that need to pull answers from your company’s documents.

A Confluence connector is the piece that acts as a bridge between that powerful search engine and your Confluence wiki. Without a connector, Azure AI Search has no idea your Confluence content even exists. This little piece of software has a big job:

• It has to securely log into Confluence.

• It needs to crawl through all your pages, blog posts, and attachments.

• It has to pull out the clean text and metadata from all that content.

• Crucially, it must understand and respect your Confluence permissions so people only see search results they’re allowed to see.

• It needs to keep the search index updated when someone adds or changes a page in Confluence.

The main problem? Microsoft doesn’t give you a ready-made connector for this. That leaves teams scrambling to figure out how to build this bridge themselves.

The challenge: Three ways to build an Azure AI Search Confluence connector

Since there’s no big red "connect" button, you’re left with three main options. Each one comes with its own set of trade-offs in terms of how much time, money, and sanity you’re willing to spend.

The DIY approach

For many engineering teams, the first instinct is to build it themselves. This usually means firing up a code editor and writing a custom script (often in Python) that uses the Confluence REST API to grab data and the Azure SDK to stuff it into an Azure AI Search index. You might use a framework like LangChain to give you a head start, but it’s still a serious project.

What you’re signing up for:

• Wrangling authentication for both Confluence (API tokens) and Azure (service principals).

• Writing code that can crawl through all your Confluence spaces without missing anything.

• Parsing messy HTML to get clean text that an AI can actually understand.

• Figuring out the best way to "chunk" long documents into smaller pieces for the AI model.

• Generating vector embeddings for your content using a service like Azure OpenAI.

• Building a system for incremental updates, so you don’t have to re-index your entire Confluence site every night.

The downsides:

• It’s a ton of work upfront. This isn’t a side project for an intern; it’s a full-blown development effort that requires experienced engineers.

• You own it forever. When Atlassian or Microsoft updates their APIs (and they will), your script will break. Guess who gets to fix it?

• Permissions are a nightmare. Trying to perfectly mirror Confluence’s complex user and space permissions in your search results is incredibly difficult. Getting it wrong is a major security risk.

The platform approach

You might think, "Surely Microsoft has something for this?" Well, kind of, but not really. The tools they offer are for different use cases and don’t quite solve this specific problem.

• Microsoft 365 Copilot Connector: Microsoft does have a Graph connector for Confluence Cloud. But this is designed to pull Confluence data into Microsoft Search and M365 Copilot, not into your own custom Azure AI Search instance. It’s for their ecosystem, not your app. Plus, it has known issues, like delays in syncing permissions.

• Azure Logic Apps: You can technically use Logic Apps to create workflows that move data around. But it’s built for simple, trigger-based tasks, not for the heavy lifting of continuously indexing an entire knowledge base like Confluence. It’s like using a scooter to move a piano.

This video demonstrates how to use Azure AI Search as a knowledge source in Copilot Studio, highlighting some of the platform-level tools available.

The third-party approach

Because this is such a common problem, a few companies have built their own connectors to sell. Companies like BA Insight offer products designed to handle all the tricky parts for you.

What that looks like:

You buy a license, point the tool at your Confluence and Azure accounts, and let it do its thing.

The downsides:

• Sticker shock. These are enterprise-grade tools with enterprise-grade price tags. Be prepared for a hefty bill.

• It’s a black box. You don’t have much control over how the indexing happens, which can be a problem if you need to customize the process.

• The dreaded sales cycle. You can’t just swipe a credit card and get started. It usually involves scheduling demos, talking to sales reps, and navigating a long onboarding process.

Understanding the costs and limitations

Let’s say you’ve managed to solve the connector puzzle. Congratulations! Now you have to deal with the operational side, and Azure AI Search’s pricing model can be a real head-scratcher.

Your Azure AI Search bill isn’t just one number. It’s a mix of different charges that can fluctuate wildly.

This kind of multi-variable pricing makes it almost impossible to predict your monthly spend. If your support bot has a busy week, your Azure bill could spike without warning. You can see the full breakdown on the Azure AI Search pricing page and get a sense of the complexity yourself.

A simpler alternative to an Azure AI Search Confluence connector: eesel AI

If reading all of that made you feel a bit exhausted, you’re not alone. The truth is that while tools like Azure AI Search are powerful, they demand a huge investment in time, money, and specialized engineering skills.

For most teams who just want a working AI solution now, there’s a much more direct route.

eesel AI is a platform built from the ground up to unify all your company’s knowledge without the complicated setup. Instead of you having to stitch together connectors, search services, and AI models, you get one solution that handles everything.

Here’s why it’s a simpler alternative:

• One-click Confluence integration. No more custom scripts or expensive third-party tools. You can connect your Confluence account in a few minutes, right from the eesel AI dashboard. The platform takes care of all the indexing, chunking, and vectorizing behind the scenes.

• Genuinely self-serve. You don’t have to schedule a demo or talk to a salesperson to get going. You can sign up, connect knowledge sources like Confluence, Google Docs, and Zendesk, and have a functional AI agent in less than an hour.

• All your knowledge, not just one source. Why stop with Confluence? Your company’s brain is scattered everywhere. eesel AI lets you connect all of it, your help desk, internal wikis, Google Drive, Slack threads, and more, into a single source of truth for your AI agents.

eesel AI simplifies the process by allowing one-click integrations with multiple knowledge sources, not just a single Azure AI Search Confluence connector.

• Clear, predictable pricing. eesel AI has simple, flat-rate pricing plans. There are no confusing formulas or per-query fees to worry about. You know exactly what your bill will be each month, which makes budgeting and proving ROI a whole lot easier.

With eesel AI, you can build an internal AI assistant for your team in Slack or an autonomous agent for your help desk that actually resolves tickets using your Confluence articles, all without writing a line of code.

An example of an eesel AI-powered chatbot answering questions directly in Slack, a simpler alternative to a custom Azure AI Search Confluence connector build.

Azure AI Search Confluence connector: Focus on the goal, not the plumbing

At the end of the day, the goal is to get fast, accurate answers from your company’s knowledge. While building a custom solution with an Azure AI Search Confluence connector is possible, it pulls your focus away from solving the business problem and throws you into the world of managing complex infrastructure.

Whether you choose the DIY, platform, or third-party path, you’re signing up for major trade-offs in cost, time, and ongoing maintenance.

For most teams, a streamlined platform like eesel AI is simply a faster way to get there. By offering a true self-serve experience, one-click integrations for sources like Confluence, and pricing that makes sense, it lets you deploy a genuinely useful AI assistant in minutes, not months.

Ready to see how easy it can be? Start your free eesel AI trial today and connect your Confluence data in the next few minutes.