I tested 5 powerful Databricks alternatives for building AI agents in 2025

Databricks is an absolute powerhouse for data science, but when my goal was to build a smart AI agent for our support team, it felt like using a sledgehammer to crack a nut. The sheer complexity and cost were just too much for what I thought should be a simple task: an AI that could understand our business and actually help our customers.

That little frustration sent me down a rabbit hole, hunting for more focused, affordable, and user-friendly Databricks alternatives. I was looking for tools built specifically for creating AI agents that can handle customer service, ITSM, and internal Q&A. I wasn’t trying to process petabytes of data; I just wanted something that could deliver real business value, and fast.

This article is what I found. I’ll break down five different ways to get the job done, from all-in-one platforms to rolling your own solution, so you can pick the right tool without needing a PhD in data engineering.

What is Databricks (and why you might need Databricks alternatives)?

Databricks is a unified data and AI platform, built by the same folks who created Apache Spark. It’s designed for massive-scale data processing, warehousing, and machine learning. Its big idea is the "lakehouse," which tries to give you the best of both worlds: the flexibility of a data lake and the performance of a data warehouse.

And honestly, it’s brilliant at what it does. If you’re a giant company trying to wrangle data engineering and data science workflows to train complex models on mountains of data, Databricks is probably on your shortlist.

But for the very specific goal of building a functional AI support agent, it comes with a few major headaches:

• It's a beast to set up. Getting started is not a walk in the park. You need specialized data engineering and machine learning skills to configure clusters, manage notebooks, and get models into production. It’s not something a support or IT manager can just pick up and run with.

• The costs can get out of hand. The pricing is based on Databricks Units (DBUs), which measure how much processing power you’re using per second. For an AI agent that needs to be "on" all the time for customers, those costs can become unpredictable and eye-wateringly high.

• It takes forever to see results. The journey from signing up to having an agent that can handle real customer questions can take months of development, training, and fine-tuning.

• It doesn't connect to where knowledge lives. Databricks is great with neat, structured data in its lakehouse. But it struggles to connect in real-time to the messy, unstructured knowledge where knowledge lives, in old helpdesk tickets, sprawling Google Docs, and chaotic Slack threads.

For a lot of us, the point isn't just to build an AI, it's to solve a business problem. And for that, you need a different kind of tool.

My criteria for the best Databricks alternatives for AI agents

To find the right tool for the job, I completely ignored the big data benchmarks. Instead, I focused on what actually matters when you want to automate support and help your team. My goal wasn't to find a platform that could process more data, but one that could deliver business value faster and more efficiently.

Here’s what I was looking for:

• Time to value: How fast can you get from zero to a working AI agent? I wanted solutions that could show me results in minutes or hours, not weeks or months.

• Ease of use: Is the platform genuinely self-serve for someone who isn't a coder, or does it require a whole team of developers to get anything done?

• Knowledge connectivity: How well does it pull in knowledge from all the different places where information is scattered? I’m talking about helpdesks, wikis, chat tools, and internal docs.

• Workflow customization: Can you actually control the AI's personality, tell it what to do, and decide which questions it should handle versus pass off to a human?

• Transparent pricing: Is the cost predictable? I was looking for clear, upfront pricing without confusing metrics or sneaky fees that punish you for doing well.

The top 5 Databricks alternatives for AI agents at a glance

After digging in, I found the options generally fall into a few different camps. Here’s a quick rundown:

• eesel AI: The best fit for teams who want powerful, self-serve AI agents that work with their existing tools. It’s shockingly simple to set up and has a predictable subscription price.

• Helpdesk-Native AI: A good option for teams who live and breathe inside their helpdesk (like Zendesk or Intercom) and don’t need the AI to know much about the outside world. Setup is simple, but it’s usually an add-on to your helpdesk bill.

• The DIY Stack: For large companies with dedicated AI teams who need total control and have the budget to match. It's incredibly complex to build and the "free" open-source parts come with high infrastructure and developer costs.

• Cloudera: This one is for huge enterprises that need on-premise or hybrid-cloud data management. Like Databricks, it’s very complex and comes with an enterprise-level price tag.

• Cloud Data Warehouse AI: This makes sense for teams who are all-in on a single cloud provider like AWS or GCP. It can be complicated, with a pay-as-you-go model that’s hard to predict.

A detailed look at 5 powerful Databricks alternatives

Alright, let's get into the nitty-gritty of what makes each of these options tick, including their strengths, weaknesses, and how much they’ll set you back.

1. eesel AI

eesel AI is an AI platform built from the ground up for customer service, ITSM, and internal support. Instead of making you rip and replace your current systems, it just plugs into them. It connects to your helpdesk, chat tools, and knowledge docs to automate support, help agents write replies, and power chatbots.

Why it's on the list: It's the complete opposite of Databricks in terms of complexity and time-to-value. I was genuinely surprised when I signed up and built a working AI agent that learned from our real business data, like past Zendesk tickets and internal Confluence pages, in under 15 minutes.

What I liked:

• It’s truly self-serve: You can go from sign-up to a live AI agent without talking to a salesperson or sitting through a mandatory demo. It’s built for normal people to use.

• It unifies all your knowledge: It instantly learns from your old tickets, macros, help center articles, Google Docs, Slack history, and over 100 other sources.

• You have total control over the workflow: A simple prompt editor lets you define the AI's personality, tone of voice, and even set up custom actions (like looking up an order in Shopify or creating a Jira ticket).

• You can test it risk-free: You can run your AI against thousands of your historical tickets to see exactly how it would have performed. This gives you a clear idea of your ROI before it ever talks to a real customer.

A screenshot of the eesel AI simulation feature, which provides a safe testing environment and is one of the powerful Databricks alternatives.::A screenshot of the eesel AI simulation feature, which provides a safe testing environment.::A screenshot of the eesel AI simulation feature, which provides a safe testing environment and is one of the powerful Databricks alternatives.

What to consider: It's not a general-purpose big data platform. If you need to run complex data science queries or manage a massive data lake, this isn't the tool for you. It’s laser-focused on conversational AI and workflow automation.

Pricing: eesel AI has transparent subscription plans with no per-resolution fees, so your costs are always predictable.

• The Team plan is $239/month (billed annually) and gives you up to 1,000 interactions a month, 3 bots, and the ability to train on docs and websites.

• The Business plan is $639/month (billed annually) and bumps that up to 3,000 interactions, unlimited bots, and adds training on past tickets and advanced AI actions.

• A Custom plan is available for larger needs with unlimited interactions and more advanced integrations.

2. Helpdesk-native AI

These are the AI features built directly into big helpdesk platforms like Zendesk or Intercom. They give you integrated chatbots and agent-assist tools that live right inside the helpdesk you already use.

Why it's on the list: For teams who are perfectly happy inside their helpdesk's world and only need some basic AI features, this is often the path of least resistance.

What I liked: The integration is obviously seamless, and the interface is already familiar to your agents, so there’s not much of a learning curve.

What to consider: You're completely locked into that one vendor's vision for AI. These tools often have a hard time learning from knowledge that lives outside the helpdesk itself (like your internal Confluence pages or scattered Google Docs), which leads to pretty generic answers. You also get very little control over the AI's behavior, actions, or when it should escalate a ticket to a human.

Pricing: These are usually pricey add-ons to your existing helpdesk plan, and many charge you per resolution, which can lead to scary, unpredictable bills.

• Zendesk AI: It’s included in their Suite plans (starting at $55/agent/mo), but the base AI is pretty limited. More advanced features cost extra. They also have a pay-as-you-go option at $2 per automated resolution, which adds up fast.

• Intercom Fin: You can get the Fin AI Agent for $0.99 per resolution, with a 50-resolution monthly minimum. Even if you're already paying for an Intercom plan, you still have to pay the $0.99 on top of your seat price. This model literally penalizes you for being successful.

3. A DIY stack

This is the "roll your own" approach. It involves stitching together open-source frameworks like LangChain, a vector database like Pinecone, and a large language model (LLM) through an API to build an AI agent from scratch.

Why it's on the list: This is the ultimate power-user option. It gives you maximum flexibility and is what a highly technical team with a big budget might do if they want total control.

What I liked: You have complete control over every single piece of your stack. No vendor lock-in.

What to consider: The development and ongoing maintenance effort is enormous. This isn't just a project; it’s a full-time job for a dedicated team of expensive AI engineers. You have to build everything yourself: the user interface, the reporting dashboards, the testing environment, and all the integrations.

As one person on Reddit wisely put it when talking about this kind of setup, 'it's a trap!'

Reddit

Pricing: While some of the core frameworks are open-source, this route is far from free. You'll have hefty bills for LLM API calls, cloud hosting, vector database usage, and, of course, the salaries of the engineering team needed to build and maintain it. Even LangChain's own platform, LangSmith, has a team plan starting at $39 per seat/month plus usage fees, which shows you that even the "free" tools have platform costs.

4. Cloudera

Cloudera is an enterprise data platform that, much like Databricks, grew out of the Hadoop ecosystem. It's known for its strong security, governance, and its ability to run on-premise, which is a big deal for some companies.

Why it's on the list: It's one of the most direct Databricks alternatives from the big data world. It also serves as a great reminder that if your main goal is an AI agent, a full-blown data platform is often massive overkill.

What I liked: It’s a solid choice for highly regulated industries like finance or healthcare that have strict data control requirements and need to keep everything on-premise.

What to consider: It's incredibly complex and expensive to set up and manage. The whole platform is designed for large-scale batch data processing, not the real-time, conversational back-and-forth that a modern support agent needs.

Pricing: Cloudera's pricing is built for large enterprises and involves a long sales cycle. Its cloud services are priced with a "Cloudera Compute Unit (CCU)" that makes forecasting costs tricky. For example, their AI service is listed at $0.20/CCU per hour, but that doesn't even include the underlying cloud infrastructure costs. The on-premise solutions are "Contact Sales," which you know means a big, long-term contract.

5. Cloud data warehouse AI

This approach means using the machine learning features that are being baked into major cloud data warehouses. These tools let you use SQL commands you already know to build and deploy ML models directly on data you have stored in your warehouse.

Why it's on the list: For companies that are already deep in a single cloud provider's ecosystem (like AWS or GCP) and have all their data in one place, this can feel like an easy win.

What I liked: It integrates tightly with your existing cloud data and services, which can simplify parts of the data pipeline.

What to consider: These tools are built for structured, tabular data, think neat rows and columns. It's incredibly difficult to train models on the rich, messy, unstructured knowledge found in help articles, past support tickets, or internal wikis. This is a huge limitation that often leads to generic, unhelpful AI responses that can't solve real customer problems.

Pricing: It’s the classic pay-as-you-go cloud model. While that sounds flexible, the costs can be a nightmare to track and predict. You get billed separately for data storage, query compute, model training, and API calls, making it a headache to budget for.

This video breaks down some Databricks alternatives that can fit different needs and preferences.

How to choose the right Databricks alternative for you

Feeling a bit lost? Don't worry. Picking the right path gets a lot easier when you ask yourself a few simple questions.

• What problem are you really solving? Are you trying to crack a big data analytics puzzle or fix an operational workflow? If you want to automate ticket responses, a tool built for automation is almost always a better fit than a data platform.

• Be honest about your team's skills. Do you have a crew of data engineers and ML scientists ready to manage a complex platform? Or do you need a no-code, self-serve solution that your support or IT manager can own?

• Where do your answers live? Make a list of all the places the information needed to solve customer problems is hiding. The best tool will be one that can connect to all of them, not just a single, clean database.

• Demand a real-world test. Don't fall for a polished demo that uses perfect data. The best way to evaluate an AI agent is to test it on your own messy, historical data. Look for tools that offer a simulation mode or a free trial that lets you connect your actual knowledge sources.

The verdict on Databricks alternatives: Do you really need a big data platform?

While platforms like Databricks and Cloudera are incredible feats of engineering for data science at scale, they are often the wrong tool for building AI agents for business teams. Using them for support automation is like hiring a theoretical physicist to fix a leaky faucet. They might figure it out eventually, but it’s going to be slow, expensive, and way more complicated than it needs to be.

The trend in AI is shifting toward more accessible, self-serve platforms that solve a specific business problem and do it exceptionally well. For automating support and unlocking internal knowledge, the focus should be on speed, ease of use, and connecting with the tools your team already uses every single day.

Get started with one of the best Databricks alternatives in minutes

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