Weaviate pricing: A 2025 guide to costs and considerations

If you’re building any kind of modern AI app, especially one with semantic search or a RAG-powered chatbot, you’ve probably realized you need a vector database. Weaviate is one of the big names in this space, and for good reason, it’s a powerful open-source tool. But figuring out what it’s actually going to cost you is another story entirely.

a developer trying to estimate costs for a project with 10 million records, and the comment section was a mix of guesswork and confusion.

Reddit

It’s a common headache. I was just scrolling through Reddit the other day and saw a developer trying to estimate costs for a project with 10 million records, and the comment section was a mix of guesswork and confusion. The pricing models are tangled up in jargon like "vector dimensions" and "AIUs," which makes trying to create a simple budget feel impossible.

So, let’s untangle this mess. This guide will break down Weaviate’s pricing models, from Serverless and Enterprise to the often-underestimated costs of hosting it yourself. We’ll do it in plain English so you can make a smart decision without getting hit with a surprise bill later.

What is Weaviate?

At its heart, Weaviate is an open-source vector database. You can think of it as a special kind of database built for the AI era. Instead of storing data in neat rows and columns like a traditional spreadsheet, it organizes information based on its meaning. It does this by turning your data, whether it’s text, images, or audio clips, into a list of numbers called a vector embedding.

This structure is what lets AI do some pretty cool things. It enables an application to find the most relevant information based on context, not just simple keyword matching. This is the engine that runs smart search systems, chatbots that can actually cite their sources, and recommendation engines that feel like they read their mind.

Weaviate is loaded with features, like being able to handle different types of data (not just text) and a "hybrid search" that mixes old-school keyword filtering with new-school vector search. You can grab the open-source version and run it on your own servers, or you can use one of Weaviate’s managed cloud services, which is where things get interesting with the pricing.

Understanding Weaviate’s pricing models

When it comes to paying for Weaviate, you’ve got three main options. Each one hits your budget and your team’s to-do list in a completely different way.

1. Weaviate Cloud (Serverless): This is the pay-as-you-go route. You’re billed for what you use, which makes it a popular choice for getting a project off the ground or for apps with fluctuating traffic.

2. Weaviate Enterprise Cloud: This option gives you a dedicated, fully-managed setup for big, important applications that need top-tier performance and can’t afford to go down.

3. Self-Hosted (Open Source): This is the DIY path. You download the software for free and run it on your own infrastructure, whether that’s a cloud server or a machine in your office.

Let’s get into the details of each, starting with the one most developers try first: the Serverless plan.

Serverless cloud pricing breakdown

The Serverless model is built for flexibility. The idea is that it grows with you, so you don’t have to guess how many servers you need right at the start. The catch? The pricing is based on a few metrics that can be a little confusing.

The biggest factor in your bill is the number of vector dimensions you store, calculated per million, per month. To give you a baseline, Weaviate’s "Standard" support tier costs $0.095 per 1 million vector dimensions stored.

Okay, so what on earth is a "dimension"? It’s just the size of your vector embedding. Different AI models create vectors of different sizes. For instance, OpenAI’s popular "text-embedding-ada-002" model creates vectors that are 1536 dimensions long. If you were to store 1 million of these vectors, your monthly bill would be based on 1.536 billion total dimensions.

On top of that, your final bill gets adjusted based on the support tier you pick.

A couple of other things can move the needle on your bill:

• Data Objects: Simply put, the more stuff you store, the more you pay.

• Storage Type: You can pick between "Performance" and "Compression" storage. Compression can save you a good chunk of money, but it might slow things down a tiny bit.

• High Availability: If your app absolutely cannot go down, you can turn on high availability. Just know that it will triple your costs.

The biggest challenge with this model is trying to predict your costs. Unless your data grows at a perfectly steady rate, forecasting your monthly bill is tough. You have to constantly keep an eye on how many objects you have, calculate the total dimensions, and guess at any future spikes in usage. For teams without a dedicated person to manage infrastructure, this can be a real pain.

Enterprise cloud pricing explained

For bigger companies running apps that are central to their business, Weaviate has an Enterprise Cloud plan. This gets you your own private, dedicated environment with guaranteed performance and better support.

Here, the pricing model changes completely. Forget pay-as-you-go. This is all about annual contracts and a metric they call AI Units (AIUs). You won’t find a price list on their website, you have to get on the phone with their sales team.

But, we can get a few hints from their cloud marketplace listings. The AWS Marketplace and Azure Marketplace show a starting contract of $10,000 for a 12-month period. On top of that commitment, there are overage fees, which AWS lists at "$0.285 per 1M vector dimensions". This plan also comes with cost-saving features, like moving older data to cheaper "Warm" or "Cold" storage.

The main issue here is the lack of transparency. The "Contact Sales" approach means you can’t quickly compare costs or figure out the total financial commitment without going through a whole sales cycle. That’s a huge hurdle and often feels like overkill for teams who just want to build a solid AI support agent without signing a five-figure check.

Hidden costs beyond the price tag

No matter which plan you’re looking at, the price on the website is just the beginning. The real cost of using Weaviate goes way beyond the monthly invoice, especially if you’re tempted by the "free" open-source version.

This video explains how to use storage tiers to optimize your vector database for search speed, accuracy, and costs.

Developer and infrastructure overhead

That Reddit comment about running Weaviate on a "$50 VPS" might sound great, but it’s a massive oversimplification. Sure, the server itself might be cheap, but the real cost is the dozens, if not hundreds, of engineering hours spent on setup, configuration, security updates, performance tuning, scaling, and daily maintenance. This isn’t a one-and-done job, it’s a continuous operational burden that pulls your developers away from building features for your customers.

Complexity of integration

A vector database is only one component. To build a working AI agent, you still need to set up data pipelines, run embedding models, write all the application logic, and build custom integrations to connect everything to your existing tools, like your Zendesk helpdesk or your Confluence knowledge base. What seems like a small side project can easily balloon into a multi-month engineering epic.

The learning curve

Using a tool like Weaviate well requires a pretty specific skillset. Your team needs to get up to speed on vector database theory, indexing strategies, and cloud infrastructure management. If you don’t have that expertise in-house, you risk building a system that’s slow, inefficient, or insecure, which ultimately slows down your launch and frustrates your users.

These hidden costs are exactly why a lot of teams end up skipping the DIY approach. They opt for fully-managed platforms that bundle the entire AI stack, hiding the complexity of individual components like vector databases.

A simpler alternative: eesel AI

Instead of getting lost in the weeds of managing a vector database, you can use an all-in-one platform like eesel AI to build and launch powerful AI support agents. It takes care of all the messy backend infrastructure, including vector storage, retrieval, and model management, so you never have to think about Weaviate pricing or configuring a server again.

Transparent and predictable pricing

With eesel AI, you can forget about confusing metrics like vector dimensions or AIUs. The pricing plans are straightforward and based on a simple, predictable number: monthly AI interactions. You know exactly what you’re paying for, and there are no per-resolution fees, so your costs won’t suddenly jump after a busy month. It makes budgeting refreshingly simple.

A screenshot showing the simple, transparent Weaviate pricing alternative offered by eesel AI, based on monthly interactions.

Go live in minutes, not months

eesel AI is designed to be completely self-serve. You can connect your helpdesk, like Zendesk or Intercom, and pull in knowledge from all your sources, like Google Docs or a public website, with just a few clicks. A project that would take months of development with a tool like Weaviate can literally be done in an afternoon.

Test with confidence

One of the biggest anxieties with a DIY setup is not knowing how well it will actually work. eesel AI has a simulation mode that solves this. You can safely test your AI agent on thousands of your past support tickets in a sandbox. This gives you real data on how many tickets it can resolve and how much it will save you before you turn it on for customers, taking all the guesswork out of the equation.

The eesel AI simulation mode, which helps businesses understand AI performance and costs, a key consideration when evaluating Weaviate pricing.

Focus on value, not vectors

Weaviate is a seriously powerful and flexible vector database, there’s no doubt about it. But its pricing can be complicated, difficult to forecast, and a potential budget trap. The Serverless model gives you flexibility but requires you to keep a close eye on costs, while the Enterprise model locks you into a large, opaque contract.

Even more importantly, the real cost of using a tool like Weaviate isn’t just the monthly bill. It’s the huge investment in developer time and infrastructure headaches. For most businesses, the goal isn’t to become vector database experts, it’s to solve customer problems as quickly and effectively as possible.

A fully-managed, end-to-end platform like eesel AI takes the infrastructure burden off your plate. It offers a faster, more predictable, and ultimately more cost-effective way to deploy powerful AI that actually helps your customers and your team.

Ready to launch an AI support agent without the infrastructure mess? Start your free eesel AI trial today and see how easy it can be.