Neo4j AuraDB Reviews

Neo4j Aura (pay-as-you-go) is primarily used for research and production-grade graph analytics, including knowledge graph construction for complex relational data and graph-based reasoning and traversal for AI-driven analytics.

Neo4j Aura has been valuable in terms of reducing infrastructure management overhead, providing a managed, scalable Neo4j environment, and enabling faster iteration on graph-based research and applications. However, this value was significantly undermined by the recent incident where the service was suspended without prior notice, leading to several hours of data inaccessibility. For mission-critical workloads, service continuity and predictable lifecycle management are as important as technical performance.

Neo4j Aura (pay-as-you-go) could be significantly improved in the areas of service lifecycle management, communication, and migration handling, based on my recent experience. Specifically:

- Advance notification and transparency for service suspension: My Aura project was suspended without prior notice, immediately making the database inaccessible. For a managed database service, users should receive clear advance warnings before any suspension that affects data availability.

- Clear handling of AWS Marketplace legacy subscriptions: The transition from a legacy AWS Marketplace listing to a new listing was not communicated clearly. When I followed the instruction to 'update' or re-subscribe, a new organization and project were created automatically, while my existing project remained suspended with no visible option to re-link the active subscription. This created confusion and operational risk.

- Explicit migration guidance in the UI and subscription flow: It was not clear that migration to a new project was mandatory and irreversible. This information was only provided after contacting support. Such constraints should be clearly surfaced before a user takes action.

- Reasonable and safe migration windows: After requesting emergency assistance, the suspended project was temporarily unsuspended for only one hour to allow snapshot and migration. This timeframe is not sufficient for safe migration of a non-trivial graph database and exposes users to unnecessary data loss risk.

Key areas for improvement include service governance and communication, especially around subscription transitions and deprecations. There should be clearer visibility and warnings for legacy subscription lifecycle changes. Safer and more flexible migration windows are needed when forced migration is required. Users also need explicit UI guidance for re-linking subscriptions or understanding when re-linking is not possible. From a user perspective, process reliability is as critical as technical features.

I have used Neo4j Aura (pay-as-you-go) for 2 years.

The pay-as-you-go pricing model is generally reasonable and suitable for scalable workloads. However, users should be aware that pricing transparency alone is not sufficient. Subscription lifecycle changes, such as legacy marketplace transitions, can have a significant operational impact. Clear advance communication around such changes is essential to make pricing truly predictable.

Neo4j Aura is a technically strong and capable managed graph database, and it has been valuable for research and production use. However, this incident revealed a serious gap between technical capability and operational governance. A production database service was suspended without prior notice, and a platform-side marketplace transition effectively forced migration. The responsibility and risk of emergency migration were placed almost entirely on the user, and only a one-hour window was provided to safeguard existing data. I am not opposed to migration or platform evolution. What I strongly advise is that such changes be handled with clear advance communication, explicit explanations of irreversible actions, and migration timelines that are realistic and safe.

Think of Neo4j AuraDB as a special type of database - it's a graph database. Graph databases can be used for situations where you want to do relationship-centric modeling. If you want to identify how data points are related to each other, that's where AuraDB does really well.

Specifically, in terms of RAG and generative AI use cases, where you want to find out how close data points are to each other, AuraDB does really well. It's fast because the data is essentially a graph database with points linked to each other.

It feels like a perfect solution if your use cases involve identifying or working with relationships within the data.

Think of AuraDB as a database. For example, imagine you have textual data in the form of documents, and you want to feed that data into an existing LLM model to gain extra context. That's where you would use AuraDB.

In this use case, you would convert your textual corpus into a graph database and store it in AuraDB. This can then be fed into an existing or newly created LLM model, which will provide better insights. You can then perform analysis on your data, and your LLMs can answer questions and provide better context based on the additional data you've provided.

This is essentially RAG workflow, but it's really useful for storing extra data or storing your data efficiently.

AuraDB effectively manages complex data relationships. If there is an inherent need within your data or your use case to identify how the data is related to each other and how the individual points are related to each other, then the graph structure of the database itself is the biggest feature AuraDB provides.

It also has a query language called Cypher, which is used to query within the database, create the database, and get your use cases out.

So the key features or the key pointers are the Cypher query language, its speed, and the inherent graph structure of the database.

The most beneficial things in terms of AuraDB are its speed, its good pricing, the multi-cloud availability, and its availability across GCP, Azure, and Amazon. It's great for use cases where you want to do relationship-centric modeling. So, those are the most valuable things in AuraDB.

I also work with real-time data in the AuraDB solution. A lot of this, especially the scalability and how efficient these conversations are, depends on what model or writing strategy you go for. But you can definitely work with real-time data.

For my personal projects, I use AI. What we're seeing right now can work very well with RAGs in AuraDB or any graph database. So we take extra data, put it in a graph database—AuraDB in this case—and feed it to an existing large language or a small language model. With that, an AI model can gain some extra understanding of your data, which is stored in a graph database.

It can give out very contextual and specific answers based on the extra data users provide in the form of a graph database, which is stored in AuraDB. So the use cases are, from what I mean, the terminology is graph RAG, but that's where I see a lot of potential use cases for a lot of data.

The outcome accuracy with the AI-enhanced graph is good for my use cases. However, it may be difficult to assign a numerical accuracy metric to Neo4j. But for example, with text summarization, you cannot put a number to the accuracy. However, just seeing the answers and the improvements in the model, it's definitely helpful in improving the results. It's essentially giving an extra context to your model. So, I definitely see the advantages of using AuraDB.

I've been using it for a few months now, and everything has been fairly positive. Maybe in terms of documentation, they can improve a little bit. 

Neo4j AuraDB already has a good set of documentation, and the initial setup is easy, but it could be made a bit easier. For me, things are going very well, actually.  

In terms of AuraDB, the conversations have always been around scalability. So that's where people are majorly concerned: whether it can be used for truly production-grade projects. But Neo4j AuraDB consistently comes up with updates. But potentially, that could be one area where maybe I can see some more improvements.

I have been working with AuraDB for around six months now. It's mostly been an experimental thing where I try out projects and find use cases to see its maximum potential.

I do find it stable. There are some competitors out there, but in terms of the learning curve, it's very easy. The initial setup is very easy. So, it's definitely a stable solution.

Five years back, scalability was considered a bit of an issue with respect to AuraDB. But I think with the recent updates, they've handled it very well.

Currently, I'm using AuraDB just for experimental purposes, so from what I've read and what I've seen about AuraDB, it can handle quite a vast amount of data.

There may be some performance issues when your database or your data is very large, but then again, it's completely dependent on what pricing strategy you go for.

From my side, right now, it has been mostly experimental and working on personal projects. So, again, it's dependent on what project I've seen. But it can also be used for large-scale projects. That's where I see conversations where people are a little bit concerned, wherein very large use cases, where billions of data points are there, whether it would be as efficient. It would work, but maybe it might take a hit in terms of speed, even the efficiency of it.

As of now, I have not reached out to them as such because everything has been fairly clear to me. But I'm fairly sure that the technical support is good.

I have not worked with other graph databases, but I am aware of the competitors. There is TigerGraph database, and I think Amazon Neptune, and one from Azure as well. I've not really worked them out, so I use AuraDB.

I found the initial setup fairly straightforward. From what I felt, the learning curve was a bit simpler. AuraDB had their courses out there, and some of them are out there for free, so you can just quickly learn them. And I just felt that the initial setup was much simpler compared to others, and I was able to catch on to it. 

The deployment is just a standard way—it's like any other database. There's no difference in the way AuraDB does things. 

AuraDB can be hosted or is available in the major cloud services. So, the deployment procedure remains pretty standard compared to the other existing databases out there. There's no difference as such.

We use the public cloud, so that's where the deployment is being worked out.

The deployment time depends, again, on the project and the circumstances. But, the initial learning, it might take two to three months to pick it up. And working on a project, again, maybe another three, four months. And in terms of deployment, another one, two months to it. But, again, it's purely dependent on the project and the circumstances.

From what I have seen, there's no real maintenance or anything extra to it. It's just that since it's a new technology, or rather, not many people might be aware of it, it's just the awareness needs to be there, but there's no additional maintenance as such.

I have done the deployment myself. There has been no real assistance, at least until now. But I think their community support is fairly nice, so that's something to look out for as well.

The product offers three pricing strategies. 

One is the free version of AuraDB, which can be used for small and experimental projects, which is what I'm doing. 

Then there is AuraDB Professional, which is $65 a month. 

And then there is AuraDB Enterprise, which is for the production of large-scale use cases, and that's where they give more security and support. 

So those are the pricing strategies. 

I use the free version as well.

I would definitely recommend AuraDB to others. Give it a shot to see whether it fits your use case, and I would definitely recommend it.

So, for my current usage, I would give AuraDB a nine out of ten. I think it's fairly good. Again, the small improvements might be in terms of the scalability and a little bit more documentation, but a fairly solid nine out of ten.

I was getting data from Hacker News to store it on Neo4j. I was running a cron job using Neo4j to scrape topics and comments from Hacker News every two hours. I ran that for at least a year and a half, but stopped recently. I wanted to see how stable and useful it was.

First, I liked that it was free. I also liked that you can run multiple languages with it. I can connect it to a Spring Boot app, a Python app, or a Go app. If I'm doing network correlation, graph or node analysis, I can easily connect my Google Colab to my Neo4j AuraDB account. Those are the things I like about it.

I like the idea of graphs and nodes and the possibilities Neo4j AuraDB offers.

I've experienced it crashing a few times, so stability could be better.

For things to improve, I think the GUI on the cloud needs improvement. If it's more intuitive, someone new to it can spend less time on tutorials and pick it up faster. The truth is, if your product is good, you spend less time on advertising. I take my inspiration from Telegram. Their product was so good they didn't need to spend much on advertising. It just grew on its own.

I have been using it for the last one and a half years. 

I've experienced it crashing a few times, but it's common with other products like Chrome or Firefox. So I understand that sometimes products require restarts. But outside of that, it's a very good tool.

It was for personal use. I got interested in the graph stack, which uses the Neo4j database as its backend. So it was more personal than for a company.

There were multiple reasons that I decided to go with Neo4j AuraDB rather than something else. Number one, it was cheap. 

Then, being able to run it on the cloud for a single node was appealing. The tools also made me choose Neo4j. 

There's a very good community around it, and the learning resources are easy to follow. They have useful blogs, and there's a product manager with a helpful YouTube channel. The community makes it very easy to use. That's one of the big selling points for me.

The initial setup is neither easy nor difficult. I've created both the on-premise app (downloaded directly from the website) and the AuraDB cloud version. The cloud version is simpler. I needed a short video clip on the website to install the on-premise Neo4j on my system.

The Cypher tutorial that comes with the on-premise Neo4j installation is thorough. 

I used the free tier.

If you want to use it for the first time, I'd advise you to go for the cloud version, AuraDB. 

If you want to use it as a company, at least using that, you can test if it fits your use case. As developers, we're trying to solve problems. The best way, the easiest way, the less painful way, and also the best way to maintain it is important. You might leave the company, and someone else needs to take over, and you want to spend less time setting them up so they succeed faster. I think they've done well with the AuraDB cloud section.

With the right tutorial, it is easy for a beginner to learn to use this tool for the first time. There are a lot of good tutorials available. I got into it from a Medium blog, and I've also tried creating tutorials myself. 

Overall, with my experience, I would rate it a nine out of ten, from one being bad to ten being the best.

I'm a research scholar, and I've learned cipher language for my research work. I've been using Neo4j AuraDB for that cipher language.

I'm also working on Python as a front-end language. Integrating the front-end language with Neo4j AuraDB is a very easy process. We can directly import packages and see the visualization in the Python interface. Neo4j AuraDB provides interfaces for languages other than Python, such as Java.

I have been using Neo4j AuraDB for three to four years.

We don't have to install Neo4j AuraDB. The solution provides a server, and we can use an online server to create the database. You may not have to install it on the local system or server. You can also have direct online access.

I am using an open-source version of Neo4j AuraDB.

The solution's documentation has been well written. Sometimes, I look at different sources on the internet when I need help. Everything is available on the Neo4j website.

I attended the NODES workshop organized by the Neo4j community, where people from different backgrounds gave a very good lecture series. The lectures focused on which particular area and domain we can use Neo4j AuraDB as a database.

I would recommend the solution to other users. I have already recommended it to my colleagues working on different types of graphs. Some of my colleagues have started using Neo4j AuraDB and built a big graph. One of them has come up with a very nice social network graph.

I would definitely recommend Neo4j AuraDB to someone looking for an easy-to-use graphical database with a good user interface. The solution's visualization is very good, and you can use it to visualize property graphs and state-of-the-art knowledge graphs. Neo4j AuraDB has plenty of options in its repository.

Overall, I rate the solution eight and a half out of ten.

I worked on a project focused on the quality of public menus, using Neo4j AuraDB to connect and create relationships between food items. This allowed us to visualize data in interesting ways and identify communities. A key feature was using the Green Dot to link unstructured data, such as investment information, with structured data from tables and PDFs. The AuraDB documentation was also helpful in making these connections and providing valuable insights.

The most valuable features of Neo4j AuraDB include its flexible data model and broad language support. It’s great that it offers a dedicated query language, which delivers excellent performance and high availability. Additionally, it’s hosted on AWS Cloud, which ensures reliability. The platform also allows for the integration of videos and other media.

Some features can help if they can visualize graphs better.

They have Neo4j Bloom, which is great for visualization. If you can visualize the graph directly within Neo4j AuraDB, that would also work well.

I don't have any problems about the performance

Scalability is very good.

I’ve used RDP before but prefer to start my analysis with Python and sometimes Neo4j Bloom. The most important feature is that Neo4j is a powerful graph database, enabling faster and more efficient analysis.

It's very simple to create a cloud account, and it takes a few minutes to deploy.

ROI is nice because you can have an incredible return.

It has fair pricing.

The community is very nice, and you can find many things.

Neo4j AuraDB is a powerful graph database that enables us to accomplish impressive tasks. Specifically, as a cloud-based service, it eliminates the need for a high-performance computer to use it.

Sometimes, I collaborate with Smiths when working with large amounts of information. To streamline the process, I often use a chatbot agent plugin, which allows me to respond quickly in real-time, improving the overall user experience.

I've been using this chatbot agent for investment-related projects, but my first project focused on maintenance and public school menus. This initial project is more important because it involves public schools, children, and food insecurity. Conducting this analysis and developing the AI project with Neo4j could lead to meaningful results in the future. We can improve the accuracy of the model by providing context. I can't supply the necessary context if I use traditional methods, like vector regression. However, by creating a knowledge graph in Neo4j AuraDB, I can offer this context to the model, leading to better accuracy and performance.

It's very easy to maintain it.

It's an incredible tool that is quick to use and delivers impressive results. Many people should give Neo4j AuraDB a try. It's a very effective graph database.

It is a very difficult job to show the graph schema and understand a problem using the graph technique.Neo4j AuraDB is a very convenient tool for our team because it allows access to anyone invited to a project. Others can easily access the graph data if I invite people and train the schema.

The solution's most valuable features stem from its easy connection to other modules and integrations. Understanding graphs can be a very complex concept, as there are many other combination modules in it, like LangChain, the LLM module, and the data processing module. I think there are more than four modules that heavily rely on GraphRAG's implementation, but AuraDB's role is very good because everything is very convenient for our team in the cloud and graph database systems.

During the product's setup process, disconnections in the tool's network caused some problems, making the solution's stability an area of concern that requires improvements.

I have been using Neo4j AuraDB for a year. I am a customer of the tool.

Stability-wise, I rate the solution a seven out of ten.

I had to use the cloud system of the tool because our company had faced scalability issues. I used the database for small use cases involving graphs. The tool has issues stemming from the disconnection part that keeps occurring, which led to GraphRAG implementation issues.

Scalability-wise, I rate the solution a four out of ten.

I never had to contact the solution's technical support team.

During the product's initial setup phase, there were some issues due to disconnections in the tool's network.

The tool's enterprise edition is very expensive.

The tool is easy to use.

The tool offers very easy and convenient modules for our teams.

The tool does help handle data security and privacy concerns in our use cases. Our data is a very important factor for us in terms of privacy and security. I think the tool's modules are good. The product offers a text file for AuraDB's information. You should be careful with the text file because it includes information like URLs, passwords, and usernames.

I strongly recommend that people use the tool. I suggest others not be afraid of GraphRAG and databases on the cloud system. The tool is a very convenient source for a beginner of the graph database.

I did use the tool's AI capabilities. There are many important processes in the GraphRAG procedure, and among them, the graph construction was very important because, as per RAG, everything depends on the graph schema. We deal with the design for the graph, and then the tool can easily integrate into their traditional database. The cloud is very easy to access and manage.

I rate the tool a seven out of ten.