I used Neo4j AuraDB for building a graph in which I listed properties and the properties of these properties, such as the bedrooms, bathrooms, and whether it is a new or old home. I also made nodes for people who own these properties and used this information.

Neo4j AuraDB helped us in building the recommendation system for our project in which we have nodes of people and nodes of properties and use easy links between those properties and people, enabling us to recommend specific properties to specific people dependent on their preferences.

We set the recommendation system as if a user likes properties with two bedrooms and one bathroom. We make a Cypher query that filters properties he liked with two bedrooms and one bathroom. Then we look for another recommendation for him such as properties with three bedrooms or one bathroom or three bedrooms and three bathrooms.

In this project, approximately 1,000 users use it daily now for recommendations, in which they enter the system and we generate a Cypher query for them and display properties from the project depending on this.

Building the graph easily is the best thing that Neo4j AuraDB offers. Also, navigating the graph and navigating from one node to another was very helpful. The GUI of Neo4j AuraDB and how I can look at the schema of the graph was a very helpful feature.

In the interface of Neo4j AuraDB, I can watch the schema of my graph, in which I can choose some nodes and the GUI draws these nodes, so I can see the graph with my eyes and do some edits to it or use it as it is.

The speed of recommendation really increased after we converted the first graph we had to a Neo4j AuraDB graph. We had the speed initially at about 5 to 10 seconds. Now we have the speed from 1 to 5 seconds. The response time of navigating Neo4j AuraDB graph reduced from 5 to 10 seconds in some cases to 1 to 5 seconds in most of the cases.

I have been using Neo4j AuraDB for two and a half years. The last time I used Neo4j AuraDB was three months ago, in which I built a graph using Neo4j nodes.

Neo4j AuraDB is very stable.

It is very scalable as we now handle about 1,000 users each day with our graph in Neo4j AuraDB, and it works very well.

They are very helpful.

I will rate the customer support a 10 as they helped us in most of the cases. I think that our calls take too long now as I was expecting this call to continue from 5 to 10 minutes, but now we have about 15 minutes.

We were using a solution that was built on my company. Neo4j AuraDB solution was built on a data structure in which we build a graph by a data structure graph and we use all things in this graph. This was built in my company locally, so it does not have all the required features and is different from Neo4j AuraDB.

We did not see any other solutions.

If you have a graph that you need to build, you can use Neo4j AuraDB directly and not navigate any other solution as this solution has all required things. I rate this product a 9 overall.

My main use case for Neo4j AuraDB is solving problems with the documentation adhering to what we have on the chatbot for problem solving. These documentations are of Microsoft Surface Laptop, and there are multiple problems over there, but they are all interconnected. This interconnection of documents or rather sites of problem can be done in a more sufficient way. In the conventional RAG way, it was problematic because there was no connection between two problems. However, with Neo4j AuraDB, a problem is connected. There are hyperlinks in one documentation which leads to another documentation. This is where Neo4j AuraDB, which has the graph capabilities to connect two segments or two documentations, was very beneficial.

Neo4j AuraDB helped me tackle those interconnected documentation challenges by making things much faster. Each of the nodes I define in the graph is one documentation. The connections, these relationships were simpler when it comes to graph architecture because, for example, one problem would be blue screen. The solution to it would be restarting your laptop or if your device hardware is damaged. All these two connections were given to two other nodes. We have a map out of it. The number of nodes decreased at a very huge level when it comes to the conventional way. With Neo4j AuraDB, documentation and adding things were very easy because the UI is very exploratively helpful.

Regarding my use case with Neo4j AuraDB, something I want to add is that if we go the conventional way, there were a lot of conventions because the first problem which the customer comes in and adds to the chatbot could be anything. Then the next problem would be L2 level. Then, anything coming in interaction could be L3 level. In the conventional way, it was going very redundant. There was no connection to it. However, in Neo4j AuraDB, it was a graph, so the number of documentation and the number of storage was very much decreased at a very huge level. The connections were very logical. Backtracking of things was very much helpful because we were able to see that in level three, when the customer went for an answer like restarting your laptop, then why they came down to this ladder of graph or nodes from L2 and then L1. This was helping us to backtrack the solution and maybe debug things. This was where a few of the challenges we faced from the conventional and helped us to push our things to Neo4j AuraDB.

Neo4j AuraDB has positively impacted my organization with specific outcomes or improvements. I may not be able to answer on the organizational level because I am on the development side of it, but a specific outcome or improvement that I have noticed while using Neo4j AuraDB is that if you find your data on which you are working is interconnected, those chunks, which we call chunks in RAG, are a set of data and a set of NLP lines which we generally retrieve or add into the vector embedding. If we find things that are interconnected and those interconnections are meaningful, then Neo4j or graph architecture is something that is very much beneficial. Neo4j would be a pioneer of this technology online, giving us a free tier to explore and enjoy and understand things at a much higher level. On the organizational level, if this would not have been in place, which I am speaking for Neo4j AuraDB, then things would have prolonged in more efforts plus line level. We may have spent more time on development things, which are very much futile and not fruitful to us. However, Neo4j helped us decrease our development time and also the storage level, which was very much helpful.

In sharing metrics around the reduction in development time or storage with Neo4j AuraDB, mentioning exact numbers would be unfair to my current organization. For development time, the development time is reduced by around forty to fifty percent because in the conventional way, development involved getting up with a system that understood this type of data where connections also meant something. Neo4j was a platform itself where we just feed in data. That is another feature to it, that we can just feed in data, and it tries to understand itself with the use of AI. That is the feature which helped us to decrease at this magnitude of time. In terms of storage, it helped us around twenty percent because it was removing redundant places. In the conventional way, we had to duplicate a few things for more functioning, but as the architecture changed, we went to something faster. Caching of non-duplicate was something we were doing in the conventional way, but with Neo4j, we now have a very good amount of search time. Development time decreased, as well as on the customer front, the query time also decreased.

The best features Neo4j AuraDB offers, the best feature which stands out is the exploration and the UI which comes up. We can drag and drop, we can see what things are there, what nodes are connected to whom in a visual way. It is more understandable to people who are from the business side and non-technical side, who are not able to understand the query itself. We were able to explain things to them with the UI itself. This is one of the features. The second thing is the visualization of the graph, which is really helpful to make others understand what was happening and to backtrack things. The generative AI feature to create the Cypher query languages is valuable because CypherQL is not something which everybody is well-versed with. With the generative AI part to it, it was really helpful that we explain what we want, and it creates an easier level of query languages which goes there. Those features are valuable in day-to-day life because whenever we have a review with the tech team or our client, those graphs, when things get to backtracking of the solution, are very much helpful with the visualization, understanding them, understanding the problem, and how things went in. Debugging was very helpful.

In day-to-day life, the testing team found the generative AI query formation very helpful because then anybody can become an expert on the Cypher query language, which was embedded into the AuraDB interface. That was very helpful, and nobody has to go anywhere else to query or give intricate security details there to generate a query onto other platforms rather than in here itself. We can give what we want, and this used to generate our query languages. We were very much so secured that the data resides there and only we are asking questions.

Regarding the features of Neo4j AuraDB, another point I would like to add is that they offer a free tier. Even before we start off with client billing and everything, we were able to explore those things. We were able to understand that the documentation is really helpful in these places. They also provide us with some sample data so that we can play around with the graph architecture and understand how things are working around in the free tier. That is very much important. These were a few features that we like. The agent is a very new feature I am trying to explore, but not on this use case. However, the agent is something which I can see can be helpful for more business type of use cases where we add what we want.

If I say so, how Neo4j AuraDB can be improved, at a very minute level, I can say that the graphs, if I want things to be in three dimensions. Currently we get them in two dimensions. When we scale this up, the maps become complicated, and in two dimensions things can be complex in visualization purposes. When we put in filters, the connections also vanish sometimes. The visualization side is something that could help. Additionally, if there is a voice search capability, which we can implement, that would add more functionality. With so much AI involved, AI could be helpful if we put in our data and enable it to give insights from the networks already developed in the graph. Some insights could be shown to our client, indicating that their data already contains meaningful insights, which could serve an analytical purpose. Voice search on the graph would be easily understandable and provide faster outputs to the customer.

I have been using Neo4j AuraDB for the past two years, but on a very minimal level, primarily for research purposes. It has been here and there, but I have been in touch with AuraDB.

What advice would I give to others looking into using Neo4j AuraDB? First, please know how your data reacts, how your data is interconnected, and how your data moves around the other data in itself. What is the subset and the superset of each other? How can you manage your data? If you have this level of information, then this is the go-to-market solution for you. Second, if you are currently using conventional databases or other systems, you need to understand how graphs work. You have to work to comprehend this concept, and then come to this solution; it would be very helpful because this solution has some documentation and a learning curve. Do your homework before coming to this stage of work.

If you know your data and the connection between your data has meaning and you see that those meanings can be helpful in understanding your data, this is a perfect solution.

My main use case for Neo4j AuraDB is building recommendation and dependency mapping systems. For example, one of the projects involves using the model to establish relationships between users and products to generate personalized recommendations in real time. In another project, I am mapping dependencies between microservices and APIs to identify potential bottlenecks or failure points. Neo4j AuraDB's graph structures and queries make it easier to manage complex relationships compared to other databases.

I have been using Neo4j AuraDB for about two years. I first started with Neo4j as a community edition for local prototyping, then moved to Neo4j AuraDB for production workloads because of the fully managed setup and seamless integration with AWS. Over that time, I have used it mainly for building recommendation systems and dependency mapping features, where graph queries significantly improved performance compared to other relational models.

Neo4j AuraDB has had a positive impact on our organization by significantly reducing our infrastructure and maintenance overhead, allowing our team to focus more on product deployment and data modeling rather than managing servers or scaling issues. From a business perspective, it has improved the performance of our recommendation and dependency mapping systems, which directly enhances user experience and reliability. The query response time has decreased, and overall, Neo4j AuraDB has increased our deployment speed, improved system stability, and lowered operational costs, making our entire database stack more efficient and scalable.

In my opinion, the best features of Neo4j AuraDB are its fully managed environment, the Cypher query language, and the visualization tools. The managed setup saves considerable DevOps time since we do not have to worry about clustering, backups, and patching. Cypher is very intuitive for working with complex relationships, making it easy to express graph traversals that could be complicated in SQL. The visualization tools are also excellent for debugging and understanding data relationships.

Additionally, Neo4j AuraDB's seamless integration with AWS and its ability to scale automatically make it ideal for production workloads without manual infrastructure management. The fully managed environment is the feature that has made the biggest difference for our team. Before using Neo4j AuraDB, we spent considerable time maintaining clustering and backups and managing updates manually. With Neo4j AuraDB, all of that is automated, allowing us to focus entirely on the deployment and optimization of our graph instead of worrying about infrastructure. It has also improved reliability; we no longer have to deal with downtime for patching or scaling issues. The managed setup ensures consistent performance during automatic traffic spikes, especially during recommendation queries.

I would also mention that the managed environment gives us peace of mind with automatic backups and security updates, ensuring our data is always protected and compliant without extra effort from our side. The performance consistency across environments from development to production has also been reliable. Overall, it has allowed us to focus on delivering new features instead of maintaining infrastructure.

Neo4j AuraDB is a very strong platform, but there are a few areas for improvement. The pricing model can be somewhat unpredictable as your database grows, since costs scale with node and relationship counts. More transparency or cost estimation tools would be helpful. Additionally, importing large datasets should be smoother; bulk data sometimes requires extra tuning or batching to avoid timeouts. The documentation is good overall, but it would benefit from including more real-world production examples and best practices for optimized Cypher queries and scaling patterns. Lastly, on the lower-tier plans, the cold start time can be frustrating during production, so improving the setup speed would enhance the developer experience.

One improvement that would make daily work easier is having better monitoring and performance analytics directly in the Aura dashboard. Currently, we rely on external tools and custom scripting to track query performance and resource usage. Having native, real-time insights would help us quickly identify slow queries or inefficient patterns. Additionally, smoother integration with our CI/CD pipelines would be beneficial, such as an easy way to manage schema migrations or automatically seed data, which would make our development and testing process more efficient. These small enhancements could streamline our workflow.

I have been working in my current field for almost four years. I work specifically in backend development. During that time, I have focused on scaling backend systems, native cloud-native programming, and integrating and managing different databases. I am experienced in designing APIs and microservices on different technology stacks.

My advice for others considering Neo4j is to clearly identify where their data model truly benefits from graph relationships. If your application involves complex connections such as recommendations, dependencies, or network analysis, Neo4j AuraDB is an excellent choice. Take advantage of the managed services to save DevOps time while also monitoring your node and relationship counts to keep cost predictions in check. I also recommend investing time in learning Cypher properly; it is intuitive once you become accustomed to it and makes querying relationships very powerful. Finally, use the visualization tools early in deployment; they are great for understanding your data structure and debugging relationships. I would rate this product a nine out of ten.

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 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.