Neo4j Aura (pay-as-you-go)

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.

Neo4j AuraDB is a cloud-based graph database. It’s mainly used for projects that must start small and scale up as required. The cloud interface is easy to use and requires no maintenance, making it ideal for development and client handover.

From my experience, I particularly like the professional version. Initially, developers often start with the free variant. Once the project grows, we switch to the professional version, which offers multiple databases, expanded memory, and better scalability. This allows us to handle more data and use cloud scaling features.

There’s room for improvement in Neo4j AuraDB, especially on the developer side. The learning curve can be steep, and the interface for developing and pushing code can be unnecessarily complex. It might be beneficial to simplify this process to help developers ramp up more quickly.

Working with graph databases like Neo4j can be more challenging than standard databases, particularly for juniors and those new to graph technology. Streamlining the development process could make it easier for new users to get up to speed. This would be particularly useful for teams with less experience in graph databases.

If I could add a feature to Neo4j AuraDB, I’d focus on improving the Bloom interface. It’s excellent for visualizing smaller datasets, but navigating through it becomes challenging as the data grows—say, past 100,000nodes. The interface works well for beginners but doesn’t scale effectively for more advanced users of large datasets. I want a UI that bridges the gap between the easy-to-use Bloom interface and more complex, text-based tools. This would help manage larger datasets more efficiently and improve performance.

I’ve been working with Neo4j since it first launched, and I've been using Neo4j AuraDB for around two years. AuraDB is relatively new, having been around since about 2021. It moved into the cloud, which made it easier to use. As a tech consultant, I use AuraDB forthe projects I’m working on.

For Neo4j AuraDB's stability, I would rate it around eight or nine. We've only had issues when using multiple heavy instances on the same setup, but we haven't faced significant problems with either the professional or enterprise versions. I haven't worked much with the enterprise scale, but I haven't heard any complaints from the teams using it.

As for technical support, I personally haven't contacted them, but my team has, and they were quite satisfied with the support they received.

When it comes to installation, setup, and deployment of Neo4j AuraDB, it's straightforward.

Since AuraDB is cloud-based, you don't have to deal with manual installation or server management. You download the desktop application, connect to it, and you're' ready.

I come from an open-source background and often use Docker
instances, but with AuraDB, the process is straightforward. Developers can start with a free instance that handles up to 200,000 or 400,000 data points, sufficient for smaller projects. Upgrading is simple and affordable as they gain confidence and the business needs to grow. Overall, the setup is user-friendly and efficient.

Neo4j AuraDB is reasonably priced, especially considering it removes the need for cloud administration and associated costs. It's a good deal for the professional version, as it includes managed services, which reduces the overhead compared to setting up your own infrastructure. The cost can be higher for enterprise-scale projects, but that's often due to the scale and complexity of the project rather than the product itself. Startups sometimes overestimate their needs and jump to enterprise pricing too quickly, leading to higher costs than necessary.

As a consultant, my decision to use Neo4j AuraDB comes from personal experience and client demand. Initially, I started using Neo4j when graph databases gained traction, which worked well for me. Clients began asking for it because Neo4j has a strong reputation and brand. Neo4j is an easy choice when presenting options to clients due to its established credibility.

If you’re considering using Neo4j AuraDB for the first time, my advice would be to first ask yourself why you need a graph database in the first place. Understanding your specific use case is crucial because graph databases are not a one-size-fits-all solution. You need to know how to design and implement it properly to avoid failure. If your use case fits, then I would recommend Neo4j. It's often a good starting point due to its reasonable pricing, strong support, and community resources. Many other graph systems have their own advantages, but Neo4j’s support and ease of use make it a solid choice.

For beginners, Neo4j AuraDB is generally easy to get started with. Downloading the desktop application and setting it up is straightforward. However, mastering it beyond the basics can be challenging. New developers with little experience in graph databases might find it hard to progress beyond the initial setup. The learning curve is steeper when moving to more complex development tasks. It’s important to understand the graph database concept itself, as applying traditional database knowledge may not always work well. While the initial setup is simple, deeper learning and effective use of Neo4j require a broader technical aptitude and a good grasp of how graph databases function.

Overall, I’d rate Neo4j AuraDB a nine. It’s a simple and effective tool for getting started with graph databases. The price is reasonable, especially for beginners, and it’s free for those who want to explore. As your needs grow, the pricing remains acceptable. It’s stable and has no major issues if you follow their process. It’s an excellent tool for learning and scaling, and Neo4j has a strong position in this market space.