Elastic Cloud (Elasticsearch Service) Reviews

I developed a chatbot with text summarization and question answering capabilities. I need to summarize multiple PDFs, and I have a database in Google Cloud Storage where I perform keyword matching with Elastic Search using exact keyword matching.

I have different clients, but I use Elastic Cloud (Elasticsearch Service) for one specific client. For that one job, Elastic Cloud (Elasticsearch Service) is the main tool because I am using an Elastic Search strategy instead of a vector database.

Scalability is valuable to me. I have 50,000 PDF JSON files that contain my metadata, and I am really glad to use Elastic Cloud (Elasticsearch Service) for this volume without any issues. From a startup's perspective, I would say that until 10 GB of storage, there is no problem whatsoever.

Application-wise, everything was easy to work with. I really appreciated their dashboard because everything was clear, and it was easy to implement.

Because I am pursuing a PhD and work under the university, my university has an agreement with AWS, which makes it essentially free and easier to use. In the AWS ecosystem, everything is connected and I can control everything without uncertainty about what is happening behind the scenes. However, when using Elastic Cloud (Elasticsearch Service), I connected it to Google Cloud but I am paying separate receipts. Over the last two months in October and November, I paid two separate invoices that are not connected to Google Cloud, which I did not appreciate.

Google Cloud has a nice interface that gives me full control of pricing and billing. I can see daily, weekly, and monthly breakdowns with bar charts, and I can track exactly how much I spent during any period. Elastic Cloud (Elasticsearch Service) does not have such a tool for billing visibility. Since I am handling significant amounts of money and am responsible for this task within my company, I have high expectations for pricing and billing transparency. I would appreciate the ability to set a spending limit, such as uploading 200 euros, and receive notifications when reaching 50% of that limit. These notifications could appear on the dashboard, in the application, or via email. It would be valuable to see a timeline of my spending.

I would characterize the pricing as somewhat expensive. I did not use competitors extensively, so I may have a bias about this. The pricing of large language models is not expensive—I use Anthropic's Claude or Google's Gemini, which are state-of-the-art models. However, I am uncertain whether I have a bias about Elastic Cloud (Elasticsearch Service) pricing. It is not extraordinarily expensive, but when I compare it with the cost of using large language models or Google Cloud storage, it is quite expensive.

A couple of days ago, the Elastic team reached out to me. We have been regularly using the service since April, and 10 days ago at the beginning of December, I deleted my hosted deployments because I did not like the idea of paying when I am not actively using Elastic Cloud (Elasticsearch Service). They informed me that there is a serverless option available. Before Christmas, I want to try it to see how it works, as I am uncertain about the serverless concept and whether it will provide the same functionality that I use with the hosted deployment.

I have been using this since April.

I have experienced no issues whatsoever in the last five or six months. Whenever I perform my searches, and because my application is active with clients using it, there has been no feedback about problems. During my tests, I did not observe any lag or delay.

I would give them a rating of ten. They were extremely helpful, kind, and communicative. Three people from the Elastic team spoke with me, and they were genuinely trying to solve the problem and understand my expectations. It was really excellent, and I recommend them highly.

Positive

I used the hosted deployment version, not the serverless option yet. The hosted deployment took me 10 to 15 minutes to set up. It was very easy and primarily involved indexing. I generated Python code on my local computer within minutes and pushed everything with the indexing. It took about 15 to 20 minutes total, though this is related to the size of my folder. I was indexing around 10,000 PDFs and creating metadata JSON files. The process was easy and fast.

I also use AWS daily in their ecosystem, which contains everything I need. I use Google Cloud primarily because of the pricing, as I must consider profit margins.

My team is small, consisting of about four or five people. On Elastic Cloud (Elasticsearch Service), only two of us work with it, but I am the one who uses it daily.

So far, I have not performed any maintenance.

Elastic Cloud (Elasticsearch Service) focuses on exact keyword matching. This means they do not address semantic similarity well. For example, if I use a word and then use another word with the same meaning in a sentence but not a synonym or similar word, Elastic cannot understand this semantic similarity, which is important for my chatbots. This is why I was using vector databases, as they focus on semantic similarity of words and tokens, whereas Elastic looks for exact word representation.

The team mentioned that hybrid search is an option. I have my own vector database that I use daily as a personal solution, and I could give hybrid search a chance, but I have not tried it yet.

I would rate my overall experience as seven out of ten. Two points are deducted for the pricing, which was higher than my expectations. The pricing has been significant over the last two months and was considerably more than I anticipated. My overall review rating for this service is nine out of ten.

My main use case for Elastic Cloud (Elasticsearch Service) is to capture logs from our various systems.

For our cloud service, we have various Elastic agents that ship logs into a central location. We have it all aggregated in our Elastic Cloud. From there, we use the logs for troubleshooting, creating alerts, look for specific patterns, understanding our service a little bit better, and aggregating all that data in one place.

One of the better features of Elastic Cloud (Elasticsearch Service) is Lucene Search, which gives our users the ability to search through the mountains of logs without giving them direct access to production systems.

Another great feature is Index Lifecycle Management that allows us to move data to cheaper storage tiers as our data ages out. The feature that we love the best is LogsDB, which allows us to index our data differently so that it doesn't accumulate as much storage in our hot tier and allows us to ship many of those logs, especially older logs to cheaper storage such as S3.

Elastic Cloud (Elasticsearch Service) has positively impacted my organization by allowing us to move away from expensive services such as DataDog and gives us about the same level of service while allowing us to keep data for a longer period of time at a cheaper price.

The logging feature of Elastic Cloud (Elasticsearch Service) itself is pretty valuable, but we tried the observability module and some of the AI features.

Those need improvement. Observability is not on par with feature and ease of use with some of the leading providers out there. The same applies to some of the AI features within Elastic Cloud.

I have been using Elastic Cloud (Elasticsearch Service) for five years now.

Elastic Cloud (Elasticsearch Service) is stable.

Elastic Cloud (Elasticsearch Service) is very scalable and very easy; we've had no issues with scaling our solution out.

The customer support for Elastic Cloud (Elasticsearch Service) is fantastic. They're very responsive, and gave us great detail in all our tickets.

I would rate the customer support as 10 out of 10. They are very knowledgeable.

Positive

I previously used DataDog. We switched because DataDog was too expensive, especially when it comes to logging.

It was very quick and easy to set up. The hard part for us was taking out the metrics and observability because it wasn't relevant for us.

The ROI for this has been positive.  We have seen a return of 30-40% in lower costs and improved productivity.  

Teams are more productive because they have a level of self-service to research problems without accessing production systems, which they previously did not have the ability to do.

Previously, accessing logs was complicated, but now everything is centralized. This has boosted productivity for our support teams, and both engineers and other staff can quickly view service logs and troubleshoot issues in a timely manner.

Before choosing Elastic Cloud (Elasticsearch Service), we evaluated other options, such as Grafana Loki, and Observability.io.  We found that Elastic matched what we needed the most.

LogsDB has made the biggest difference for our team because Elastic can get expensive as your data grows. Our teams want to view data back 30, 60, 90 days and with LogsDB, it allows us to be able to capture that data for a longer period of time and without the expense.

The advice I would give others looking into using Elastic Cloud (Elasticsearch Service) is to identify your pain point and find the tool that your users are familiar with.

For us, it was logging, and Elastic was perfect for that. Our users were very familiar with Lucene Search and the Lucene Search syntax, which made Elastic the ideal option for us. There are other solutions out there that are more multi-service, but Elastic does logging the best.

Elastic Cloud (Elasticsearch Service) really saves your organization money. You don't need the folks on the back end to manage it and support it on a daily basis. 

On a scale of one to ten, I rate Elastic Cloud (Elasticsearch Service) a nine.

Elastic Cloud (Elasticsearch Service) is a wonderful solution for seamless implementation and maintaining its health. It is much more reliable in the cloud than the on-premises issues that occur very frequently on-premises. However, Elastic does not cover the whole world, and in my region, the Middle East, there are very few hosting places for Elastic Cloud (Elasticsearch Service). It is good news that Elastic recently invested in hosting Elastic Cloud (Elasticsearch Service) in Saudi Arabia, set to launch in March, which I anticipate will lead to more customers adopting Elastic Cloud (Elasticsearch Service) in the very near future.

The only way to visualize data in Elastic, whether it is on-premises or in the cloud, is using Kibana. Kibana's cloud version is not different from the on-premises version, but Elastic Cloud (Elasticsearch Service) is usually more up-to-date, as Elastic maintains and consistently updates Elastic Cloud (Elasticsearch Service) to the latest version, while on-premises versions may lag behind.

I assess the machine learning capabilities of Elastic Cloud (Elasticsearch Service) as truly exceptional, although it is the least used and least understood among many customers. There are quick features that customers can benefit from, such as anomaly detection, but they can also add their own models, which some customers perceive as complex because they do not understand machine learning models and need to have data scientists on their teams to utilize that capability. If a customer uses machine learning in Elastic Cloud (Elasticsearch Service) heavily, they will find that it is very fast to get results compared to using other tools.

Machine learning might be expensive for customers. Customers take advantage of Elastic being open source, but machine learning is not available in the open source version. If a customer is using the open source version without paying licenses to Elastic, they will not enjoy the machine learning features. That is why machine learning does not have the same popularity as Kibana and the other components in Elastic, because only those who pay for Elastic can experience it.

Regarding additional features I would appreciate seeing in the next release of Elastic Cloud (Elasticsearch Service), Elastic acquired Gena AI, and I would appreciate seeing more AI models embedded in the upcoming new versions of Elastic Cloud (Elasticsearch Service). This is what I will be waiting for.

I would rate overall Elastic technical support a seven. It is very noticeable that they are good and responsive, but they heavily collect a lot of logs from customers before resolving issues, which makes the support ticket take longer than expected.

Positive

Some of my customers utilize Elastic Cloud (Elasticsearch Service), especially in the private sector, but most of the government sector do not use it.

Elastic Cloud (Elasticsearch Service) performs well. There are two types of Elastic Cloud (Elasticsearch Service): hosted Elastic Cloud (Elasticsearch Service) and Serverless Elastic Cloud (Elasticsearch Service). Serverless is more expensive compared to hosted Elastic Cloud (Elasticsearch Service), and controlling your bills in serverless sometimes becomes unpredictable, more often than in hosted Elastic Cloud (Elasticsearch Service). Hosted Elastic Cloud (Elasticsearch Service) is not adaptive; it does not rely on data rates, and you will know your spending from day one until the end of the year because unless you change the size of Elastic Cloud (Elasticsearch Service). As long as the size of Elastic Cloud (Elasticsearch Service) is constant, your bill is constant. With serverless, the bill changes frequently based on the influx of the data rate.

I assess Elastic Cloud (Elasticsearch Service)'s ability to handle diverse data sources such as logs and metrics as very good. Elastic managed to unify their data collection through Elastic Agent, the new version of Beats, allowing you to collect various types of data with the same agent. Elastic Cloud (Elasticsearch Service) is performing well in this area, although some data still needs to be ingested by Logstash, but Elastic Agent keeps improving over time.

My overall rating for this product is nine.

We use Elastic Cloud (Elasticsearch Service), Kibana, Enterprise Search, and on-premise as in a cloud environment within our Bosch environment, and we have different customers using the search, ML, and other services.

One of our customers uses Elastic Cloud (Elasticsearch Service) Agents out of the box when their server is installed, and this captures the metrics from the different servers within their environment, giving a unified Kibana view in the form of dashboards and helping us to understand the different key metrics which are relevant for them. They also use Elastic Cloud (Elasticsearch Service) for their search and indexing operations, and they also use agents and Fleet as different integration options, and finally, they also use the MLOps for their Elastic Cloud (Elasticsearch Service) ML for their AIOps purposes.

We've got close to about 50-plus customers and we've got three huge clusters of Elastic Cloud (Elasticsearch Service) on three different environments, and customers are happy.

One of the best features that Elastic Cloud (Elasticsearch Service) offers is their wonderful documentation as the technical support is very helpful. Every time I have a doubt, it's very easy to go through the Elastic articles, and if I have any questions and raise a support case, the technical support team provides valuable insights and recommendations. Even if I'm not aware of them, it really helps to make the product experience much better.

The integrations and features of Elastic Cloud (Elasticsearch Service) are very much kept up to date, and there's at least one or more use cases suiting every single need. There's also good room for customization, as Elastic Cloud (Elasticsearch Service) understands that different customers can have different needs, allowing customers to add their own integrations and edit or update them as they wish.

There have been quite a lot of good outcomes since using Elastic Cloud (Elasticsearch Service); customers have been able to use their data much faster and more effectively, and it definitely stands as one of the best observability platforms. We are also looking at integrating Elastic Cloud (Elasticsearch Service) along with certain other observability tools and CI/CD tools to give an overall comprehensive experience to our customers.

Elastic Cloud (Elasticsearch Service) is highly scalable, giving great options to scale the solution for the customer as at the cluster level. I've seen customers being able to deliver their results or web pages to their end users in a much faster way, increasing overall productivity and usage of their respective products, therefore leading to more profits. Using other conventional methods have been costly, so Elastic Cloud (Elasticsearch Service) has been a very cost-effective solution, and most importantly, the scalability meaning that you can upscale or downscale or even auto-scale the solutions as per the need has really reduced unnecessary waste, helping in cost reduction.

I don't think Elastic Cloud (Elasticsearch Service) has any sort of disadvantages per se; most of the features are pretty good and up to date.

We have some cost-effective indexing as searches with Elastic Cloud (Elasticsearch Service), and there could be other ways where we can probably improve in terms of the design of documentation. Sometimes it gets tricky to navigate through the user manuals because there are different forms of links. For example, we are speaking about ECE 3.x and ECE 4.x, and there are different sets of documentation for 3.x and 4.x. Sometimes it gets tricky to navigate through the documents, and the links can be difficult to catch upon. The content is fantastic, but if there is a better way to navigate through the documentation, that would be really great.

Mostly it's related to some sort of sloppy documentation at times, and we also have operational complexity. For example, we have some cases where the resource consumption due to the JVM could be pretty high; these are design-level issues and have also been discussed in technical topics, and if these could be improved, overall, that would be great.

I have been using Elastic Cloud (Elasticsearch Service) for close to about five to six years.

Mostly Elastic Cloud (Elasticsearch Service) has been stable.

Elastic Cloud (Elasticsearch Service) is highly scalable, giving great options to scale the solution for the customer as at the cluster level.

Customer support for Elastic Cloud (Elasticsearch Service) is great, as I have mentioned in the past; they provide great technical support, and the support articles are great, and the technical team is really brilliant and smart.

Positive

Previously, we used Splunk and that's not really effective; it is effective in its own way, but Elastic Cloud (Elasticsearch Service) is more of an integrated solution that has a lot of benefits and provides more features than Splunk does.

One time I was stuck in a technical issue with upgrading our Elastic Cloud (Elasticsearch Service) cluster operator, and it actually happened to be a completely different issue. I was probably misguided thinking that the root cause could have been something else, so Elastic Cloud (Elasticsearch Service) support helped me to deep dive into the case. We've had a couple of calls together, a lot of diagnostics were reviewed, and eventually, we were set on the right path realizing that there could be something else actually wrong and not what I had in mind, and then they set me in the right direction providing the steps to properly fix that; I was quite impressed by the way they and their team handled it.

A lot of money and time have definitely been saved with Elastic Cloud (Elasticsearch Service); I do not have the exact metrics, but overall, we've had pretty good results and outcomes.

We also went through some open-source alternatives OpenSearch, Solr, as DataDog before choosing Elastic Cloud (Elasticsearch Service). We still use a few of the other solutions for different use cases, but predominantly, Elastic Cloud (Elasticsearch Service) has been the main use of our solution.

I've covered pretty much everything regarding Elastic Cloud (Elasticsearch Service) in our previous questions.

It's a great product; it has so many features, great customer support, and it definitely has all rights to fit into every single use case of your applications.

On a scale of one to ten, I would give Elastic Cloud (Elasticsearch Service) a rating of nine.