Amazon AWS Reviews

I am a software developer and I have experience with several languages and technology stacks. Amazon AWS is one of the technologies that I work with. It's integrated with the solution that we have. It's a continuous integration and deployment pipeline.

The most valuable feature is the availability, as we work in different availability zones.

It has been easy to use, and the tools included are quite friendly.

The reliability and ease of use are the benefits.

At times we find ourselves a little trapped, with the lack of customization, for what we need. That doesn't mean that the tool is lacking it means that we are trying to be more creative than the tool and the rest of the market. In cases like this, it is we who need to revise our plans.

I have been working with Amazon AWS for one year.

We are pretty happy with the stability.

The only issue that we have encountered is when Amazon had problems with one of their availability zones that impacted half of the world. We found that we were impacted as well, but it wasn't that difficult for us because we already had a solution with multiple zones. We had a minimal outage, as we were swapping from one server to another. It took less than ten minutes for us, so we were pretty pleased.

It's a scalable solution.

We have both options of vertical and horizontal scalability. Everything that we have needed so far has worked well. We have no complaints.

We are in the process of scaling up. We have an IoT solution and if we translate that to endpoints and devices that are monitored, we would have 70,000 devices, and counting, in the field. In terms of clients, there are 300 to 400, each of them with their own users.

We have contacted technical support and because we are just a small client, rather than a partner, it can take 30 minutes to get a solution. We have not had to use it much to this point, so this may not be a fair evaluation.

This solution was already in place when I started with the company. AWS was in-place and they have never switched to anything else.

Our build, or deployment, is dependent on the application, but the pipeline for submitting a new commit and making it a hot deploy would take from five to twenty minutes, depending on the solution.

For our infrastructure, the cost is approximately $25 per device, and you have to include the other tools that we have in the cloud, for a total of approximately $200,000 per year. Our tools included several databases and Kubernetes. If the price was a little bit cheaper, I would consider this solution to be a ten out of ten.

I'm not very experienced in the solution yet. I don't have a clear view of all that is offered, but with the experience that I do have, I'm pretty happy with the features and it is difficult for me to find where they are lacking.

Currently, I am switching to Redshift, which is one of their solutions that is already deployed. I can't say that I'm missing anything from their roadmap, so far.

I would rate Amazon AWS a nine out of ten.

Auto Scaling and CloudWatch Logs are the most valuable features. With just a few criteria to scale in/out of, you can save the life and time for DevOps.

The CloudWatch Logs feature provides many plugins, so that we are able to manage various types of logs centrally.

In the era, we used private clouds as network virtualization must be controlled by the IT division, server rooms were in the remote branches and DevOps were distributed in various areas. Now, we can use the same API and the same workflow without considering to centralize the logs.

IaaS is sometimes way too complicated to complete one task.

I have used this solution for around eight months.

So far, the VPC is sometimes not that reliable. Therefore, we have to set up a redundant VPC to make sure the connection is always available.

We do not have any scalability issues until now.

I have never used technical support.

Previously, we surveyed OpenStack. However, due to the time, budget and manpower limitations, building a private cloud is not practical in our case.

Managing IaaS was very difficult in the beginning, i.e., tons of jargon to get up and I struggled for months.

Try the Google Cloud Platform (GCP). It is yet another good choice because sometimes, what you need is just a platform and not to build a platform from the infrastructure.

When your division grows to a certain scale and you really need DevOps, then you could move either to a private/public cloud. Otherwise, it is a waste of time and money.

We use the solution for cloud service and analytics. 

The tool's most valuable features are AWS Security Hub and AWS GuardDuty.

There are plenty of areas for improvement. For example, the ease of tagging could be improved. The tool could integrate AI tools to identify better and manage costs. Calculating the cost of some services could be more straightforward.

I have been working with the product for six to seven years. 

I rate the tool's stability a nine out of ten. 

I rate Amazon AWS' scalability a ten out of ten. My company has 200-300 users.                   

The quality of technical support depends on how much you pay. 

Neutral

We used GCP before and switched to Amazon AWS because it was cost-effective. 

I rate Amazon AWS' deployment ease as five out of ten. It can take a few weeks to complete. 

The deployment process was completed in-house.

The solution's ROI is good. 

I rate the tool's pricing a six to seven out of ten. 

I recommend the solution to others because of our smooth experience. I rate it an eight out of ten.

My primary use case was as a place to migrate legacy systems.

AWS has reduced our costs and maintenance requirements. It also allows us to control our load in peak times and automatically increases or decreases your capacity as required. 

The features I have found most valuable are S3 buckets and Lambda services.

An area for improvement would be API creation - a lot of tools are provided, but there can be issues with integrating them. There is also a cost underlay, in that at the end of the day, some costs are not in the picture, so AWS needs to improve its costing toolset.

I have been using this solution for two years.

The stability of AWS has improved over time and can now be well managed.

This solution is scalable to any limit.

I previously used Rackspace Openstack but switched because it required more manpower than AWS, and AWS is more cost-effective and tolerant.

The setup was somewhat complex. It was done in three phases over a year.

This solution reduced our overhead cost by 30-40%.

Technical support is expensive to use.

I evaluated Oracle Cloud, but it was more complex to use and provided a smaller toolset than AWS.

This solution is one of the top tools available for legacy migration. I would rate this solution as eight out of ten.

My primary use case is to set up an end-to-end application to deliver a business case involving data ingestion, processing, transformation, and checking, followed by outputs to other functions and processes in AWS and also to external systems.

We are using Step Functions as a core automation tool and it offers great power through its simplicity. It is quite easy to use, although there is a learning curve when using the Step Function scripts. Once mastered, after a week or so, the flows can be built quickly and effectively, allowing us to link a custom business process to multiple other AWS service automatically. 

That done, most business cases can be delivered easily and quickly, all in a serverless and cost-effective way. 

AWS has improved my organization by:

- saving us time, cost, and difficulty by allowing us to use serverless services

- enabling us to assemble complex applications with the minimum of boilerplate and plumbing

- allowing us to pay-as-we-go, so we can rapidly prototype, test, and then deploy to a production application setup

We can run advanced demos with our own data very quickly, showing potential clients the value of our services when we assemble apps for them.

We can show customers clear cost benefits and clearly effective solutions when assembling AWS services together. 

The security has great IAM, roles, and carefully partitioned permissions that allow us to fine-tune control across our applications. External intrusion attempts will never get past application boundaries, which increases trust.

The composition of apps has everything wrapped according to function and applications. We can assemble services as we go. This speeds delivery times by orders of magnitude.

The price forecasting and billing dashboard by service, with billing budgets and alerts, have helped us shut down resources that were accruing costs that we no longer needed, saving us money.

The service's power lies in its simplicity. It is great in that respect. 

The UI is constantly being improved and the billing dashboard has been improved.

Previously, we asked for more end-to-end workshops, examples, and tutorials and these have been added and improved. 

Recently, AWS has been adding improvements across services, documentation, tutorials and we have now got workshops with real-world scenarios which are tremendously useful It makes me a very happy user. 

AWS and the cloud is a space for constant learning and AWS has increased their output in that respect. 

I have been using AWS since 2014.

The solution is very stable. The only errors I encountered were my own. Some services took a few minutes to refresh and propagate across my environments, and once these had propagated, the solutions were rock solid.

The scalability is excellent. At no point have I hit scalability limits with AWS services and features. 

Customer service and tech support were excellent a few years ago when I needed them.

My general process is to explore and check options and run from a tutorial or AWS workshop. If this doesn't get me results, I then do a web search, and I generally find either further AWS docs or a specific example I can use to solve my issue. Within the last few years, my colleagues and I have been able to deliver as required. 

We did previously use a different solution when building AWS Lambda cloud functions. I could compare them directly with Azure Functions and Google Cloud and have found that the AWS Lambda solution is simpler, clearer, deploys quicker, and is generally much more simple and effective to use.

In terms of documentation, AWS is the clear leader. Their end-to-end examples and workshops are much more effective.

AWS services in many cases are deployed to AWS after being validated in Amazon.com's operations. This is evident in the ease-of-use and simplicity of many of the service features, and also in the excellent options offered for more complex services like AWS Forecast, where, for example, a checkbox and drop-down allows the user to add holidays for the country they work in when doing forecasts.

AWS has a stronger focus on business solutions than either GCP or Azure, and in many of the solutions, I have used. This is why in many cases I have switched from using other clouds, to AWS. 

The setup in AWS is a whole service in and of itself. To set up AWS applications, AWS offers a full service, CloudFormation, with some added features that allow us to automate the deployment of the full solution stack.

This makes setup complex, in that one must modify the CloudFormation template one requires and validate it. An external resource was required to check the templates. 

Once this is done, the full solution stacks are automatically deployed. 

I handled the initial setup in-house and by myself.

A recently deployed Step Function automation fulfilled all the needs of a workflow automation engine while remaining below the free operation per month, so we were able to deliver a fully automated application approval process without paying for any workflow automation engine license fees or any server hardware or infrastructure costs.

I would advise others to work from an architecture overview. 

Be aware of the very powerful schema-less data services in the cloud. They can help remove the need for data warehouses - e.g. multi-TB datasets - can be read, joined, queried and made to output daily reports within minutes, on temporary clusters, and that cost less than USD1000 per month. This is compared to the hundreds of thousands of USD for data warehouse licensing costs, plus the schema design time and ongoing DevOps they require.

Moving to serverless operations in the cloud frees up your people to deliver business services rather than spend days and days on administering data centers and the associated concerns that come with them.

I also looked at Azure and it was deemed less reliable than AWS as AWS has not had as many outages and uptime concerns as Azure has had of late. Azure Function Apps, Data Factory, Managed SQL.

Besides Azure, I looked at GCP and VMs, Cloud Functions, Speech-to-Text transcription, BigTable, and BigQuery.

Empower your in-house people to start building and running their workloads in AWS. 

Let them learn as they go. There are multiple online courses for a few dollars that can assist with specific, individual AWS services, as well as running through the AWS workshops. 

Incentivize AWS certifications. Involve your tech people with business solution prototyping. 

Tag your resources, name them well, and set budget thresholds. Assign people to tune the resources being used. Incentivize communications and publish the AWS services and features being used to deliver your business capabilities.

Customers can use it for the web-based management of the product. We also store and retrieve data for their network connections. Also, we use the AI/ML portion called SageMaker to calibrate the algorithms and basically drive automation into the customer's use case. Typically our use cases are in hotels, public transportation, convention centers - anywhere where you are sharing internet connections. For example, hotels, conventions centers - anything where you might have people jockeying for a shared internet connection with possible oversubscription or network congestion. We also have enterprise Work-From-Home users due to the pandemic and they need to continue to provide access to those remotely into their own data center, corporate network, and public cloud.

Flexible fast way to bring up servers and network infrastructure with variable costs.

We use the AI/ML Sagemaker to help us build models. 

We use several feature services on AWS, including Lambda, S3 database, RDS database, Alexa Voice Services and Cognito Gateway. They are all excellent in terms of offering great functionality.

They're pretty good about taking customer feedback and are generally able to productize the requested feature.

The initial setup is straightforward, especially if using Lightsail to start.

The solution scales very nicely.

The stability is good with a large number of Availability Zones WW.

Technical support is helpful and responsive but you must pay for a tiered support plan to ensure response.

The pricing is something you have to watch. You really have to constantly optimize your costs for instance, storage, IP's and things like that. That can become a job in itself to manage just from a budgeting standpoint if you are a moderate to heavy user. However, that's true for Azure or GCP as well. 

If they did more automation on alerting you to cheaper pricing or automated volume pricing based on time/use or even porting you on to on-demand instances automatically, that would be kind of cool. That's something that I haven't seen yet. They could just automatically optimize for your workflow and put you onto a lower-priced instance to save you money.  you Maybe allow you to pick an economy setting, or a performance setting, by time of day etc. something like that. That would be great. Then you don't have to think about it as much as you do in the current iteration.

It would be interesting to have a cost optimized accounting service so that they would come in and help remediate and give suggestions on how to cut costs. I know it's probably antithetical to their bottom line, but that said, obviously, if you take the high road there, you're going to probably keep people, and keep people from switching for lower costs. A lot of times, they can architect a better solution or a similar solution for lower cost and that would lead to customer retention--or maybe a longer term retention discount if youve stayed with them for awhile. That would be helpful if they had that. They have solutions architects, to consult however, they're usually just trying to design the best technical solution as opposed to the most cost-optimized solution. 

We've been using the solution for about four years at this point.

Services are pretty good stability-wise. They've got great redundancy. The one thing I would tweak them is when you're within the region or zone, they make it more difficult for you to do redundant zones, without carrying the IP addresses over seamlessly. That is a little bit of a sticking point, so you could have remote redundancy with the addressing there with it even outside of the AZ's. That would be a lot easier than having to go through the programming of it. 

The scalability is great. You can go from one small instance to GPU, very powerful instances, clusters. There is not any problem with scaling if you can afford it. If you've got the volume, you certainly can scale.

We have maybe a dozen or so customers that will use the product and then access the UI and the management system through the cloud. Then, of course, as developers, we have about 10 to 25 employees that have to use it to varying degrees to support the customers and do development.

I like the tech support. It varies by level in that you've got to pay more to get the immediate response time. Generally, I'd say it's pretty good. Literally phone rings minutes after you log a trouble ticket. They're usually pretty good about escalations and helping. Out of AWS, Azure, and GCP, I'd give them the number two ranking. Azure has good support, however, it's expensive. GCP probably is number three I'd say, of the top three.

We also occasionally use the Google Cloud Platform and Azure, although we tend to use AWS the most. GCP is a little bit cheaper overall, however, then you've got the cost of management that is typically a person so you do need to invest in that. 

We started with Amazon and we've pretty much stayed with them. We've switched to Google and done some work on Azure that was customer driven, however, pretty much our prime public cloud has been AWS.

The initial setup is not overly complex. It's pretty straightforward. 

It's pretty easy to get started. However, you do have to make an investment and learn the different cloud platform's nomenclature. Most of our guys now are cloud practitioners and architects now that they've taken the training. We had to bite the bullet even though we've been users for four years. There is an investment that you have to make on the OPEX side. That's the case for any of the public clouds. Although once you know one, you can pretty much pick up the other ones pretty quickly.

In-house

Have to watch price/billing creep, but there are tools to watch and monitor your usage and billing.  

Azure. GCP

We're a software development group building specialty LAN/WAN optimization solutions, so we don't use a lot of canned products per se.

We do tend to sue reasonably new software versions of the OS...whatever is the latest LTS selections.

If you already have your workload ready, that's helpful, as you can actually trial it under a free tier and then see what the cost is, and extrapolate what the ongoing cost is. In the end, that's what gets you. Being able to do some benchmark testing on how much it's going to cost for your particular workflow across the three public clouds is definitely something you probably want to do. Especially if you're going to scale, as, obviously, it can suddenly creep up to not just tens or hundreds of dollars a month, but thousands a month, depending upon what you're doing. I definitely would recommend doing some reference testing of your workflows before deciding on a solution.

I'd rate the solution at an eight out of ten. They're pretty solid. You've got all the services that you can imagine, and then some. There's a very broad breadth of products and services. We haven't had too many SLA issues for recovery or downtime. Maybe we've just been lucky or good so far...

We are a product cocktail service company, and we deliver identity-based solutions that customers can subscribe to. The back-end infrastructure is hosted in AWS.

It helps us by saving money. Having a physical server on our premises is, of course, quite expensive as compared to building an EC2 instance in the cloud.

We use AWS in-built services like EC2, ECS, Lambda services, CloudTrail, CloudWatch, etc. AWS is a very big platform that provides a lot of services.

The main reason why we use EC2 is because we are not dependent on maintaining the hardware inside our premises. Also, we have full control over the infrastructure, and we can modify it as per our own requirements.

It is stable and scalable as well.

Installation is quite easy.

Monitoring still needs to be improved.

Better support for Windows Operating Systems would be appreciated. It is good for Linux but needs to improve for Windows.

I've been dealing with Amazon AWS for more than four years now.

We are experiencing 99.9% uptime already, so we have had no issues with stability.

It is scalable. We have configured our Auto Scaling options there, which enables us to install as the requirements increase. It auto scales the server.

Technical support is okay. I will not rate it too high, but I think it might be because it is part of a basic plan. We do not have the enterprise support plan.

You don't require much technical expertise to have an instance, so the initial setup is quite easy.

On a scale from one to ten, I would rate Amazon AWS at nine.