Google Cloud Reviews & Product Details

It makes it very easy to run containerized serverless web servers, better scaling

Aws Lambda works better in terms of going serverless as cloud run uses the compute power of the web server

Helping us with going serverless, better code isolation, you don't have to pay for invocation of the request handler , you don't pay for the api gateway request

CloudRun is a service that allows you to quickly and easily deploy container-based services without the need to deal with the complexity of full GKE. CloudRun is based on knative (https://knative.dev/docs/) open-source project and is provided as a managed service within GCP. This means two things: 1. There is no vendor lock since you can always migrate your deployment to knative project hosted on any other platform 2. There is no technology lock since CloudRun uses standard containers. It means that if, at some point, your project outgrows CloudRun limitations - you can always switch to GKE. This makes CloudRun a great starting option for many projects. It removes the need to deal with GKE complexity until the project gets traction.

There is not much to dislike. Your projects either works within CloudRun limitations (and than CloudRun is great) or it don't (in this case CloudRun won't work for you and you need to use GKE). Of course, there is always more features that you can wish would be added to CloudRun out-of-box (like permanent storage for example). But since service is limited by design - it is not about disliking it, but about the question "can your project use it or not"...

In case project is represented as a singe container, CloudRun allows to deploy and scale this project without any overhead or complexity. We use it for many small projects or prototypes.

CloudSQL is easy to use managed SQL service. It is very convenient if you need traditional OLTP DB (PostgreSQL, MySQL and SQL Server) and want to enjoy managed service.

It is not really a dislike but rather a caution. While CloudSQL provides traditional OLTP DB as managed service, some of the features are removed/not-supported due to the need to run those DBMS in control sandbox. So, before migrating to CloudSQL your existing applications you need to check that all the features that you use are supported.

We use it as managed version of PostgreSQL and MySQL DB. We do not use SQL Server version of it (instead host SQL Server DBMSs due to the need to use SSRS, which is not supported by CloudSQL).

The integration with Google Analytics, Search Console and Youtube Studio.

Editing data in the dashboard can be complex sometimes.

With Looker Studio I can present data more easily to my clients.

It's a very simple, fast, and life-saver API for generating high-quality, natural-sounding speech from written text in a variety of languages and voices. It is able to generate speeches with various languages, accents, etc.

In some cases, it can produce robotic-sounding voices that are not sounds warm to the customers. It should perform more warmly even the words not familiar to it.

We needed a TTS engine to read blogs in real time to a blog website audience. Google Cloud TTS was the first choice. It helped to develop this feature fastly. We used the benefits of API, no need to develop big programs for a TTS requirement.

I have been using Google Cloud Run for several months now, and I have been extremely impressed with its performance and cost-effectiveness. The scalability and pay-per-use pricing are particularly appealing, as they allow me to easily deploy and run my applications without having to worry about capacity planning or paying for idle resources.

In order to better understand and prevent potential issues with the microservices running on Cloud Run, it would be helpful to have more visibility into the relationships and dependencies between the different services. A service mesh similar service could provide this visibility and allow for more effective monitoring and management of the microservices on Cloud Run.

As a developer, Cloud Run can be a useful solution for deploying and running containerized applications in a fully-managed environment. The platform's scalability, pay-per-use pricing, and ease of deployment can help reduce costs and improve efficiency by automating the scaling and management of our applications. This can allow us to focus on other aspects of our business while still being able to take advantage of the benefits of a cloud-based infrastructure.

Looker allows me to use different data sources, and I don't need to know each specific SQL syntax. I don't need to know DAX. I am using git and I also don't need to know git CLI.

Information about buffering results. I made a report; I saw charts. Then I enable predictions, and nothing happens. When I clear a buffer and rerun the command, I see new Looks with forecasts included.

It unifies business definitions. In the past, each person that made a report wrote an SQL query, and they often used different definitions to calculate the same measure. Looker provides us with a model with only one definition.

No administration is needed. I can choose from multiple options for replicating my DB and whether I want a backup. I can use Google IAM roles. I can query data from BigQuery or Looker. It is well integrated.

I don't like working with it from the console level. Also, working from CLI is hard for me. I like traditional software to connect with DB, like Oracle SQL Developer od SQL Server.

It reduces effort related to hardware maintenance and generally with database administration. It also makes data more available for microservices than in the past with on-prem databases.

It deploys very fast from container to production.

Sometimes when we meet the application with background tasks, it really does not support very well.

It helps us to deploy simple applications in a super easy way because of combining Docker.

Google Cloud SQL has a user-friendly interface and supports a range of popular database engines, including MySQL and PostgreSQL. This makes it easy for our users with database skills to get started with the platform. Its fast query performance helped our businesses to process large amounts of data quickly. The integration with other Google Cloud Platform services, reliability, security, ease of use, flexibility, and fast query performance of Google Cloud SQL make it a valuable and powerful database service for our businesses.

The main downside of using Google Cloud SQL is its potentially higher costs. However, for us, the integration with other Google Cloud Platform services, reliability, security, ease of use, flexibility, and fast query performance of Google Cloud SQL more than make up for these potential drawbacks.

Google Cloud SQL is a powerful and feature-rich database service that helps our businesses and organisations solve various problems related to data management and analysis by providing a scalable, cloud-based, and easy-to-use platform for storing and managing our data.

Google Cloud Text helps to convert Customer calls to text, whereas if there will not be such a service, you have to store audio or video files it helps to save fewer files and less size of files

The use case we had with this product was good and fulfilled the need, maybe not dislike the product but more examples and documentation are missing, unlike other products

Save audio and video calls into text file, so you have less file size and easy query possibilities, and helping sometimes understand the word that person want to share in call.

Cloud Run enables possibilities for old and legacy apps to get the opportunity to be moved to a containerized solution without knowing a lot about infrastructure and Kubernetes.

For now, I think for the purpose it was created it's perfect, of course, Clour run can't fulfill every requirement but there is no software that can do that! I really like how Clour Run works and Google offers other services to use if Clour Run will not make the cut.

Give developers without any DevOps or infra skills to start with the containerized app, very easy to start and deploy, with very minimum management and control needed from the infrastructure team.

All it takes to run a container in cloud run is to upload an OCI image to a registry and pass the image URL to Cloud Run. Since the container is self-contained, I can use whatever language I want. E.g. Go is a predestined language for Cloud Run as you can create an image directly from the go binary without any additional files. A/B testing is easy with Cloud Run as I can send a certain percentage of my traffic to a new version of my container. In contrast to Cloud Functions an instance of Cloud Run can handle multiple concurrent requests without starting additional instances.

Running containers comes at the cost of building and maintaining the container. That might require CI/CD pipelines and lifecycle management for the dependencies you are using in your container. Not the fault of Cloud Run, but you need to keep this in mind.

We are using Cloud Run for services that need to scale on demand, where we have a very different load.

Cloud run let the freedom to the developer to use the language of their choice, the runtime environment they prefer and to deliver awesome applications at scale.

Cloud Run still have some limitations. Only HTTP traffic is supported (TCP and other protols are excluded), the 1h timeout can be limiting for some use cases, and the constraint to use an HTTP server can be a constraint

To deliver APIs, it's often complex to deploy it at scale, to pay only when the service is running and to not waste resource the night and the weekend. Cloud Run solved all this challenges

Google Cloud Run integrates the programming languages like no other cloud platform does in a efficient and effective manner. You can just write your code to run full featured sevices to build application from scratch without the need of parallel infrastructure.

Google cloud run is doing exceptional job for programmers who are wanting to build the application on cloud infrastructure to run service, would love to see logging and error report for SQL as well.

Google cloud run supports wide range of programming languagesto run various scripts from database migration to other operations use. Building your own Web and hosting on cloud was never easy before with support ofrender dynamic HTML. APIs are doing wonders too.

There is support for not only some of the most commonly spoken languages but also many regional ones, and the quality is surprisingly excellent. However, there is not much data for these languages. Integrating them with other cloud services is very easy, and they can modify speech by changing the features like pitch, speed, etc.

So far, my experience has been good, apart from the beta feature where one can upload voice data to get a custom text-to-speech model. It requires requesting google for permission, just that since this was beta, it took a reasonable amount of time to build this.

We are using it to serve our users in different world geographies by allowing them to interact with our chatbot using voice commands in their regional language, which they might be more comfortable with.

The pleasure of reinforcing my data analysis system is excellent; I can offer a set of explanatory reports of sales data or business conducted in a unique way, and it is very versatile in terms of interface. It has been beneficial and revolutionary for managing or distributing information based on the aggregation of external data. Its support is growing at a dizzying pace.

I don't find it a very high caliber of software in specific search formats.

We do a lot of data analysis with a high-efficiency rate in actual and frequently updated data, so we can expect the software to always be evident in optimizing the company's internal databases. It is a pretty solid program that allows me to have regulated data, it was an excellent software that I liked, and the result was as promised.

It is an easy and awesome NLP tool that converts text into spoken words. This tool improves customer interactions with lifelike responses. The best part is we can choose from a variety of audio.

There's nothing that I dislike about this product.

It can be integrated with bots that can speak text and increase the feeling of real-life interactions instead of monotonic pre-recorded audio. Similarly, it can be embedded with various devices as well.

I think the adaptive learning capability of google Text to speech is mind blowing. I have a bit of an accent, so Google TTS did not work very good for me at first. But it got surprisingly better the more I used it.

I don't really have anything I dislike about it at the moment.

I use Google Text-to-Speech daily in my office to transcribe meetings.

So easy to use. Built for the cloud, so there are no wonky problems with syncing. Team easily shares documents, edits together in real-time and can access what they need from wherever they are.

There are a few tools and features that don't work as well in Google than in 365 products... like the paint button has to be clicked EVERY TIME you want to paint the format (no double click to toggle on for multiple changes). And there are basic things you can do in Docs that you can't do for some reason in Sheets, like add a symbol. But these are pretty minor.

We needed the team and clients to be able to easily share documents and edit together in real-time and access what they need from wherever they are. Team and clients are all over the US so cloud based, remote product was key.