the Cloud Run services comprise the container image (Container Registry + Cloud Storage), network transfer to load the container image, the Cloud Run service and network egress bandwidth

few battle-hardened options, for instance: Airflow, a popular open-source workflow orchestrator; Argo, a newer orchestrator that runs natively on Kubernetes, and managed solutions such as Google Cloud Composer and AWS Step Functions.

2. Use SuperQuery — With superQuery (installable via Chrome for example) you can even calculate your costs directly per query. In addition, the add-on offers many other useful features [3].

The only problem is that Kubeflow Pipelines must be deployed on a Kubernetes Cluster. You will struggle with permissions, VPC and lots of problems to deploy and use it if you are in a small company that uses sensitive data, which makes it a bit difficult to be adoptedVertex AI solves this problem with a managed pipeline runner: you can define a Pipeline and it will executed it, being responsible to provision all resources, store all the artifacts you want and pass them through each of the wanted steps.

Vertex AI came from the skies to solve our MLOps problem with a managed — and reasonably priced—alternative. Vertex AI comes with all the AI Platform classic resources plus a ML metadata store, a fully managed feature store, and a fully managed Kubeflow Pipelines runner.

Consider the amount of data and the speed of the data, if low latency is your priority use Akka Streams, if you have huge amounts of data use Spark, Flink or GCP DataFlow.

since TFX and Tensorflow were built by Google, it has first-class support in the Google Cloud Platform.

My friends ask me if I think Google Cloud will catch up to its rivals. Not only do I think so — I’m positive five years down the road it will surpass them.

So if GCP is so much better, why so many more people use AWS?

As I mentioned I think that AWS certainly offers a lot more features, configuration options and products than GCP does, and you may benefit from some of them. Also AWS releases products at a much faster speed.You can certainly do more with AWS, there is no contest here. If for example you need a truck with a server inside or a computer sent over to your office so you can dump your data inside and return it to Amazon, then AWS is for you. AWS also has more flexibility in terms of location of your data centres.

I would argue that there isn’t any other company on the planet that does scalability and global infrastructure better than Google (although CloudFlare definitely gives it a run for its money in some areas).

Both AWS and GCP are very secure and you will be okay as long as are not careless in your design. However GCP for me has an edge in the sense that everything is encrypted by default.

I felt that performance was almost always better in GCP, for example copying from instances to buckets in GCP is INSANELY fast

AWS charges substantially more for their services than GCP does, but most people ignore the real high cost of using AWS, which is; expertise, time and manpower.

GCP on the other hand has fewer products but the ones they have (at least in my experience) feel more complete and well integrated with the rest of the ecosystem

GCP provides a smaller set of core primitives that are global and work well for lots of use cases. Pub/Sub is probably the best example I have for this. In AWS you have SQS, SNS, Amazon MQ, Kinesis Data Streams, Kinesis Data Firehose, DynamoDB Streams, and maybe another queueing service by the time you read this post. 2019 Update: Amazon has now released another streaming service: Amazon Managed Streaming Kafka.

At the time of writing this, there are 169 AWS products compared to 90 in GCP.

Spinning an EKS cluster gives you essentially a brick. You have to spin your own nodes on the side and make sure they connect with the master, which a lot of work for you to do on top of the promise of “managed”

After you login with your token you then need to create a script to give you a 12 hour session, and you need to do this every day, because there is no way to extend this.

In GCP you have one master account/project that you can use to manage the rest of your projects, you log in with your company google account and then you can set permissions to any project however you want.

It’s not that AWS is harder to use than GCP, it’s that it is needlessly hard; a disjointed, sprawl of infrastructure primitives with poor cohesion between them.

Use your favorite API framework and language, or choose our open source Cloud Endpoints Frameworks in Java or Python. Simply upload an OpenAPI specification and deploy our containerized proxy

VPC network example