Codetown ::: a software developer's community
I don't clearly catch the difference betwenn these two concept. Someone told me that the essential différence is that the cloud computing give you a large space of storage and the grig give more advantages than storage, we can profit to much power with this last.
Does any one know more clearly these two concept; and tell us?
Tags:
I don't claim to be the expert, but the difference is (I think) in use.
Grid represents a scalable framework. You write your algorithm and your code and use as much computing power as you wallet can afford. (Useful as some work can be highly parallelizable) .
Cloud computing offers storage (true) but it's also represents the applications as well. Ideally with cloud computing, you don't need to have certain applications on your desktop - as long as you can hit the cloud, you can get, update, and use your data.
Thanks thomas;
What I got :
Grid - much computing power and can be highly parallelizable
Cloud - Storage and dont need to have certain applications on your desktop ( that's just like server application?)
Someone can tell us more?
I think if you look at the history, you will understand some difference.
In my own experience, the grid began with Oracle using it as a type of metadatabase, which would point to multiple databases residing on different but uniform hardware systems. So if a company had multiple unix boxes and needed to increase the size of their database, instead of purchasing additional hardware they could implement the grid database and combine their multiple unix servers into one database resource.
Cloud is much more in terms of it offering not only a database, but also an entire server including the operating system.
The cloud exposes an operating system, whereas a grid exposes a database.
But I am no buzz word expert so I might be wrong.
I just talked to a buddy about this, essentially the Oracle Grid product is differant because it runs the DB in memory. So access times are a lot quicker. I don't think it is really a matter of Vs. so much as Grid computing is a way to handle db transactions in a faster way.
He said their grid servers had something like 72gbs of ram. Freaking crazy
Please Bradley, wha do you think about Jackie's reaction?
Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
Check out the Codetown Jobs group.

Docker recently announced the release of Docker Desktop 4.50, marking another update for developers seeking faster, more secure workflows and expanded AI-integration capabilities.
By Craig Risi
Microsoft's Azure Virtual Desktop (AVD) now supports hybrid environments, enabling on-premises Arc-Enabled Servers to act as session hosts. This integration enables customers to run virtual desktops in their data centers while leveraging cloud management tools. The update enhances flexibility, compliance, and operational integrity across various industries.
By Steef-Jan Wiggers
The Kubernetes SIG Network and the Security Response Committee has announced the retirement of Ingress NGINX, one of the most widely deployed ingress controllers in the ecosystem. Best-effort maintenance will continue until March 2026, after which there will be no further releases, bug fixes, or security updates, according to an announcement made at Kubecon NA 2025.
By Matt Saunders
Evalite is a TypeScript-native eval runner designed for AI applications, enabling developers to create reproducible evals with rich outputs. Featuring first-class trace capture, scoring, and a user-friendly web UI, Evalite enhances testing ergonomics and iteration speed. Open-source under MIT, it seamlessly integrates with any LLM, ensuring complete data control and fostering rapid development.
By Daniel Curtis
AI workloads are growing more complex in terms of compute and data, and technologies like Kubernetes and PyTorch can help build production-ready AI systems to support them. Robert Nishihara from Anyscale recently spoke at KubeCon + CloudNativeCon North America 2025 Conference about how an AI compute stack comprising Kubernetes, PyTorch, VLLM and Ray technologies can support these new AI workloads.
By Srini Penchikala
© 2025 Created by Michael Levin.
Powered by