Compare Compute@Edge vs. Fastly

Fully managed compute platform to deploy and scale containerized applications securely and quickly. You can write code in your favorite languages, including Go, Python, Java Ruby, Node.js and other languages. For a simple developer experience, we abstract away all infrastructure management. It is built upon the open standard Knative which allows for portability of your applications. You can write code the way you want by deploying any container that listens to events or requests. You can create applications in your preferred language with your favorite dependencies, tools, and deploy them within seconds. Cloud Run abstracts away all infrastructure management by automatically scaling up and down from zero almost instantaneously--depending on traffic. Cloud Run only charges for the resources you use. Cloud Run makes app development and deployment easier and more efficient. Cloud Run is fully integrated with Cloud Code and Cloud Build, Cloud Monitoring and Cloud Logging to provide a better developer experience.

BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises. Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

JetBrains Junie is an innovative AI coding assistant that works inside many JetBrains IDEs to streamline programming efforts and boost efficiency. This agent leverages advanced AI to help developers write, test, and inspect code without leaving their familiar development environment. Junie offers both code execution and interactive collaboration, allowing programmers to switch between automated code writing and brainstorming sessions for features and improvements. By deeply understanding the codebase, Junie identifies the best ways to tackle tasks and ensures all changes meet quality standards through syntax and semantic checks. It also runs tests to minimize errors and keep the project healthy, freeing developers from routine tasks. Many developers have successfully built complex applications and games using Junie, highlighting its flexibility across different languages and frameworks. The AI adapts to each task’s complexity and workflow, making coding less tedious and more focused on creativity. Whether you are building a simple web app or a complex game, Junie offers smart support throughout the development cycle.

Pipefy is the AI-enhanced business process management platform that conserves IT resources and empowers business teams to build and deploy up to 85% of their own workflows, including HR, IT, Finance, Marketing, Sales, and more. Easily build, track, automate, and manage any custom workflow, no matter how complex, with no technical knowledge required. Pipefy’s no-code BPM software delivers an integrated end-to-end operation that improves team productivity, control, and visibility from input to delivery. Speed up deployment, lower implementation costs, and minimize process complexity and expensive stack sprawl with a built-in security suite, connections to 300+ apps and systems out of the box, and HTTP/HTTPS to integrate Pipefy with everything else.

RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.

Compute Engine (IaaS), a platform from Google that allows organizations to create and manage cloud-based virtual machines, is an infrastructure as a services (IaaS). Computing infrastructure in predefined sizes or custom machine shapes to accelerate cloud transformation. General purpose machines (E2, N1,N2,N2D) offer a good compromise between price and performance. Compute optimized machines (C2) offer high-end performance vCPUs for compute-intensive workloads. Memory optimized (M2) systems offer the highest amount of memory and are ideal for in-memory database applications. Accelerator optimized machines (A2) are based on A100 GPUs, and are designed for high-demanding applications. Integrate Compute services with other Google Cloud Services, such as AI/ML or data analytics. Reservations can help you ensure that your applications will have the capacity needed as they scale. You can save money by running Compute using the sustained-use discount, and you can even save more when you use the committed-use discount.

Enhance your productivity by ensuring only high-quality code is released, as SonarQube Cloud (previously known as SonarCloud) seamlessly evaluates branches and enriches pull requests with insights. Identify subtle bugs to avoid unpredictable behavior that could affect users and address security vulnerabilities that threaten your application while gaining knowledge of application security through the Security Hotspots feature. Within moments, you can begin using the platform right where your code resides, benefiting from immediate access to the most current features and updates. Project dashboards provide vital information on code quality and readiness for release, keeping both teams and stakeholders in the loop. Showcase project badges to demonstrate your commitment to excellence within your communities. Code quality and security are essential across your entire technology stack, encompassing both front-end and back-end development. That’s why we support a wide range of 24 programming languages, including Python, Java, C++, and many more. The demand for transparency in coding practices is on the rise, and we invite you to be a part of this movement; it's completely free for open-source projects, making it an accessible opportunity for all developers! Plus, by participating, you contribute to a larger community dedicated to improving software quality.

Site24x7 provides unified cloud monitoring to support IT operations and DevOps within small and large organizations. The solution monitors real users' experiences on websites and apps from both desktop and mobile devices. DevOps teams can monitor and troubleshoot applications and servers, as well as network infrastructure, including private clouds and public clouds, with in-depth monitoring capabilities. Monitoring the end-user experience is done from more 100 locations around the globe and via various wireless carriers.

Dragonfly serves as a seamless substitute for Redis, offering enhanced performance while reducing costs. It is specifically engineered to harness the capabilities of contemporary cloud infrastructure, catering to the data requirements of today’s applications, thereby liberating developers from the constraints posed by conventional in-memory data solutions. Legacy software cannot fully exploit the advantages of modern cloud technology. With its optimization for cloud environments, Dragonfly achieves an impressive 25 times more throughput and reduces snapshotting latency by 12 times compared to older in-memory data solutions like Redis, making it easier to provide the immediate responses that users demand. The traditional single-threaded architecture of Redis leads to high expenses when scaling workloads. In contrast, Dragonfly is significantly more efficient in both computation and memory usage, potentially reducing infrastructure expenses by up to 80%. Initially, Dragonfly scales vertically, only transitioning to clustering when absolutely necessary at a very high scale, which simplifies the operational framework and enhances system reliability. Consequently, developers can focus more on innovation rather than infrastructure management.