Business Software for Google Compute Engine

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.

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.

CloudPanel serves as a complimentary server management interface tailored for PHP, designed specifically for cloud environments to enhance both performance and security. This contemporary control panel simplifies and enriches the experience of managing PHP applications in the cloud. There are no binding contracts, concealed fees, or usage limitations to worry about. It features lightweight components such as NGINX, PHP-FPM, and MySQL to ensure optimal performance. Users can easily obtain free SSL certificates for all domains, which are automatically renewed with just a single click. The platform is equipped with several PHP versions, allowing users to switch or run a different PHP version for their applications effortlessly. It boasts a user-friendly and organized interface, making it efficient to accomplish tasks quickly. Additionally, a command-line tool is available for executing commands related to database backups, restoring, and managing two-factor authentication among other functionalities. With support for more than ten languages, including English, German, Spanish, and Chinese, it caters to a diverse user base. Moreover, the integration of two-factor authentication adds an extra layer of security, ensuring a safe login experience for CloudPanel users. This combination of features makes CloudPanel an excellent choice for developers and administrators looking for a reliable cloud server management solution.

Fidelis Halo, a SaaS-based cloud security platform, automates cloud computing security controls. It also provides compliance across containers, servers, and IaaS within any public, private or hybrid cloud environment. Halo's extensive automation capabilities allow for faster workflows between InfoSec (DevOps) and Halo with over 20,000 pre-configured policies and more than 150 policy templates. These templates cover standards like PCI, CIS and HIPAA. The comprehensive, bidirectional Halo API, SDK, and toolkit automate security and compliance controls in your DevOps toolchain. This allows you to identify and correct critical vulnerabilities before they go into production. Free Halo Cloud Secure edition includes full access to the Halo Cloud Secure CSPM Service for up to 10 cloud service account across any mix of AWS and Azure. Get started now to automate your cloud security journey!

Enhance security across the entire software stack by implementing a cohesive secrets management solution that is accessible to all engineers, from administrators to interns. Storing passwords and API keys directly within source code poses a significant security threat, yet managing these secrets effectively can introduce a level of complexity that complicates deployment processes. Tools like Git, Slack, and email are built to facilitate information sharing, not to safeguard sensitive data. The practice of copy-pasting credentials and relying on a single administrator for access keys does not support the rapid software deployment schedules many teams face today. Furthermore, tracking who accesses which secrets and when can turn compliance audits into a daunting challenge. By removing secrets from the source code and substituting plaintext values with references to those secrets, SecretHub can seamlessly inject the necessary secrets into your application at startup. You can utilize the command-line interface to both encrypt and store these secrets, then simply direct your code to the appropriate location for retrieval. As a result, your code remains devoid of any sensitive information, allowing for unrestricted sharing among team members, which not only enhances collaboration but also boosts overall security. This approach ensures that your development process is both efficient and secure, reducing the risks associated with secret management.

Deploy your machine learning model in the cloud within minutes using a consolidated packaging format that supports both online and offline operations across various platforms. Experience a performance boost with throughput that is 100 times greater than traditional flask-based model servers, achieved through our innovative micro-batching technique. Provide exceptional prediction services that align seamlessly with DevOps practices and integrate effortlessly with widely-used infrastructure tools. The unified deployment format ensures high-performance model serving while incorporating best practices for DevOps. This service utilizes the BERT model, which has been trained with the TensorFlow framework to effectively gauge the sentiment of movie reviews. Our BentoML workflow eliminates the need for DevOps expertise, automating everything from prediction service registration to deployment and endpoint monitoring, all set up effortlessly for your team. This creates a robust environment for managing substantial ML workloads in production. Ensure that all models, deployments, and updates are easily accessible and maintain control over access through SSO, RBAC, client authentication, and detailed auditing logs, thereby enhancing both security and transparency within your operations. With these features, your machine learning deployment process becomes more efficient and manageable than ever before.

Sedai intelligently finds resources, analyzes traffic patterns and learns metric performance. This allows you to manage your production environments continuously without any manual thresholds or human intervention. Sedai's Discovery engine uses an agentless approach to automatically identify everything in your production environments. It intelligently prioritizes your monitoring information. All your cloud accounts are on the same platform. All of your cloud resources can be viewed in one place. Connect your APM tools. Sedai will identify and select the most important metrics. Machine learning intelligently sets thresholds. Sedai is able to see all the changes in your environment. You can view updates and changes and control how the platform manages resources. Sedai's Decision engine makes use of ML to analyze and comprehend data at large scale to simplify the chaos.

Accelerate computational tasks such as those found in machine learning and high-performance computing (HPC) with a diverse array of GPUs suited for various performance levels and budget constraints. With adaptable pricing and customizable machines, you can fine-tune your setup to enhance your workload efficiency. Google Cloud offers high-performance GPUs ideal for machine learning, scientific analyses, and 3D rendering. The selection includes NVIDIA K80, P100, P4, T4, V100, and A100 GPUs, providing a spectrum of computing options tailored to meet different cost and performance requirements. You can effectively balance processor power, memory capacity, high-speed storage, and up to eight GPUs per instance to suit your specific workload needs. Enjoy the advantage of per-second billing, ensuring you only pay for the resources consumed during usage. Leverage GPU capabilities on Google Cloud Platform, where you benefit from cutting-edge storage, networking, and data analytics solutions. Compute Engine allows you to easily integrate GPUs into your virtual machine instances, offering an efficient way to enhance processing power. Explore the potential uses of GPUs and discover the various types of GPU hardware available to elevate your computational projects.

Akto is an open source, instant API security platform that takes only 60 secs to get started. Akto is used by security teams to maintain a continuous inventory of APIs, test APIs for vulnerabilities and find runtime issues. Akto offers tests for all OWASP top 10 and HackerOne Top 10 categories including BOLA, authentication, SSRF, XSS, security configurations, etc. Akto's powerful testing engine runs variety of business logic tests by reading traffic data to understand API traffic pattern leading to reduced false positives. Akto can integrate with multiple traffic sources - Burpsuite, AWS, postman, GCP, gateways, etc.

Google Cloud Pub/Sub offers a robust solution for scalable message delivery, allowing users to choose between pull and push modes. It features auto-scaling and auto-provisioning capabilities that can handle anywhere from zero to hundreds of gigabytes per second seamlessly. Each publisher and subscriber operates with independent quotas and billing, making it easier to manage costs. The platform also facilitates global message routing, which is particularly beneficial for simplifying systems that span multiple regions. High availability is effortlessly achieved through synchronous cross-zone message replication, coupled with per-message receipt tracking for dependable delivery at any scale. With no need for extensive planning, its auto-everything capabilities from the outset ensure that workloads are production-ready immediately. In addition to these features, advanced options like filtering, dead-letter delivery, and exponential backoff are incorporated without compromising scalability, which further streamlines application development. This service provides a swift and dependable method for processing small records at varying volumes, serving as a gateway for both real-time and batch data pipelines that integrate with BigQuery, data lakes, and operational databases. It can also be employed alongside ETL/ELT pipelines within Dataflow, enhancing the overall data processing experience. By leveraging its capabilities, businesses can focus more on innovation rather than infrastructure management.

Real-time monitoring and notification of exceptions: The Error Reporting tool counts, evaluates, and compiles crashes occurring within your cloud services. It features a centralized interface for error management that allows for sorting and filtering of results. A specialized view provides comprehensive error details, including a timeline chart, frequency of occurrences, number of impacted users, first and last occurrence dates, along with a refined exception stack trace. You can also opt to receive alerts via email and mobile for any new errors that arise. Gain a quick understanding of errors: Instantly view your application's most significant or newly identified errors on an intuitive dashboard. Relying on a log stream to pinpoint critical errors can hinder your troubleshooting efforts. Error Reporting delivers processed information directly to you, facilitating a quicker understanding and resolution of root issues. Additionally, this tool enhances your overall incident response efficiency.

Accelerate the development of your deep learning project on Google Cloud: Utilize Deep Learning Containers to swiftly create prototypes within a reliable and uniform environment for your AI applications, encompassing development, testing, and deployment phases. These Docker images are pre-optimized for performance, thoroughly tested for compatibility, and designed for immediate deployment using popular frameworks. By employing Deep Learning Containers, you ensure a cohesive environment throughout the various services offered by Google Cloud, facilitating effortless scaling in the cloud or transitioning from on-premises setups. You also enjoy the versatility of deploying your applications on platforms such as Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm, giving you multiple options to best suit your project's needs. This flexibility not only enhances efficiency but also enables you to adapt quickly to changing project requirements.

Expand your security measures on a global scale by utilizing Google's extensive infrastructure, which alleviates the complexities associated with key management, such as redundancy and latency issues. This approach assists you in meeting compliance mandates while enabling straightforward encryption of your cloud data through software-supported encryption keys, certified FIPS 140-2 Level 3 validated hardware security modules (HSMs), customer-supplied keys, or an External Key Manager. Take advantage of seamless integration with Google Cloud services and employ customer-managed encryption keys (CMEK) to oversee the encryption process across various Google Cloud offerings, all while enhancing your security posture with features like Google Cloud IAM and audit logs. Furthermore, the cloud-based key management service empowers you to handle both symmetric and asymmetric cryptographic keys for your cloud applications in a manner consistent with your on-premises management. You have the capability to generate, utilize, rotate, and destroy a range of cryptographic keys, including AES256, RSA 2048, RSA 3072, RSA 4096, EC P256, and EC P384, ensuring robust data protection tailored to your needs. This comprehensive solution not only enhances data security but also streamlines your operations, allowing your organization to focus on core activities without compromising on safety.

Organizations that choose a do-it-yourself method for implementing a graph database should anticipate a timeline of about 2 to 3 months to achieve a production-ready state. In contrast, with Graph Story’s managed services, your operational database can be set up in just minutes. Discover various graph use cases and explore a side-by-side analysis of self-hosting versus managed services. We can accommodate deployments in your existing infrastructure, whether it's on AWS, Azure, or Google Compute Engine, in any geographical location. If you require VPC peering or IP access restrictions, we can easily adapt to your needs. For those looking to create a proof of concept, initiating a single enterprise graph instance only takes a few clicks. Should you need to scale up to a high-availability, production-ready cluster on demand, we are prepared to assist! Our graph database management tools are designed to simplify your experience, allowing you to monitor CPU, memory, and disk usage effortlessly. You also have access to configurations, logs, and the ability to backup your database and restore snapshots whenever necessary. This level of flexibility ensures that your graph database management aligns perfectly with your operational requirements.

The security and risk management solution for Google Cloud enables you to gain insights into the number of projects you manage, oversee the resources in use, and control the addition or removal of service accounts. This platform helps you detect security misconfigurations and compliance issues within your Google Cloud infrastructure, providing actionable recommendations to address these concerns. It also allows you to identify potential threats targeting your resources through log analysis and utilizes Google's specialized threat intelligence, employing kernel-level instrumentation to pinpoint possible container compromises. In addition, you can monitor your assets in near real-time across various services such as App Engine, BigQuery, Cloud SQL, Cloud Storage, Compute Engine, Cloud Identity and Access Management, and Google Kubernetes Engine. By reviewing historical discovery scans, you can track new, altered, or deleted assets, ensuring a comprehensive understanding of the security posture of your Google Cloud environment. Furthermore, the platform helps detect prevalent web application vulnerabilities, including cross-site scripting and the use of outdated libraries, thereby enhancing your overall security strategy. This proactive approach not only safeguards your assets but also streamlines compliance efforts in an ever-evolving digital landscape.

Assessing the performance of production systems is widely recognized as a challenging task. Efforts to evaluate performance in testing environments often fail to capture the true strain present in a production setting. While micro-benchmarking certain components of your application can sometimes be done, it generally does not reflect the actual workload and behavior of a production system effectively. Continuous profiling of production environments serves as a valuable method for identifying how resources such as CPU and memory are utilized during the service's operation. However, this profiling process introduces its own overhead: to be a viable means of uncovering resource usage patterns, the additional burden must remain minimal. Cloud Profiler emerges as a solution, offering a statistical, low-overhead profiling tool that continuously collects data on CPU usage and memory allocations from your live applications. This tool effectively connects that data back to the specific source code that produced it, allowing for better insights into resource utilization. By utilizing such a profiler, developers can optimize their applications while maintaining system performance.

Quickly set up a virtual machine on Google Cloud for your deep learning project using the Deep Learning VM Image, which simplifies the process of launching a VM with essential AI frameworks on Google Compute Engine. This solution allows you to initiate Compute Engine instances that come equipped with popular libraries such as TensorFlow, PyTorch, and scikit-learn, eliminating concerns over software compatibility. Additionally, you have the flexibility to incorporate Cloud GPU and Cloud TPU support effortlessly. The Deep Learning VM Image is designed to support both the latest and most widely used machine learning frameworks, ensuring you have access to cutting-edge tools like TensorFlow and PyTorch. To enhance the speed of your model training and deployment, these images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers, as well as the Intel® Math Kernel Library. By using this service, you can hit the ground running with all necessary frameworks, libraries, and drivers pre-installed and validated for compatibility. Furthermore, the Deep Learning VM Image provides a smooth notebook experience through its integrated support for JupyterLab, facilitating an efficient workflow for your data science tasks. This combination of features makes it an ideal solution for both beginners and experienced practitioners in the field of machine learning.

The process of cloud migration raises numerous inquiries. Migrate for Compute Engine, a solution by Google Cloud, addresses these concerns effectively. Whether you aim to transfer a single application from your local servers or a thousand high-capacity applications across various data centers, Migrate for Compute Engine empowers IT teams of any size to shift their workloads seamlessly to Google Cloud. Its straightforward “as a service” interface within the Cloud Console, combined with adaptable migration options, simplifies the process, enabling users to significantly reduce the time and effort usually associated with migrations. Say goodbye to complicated setups, intricate configurations, and the confusion of client-side migration tools. By choosing the appropriate migration solution, your team can focus their energy on what truly counts: the successful transfer of workloads to the cloud. Ultimately, this tool not only streamlines the migration process but also enhances overall productivity and efficiency for IT teams.

DevOps Orchestration involves the integration of various tasks throughout the Software Development Life Cycle (SDLC) and Operations, allowing for flexibility in tool and environment selection while centralizing process logic away from specific tools. The Maestro solution offers a variety of plug-ins, pre-built tasks, and templates designed for continuous integration, test automation, release management, enhanced DevOps visibility, automated deployment, and seamless continuous delivery. Regardless of whether your DevOps tools are hosted on-premise or in the cloud—or a combination of both—Maestro facilitates smooth connections between workflow tasks for Continuous Integration and Continuous Deployment. The depicted workflow "CompositionTM" illustrates processes such as a Git checkout, Jenkins build, Sonar code analysis, testing phases, and custom notifications. By eliminating manual tasks from your tailored release workflow, you can establish an accurate and repeatable series of operations that function reliably each time, ensuring consistency and efficiency in your deployments. This approach not only streamlines processes but also empowers teams to focus on innovation rather than repetitive tasks.

Enhance performance by utilizing a scalable, secure, and highly available in-memory service tailored for Redis and Memcached. Memorystore simplifies complex procedures associated with open source Redis and Memcached, such as ensuring high availability, managing failover, conducting patching, and monitoring, allowing developers to focus more on coding. You can begin with the most basic tier and smallest configuration, gradually expanding your instance with minimal disruption. Memorystore for Memcached has the capacity to manage clusters up to 5 TB, delivering millions of queries per second at remarkably low latency. In contrast, Memorystore for Redis instances are designed to be replicated across two zones, offering a service level agreement of 99.9% availability. Continuous monitoring and automatic failover mechanisms ensure that applications face minimal interruptions. You can select from two of the most widely used open source caching solutions to develop your applications. Memorystore provides full protocol compatibility for both Redis and Memcached, enabling you to choose the caching engine that best aligns with your budget and availability needs while maximizing your application's performance. By leveraging these features, developers can significantly improve their operational efficiency.

Centreon is a global provider for business-aware IT monitoring to ensure high performance and continuous operations. The company's AIOps-ready platform, which is holistic and ready for use in today's complex hybrid cloud infrastructures, is designed to meet the needs of these distributed clouds. Centreon monitors all aspects of the IT Infrastructure, from Cloud-to Edge for a clear and comprehensive view. Centreon eliminates blind spots by monitoring all equipment, middleware, and applications that are part modern IT workflows. This includes legacy assets on-premise, private and public clouds, and all the way to edge of the network where smart devices and customers come together to create business value. Centreon is always up-to-date and can support even the most dynamic environments. It has auto-discovery capabilities that allow it to keep track of Software Defined Network (SDN), AWS or Azure cloud assets and Wi-Fi access points, as well as any other component of today’s agile IT infrastructure.

Establish a robust and scalable cloud infrastructure that enables you to exert control and maintain clear organizational structures within your cloud ecosystem. Achieve this by creating distinct organizations, outlining the relationships between users, tenants, and projects, while effectively managing quotas and services. Utilize cloud and virtualization platforms such as Red Hat Enterprise Virtualization, Amazon, and Microsoft Azure for system provisioning, set retirement dates for resources, and scale your environment according to your specific needs. By taking your current operational environment and running various what-if scenarios, you can proactively plan resources and gain ongoing insights into consumption patterns, facilitating detailed chargeback, quotas, and policy development. Enhance your understanding of performance, capacity, and workloads by utilizing SmartState's historical and trend analytics, which provide valuable insights into different facets of your cloud infrastructure. Establish the policy framework for your environment and implement automatic alerts and responses to adapt to changes in your cloud landscape, ensuring continuous optimization and agility. This comprehensive approach not only improves resource management but also fosters a proactive culture of monitoring and adaptability within your organization.

Ternary stands out as the first native FinOps tool designed specifically for optimizing cloud costs within Google Cloud. It empowers users to make informed financial choices, ensuring a culture of accountability, collaboration, and trust between finance and engineering departments. FinOps serves as a framework for overseeing the fluctuating expenses associated with cloud services, incorporating a blend of systems, best practices, and cultural shifts that maximize the value derived from every dollar allocated to the cloud. Ternary is equipped to assist organizations at any phase of their FinOps journey, developing tools that bridge the gap between finance and engineering through features rooted in FinOps principles. This innovative platform provides essential visibility and context, fostering collaboration between teams, while its workflows are designed to promote accountability. By enabling organizations to easily monitor, prioritize, and track cost optimizations to completion, Ternary enhances overall financial management efficiency across the board. As businesses increasingly rely on cloud solutions, Ternary’s role in facilitating effective financial practices becomes ever more critical.

Opal is a cutting-edge security platform designed to help organizations implement least privilege principles while introducing innovative methods for teams to enhance their productivity. We advocate for access that is decentralized, self-service, and seamlessly integrated with existing technologies utilized by your team. By eliminating bottlenecks, we empower teams to delegate access requests to the individuals who possess the most relevant context, resulting in quicker and more informed decisions. With intelligent automation, Opal takes care of the entire access process—granting permissions when they're crucial, sending out automated reminders, and revoking access when it’s no longer necessary. Transparency is key; it’s essential to have clarity regarding who approves access, who holds permissions, the status of requests, and other vital details to avoid the confusion often caused by miscommunication. Many organizations often provide excessive access with a coarse approach that lacks precision and typically lasts indefinitely. Additionally, the majority of companies rely on cumbersome and inconsistent methods to manage just-in-time access, which can hinder operational efficiency. By streamlining this process, Opal ensures organizations can maintain security while empowering their teams to work effectively.