Google Cloud Platform (GCP) is a powerful cloud provider, yet many businesses and tech teams are seeking alternatives in 2025\. According to [studies](https://www.consulting.ca/news/3699/over-half-of-firms-wasting-significant-it-spend-on-cloud-inefficiencies), cloud pricing complexity is a pain point for over 50% of IT leaders, with nearly half reporting significant wasted spend. While GCP holds a solid third place in market share, industry leaders Amazon Web Services and Microsoft Azure command roughly 30% and 20% respectively (compared to GCP’s \~10%). This competitive landscape drives companies to explore other platforms that better align with their budgets, technical needs, or regional requirements. In this article, we will discuss the ten best alternatives to GCP (Google Cloud Platform) for businesses, startups, and AI/ML teams in 2025\. Let’s get started: ## Key Factors in Choosing a GCP Alternative When weighing cloud platforms, savvy teams evaluate both technical capabilities and business considerations. Here are 10 key factors to compare GCP alternatives on: 1. **Look for pay-as-you-go rates,** discounts (reserved/spot instances), free tiers or credits, and lower data egress fees. Cost optimization tools and inclusive bandwidth can be big perks. 2. **Consider number of regions** and availability zones – to ensure low-latency access, faster communication and low redundancy. 3. **Always go for a Cloud company providing** **autoscaling features** for both VMs and containers, and support for modern orchestration (Kubernetes, serverless) are essential for handling growth. 4. **Check if there’s an availability of advanced services like managed databases**, data analytics, and AI/ML toolkits. For example, dedicated AI platforms can greatly benefit machine learning 5. **A simpler interface or developer-friendly tools** (CLI, one-click apps, templates) can boost productivity. Some clouds focus on minimizing ops overhead so developers can deploy quickly without deep cloud expertise 6. **How well the platform integrates with popular tools** and supports multi-cloud or hybrid setups. Support for open standards, APIs, and third-party services can prevent lock-in. Compatibility with on-premise systems matters for enterprise use. 7. **Security & Compliance features** like encryption, IAM, firewall, and DDoS protection are essential. Enterprises may require providers with SOC2, ISO27001, HIPAA, or local data residency compliance. Rich security toolsets and best-practice guidance are a plus. 8. **Reliability & Support guarantees** through uptime SLAs and track record of service availability. Redundancy across availability zones for high availability is important. Quality of technical support can greatly impact user experience. 9. **Global Reach & Regional Strengths** should match where your business operates. Some providers excel in specific regions or offer "sovereign cloud" options for data locality. Global CDN or edge networks can improve content delivery. 10. **Feature Breadth vs. Simplicity** depends on your needs \- whether you want a broad suite of 200+ services or a targeted set of core services done well. Align the platform's philosophy with your project's requirements. With these criteria in mind, let’s explore the leading GCP alternatives and see how they stack up. ## 10 Google Cloud Platform Alternatives ### 1\. RunPod.io – Cloud GPU Power for AI/ML ![][image1] *Via [RunPod.io](https://RunPod.io)* RunPod is a cloud platform purpose-built for AI and machine learning workloads. It offers affordable, on-demand access to GPUs. It’s ideal for startups, researchers, or ML teams who need powerful GPU instances or serverless AI endpoints without managing complex infrastructure. For anyone experimenting with [AI image/video generation](https://blog.runpod.io/generate-images-with-stable-diffusion-on-runpod/) or training models but lacking high-end hardware, RunPod has become a go-to solution. #### RunPod Key Features: * **RunPod lets you spin up GPU-powered containers** in seconds across 30+ global regions * **Provides 50+ pre-built machine** learning templates for quick setup. * **RunPod’s serverless GPU service scales pods** from 0 to N based on demand, so you only pay when your model is processing requests. * **Users can bring their Docker containers** and let RunPod handle the ops, including logging, monitoring, and fast cold-starts (their “Flashboot” tech can spin up GPUs in under 250ms). * **High-performance NVIDIA GPUs from consumer RTX cards** up to enterprise A100, H100, and even [AMD MI250/MI300](https://blog.runpod.io/amd-mi300x-vs-nvidia-h100-sxm-performance-comparison-on-mixtral-8x7b-inference/) are available, paired with NVMe storage and up to 100 Gbps networking for data-heavy tasks. #### RunPod Limitations & User Feedback: * RunPod is focused on compute – particularly GPUs and containers. #### RunPod Pricing: * A single NVIDIA A100 [80GB GPU is around $1.64–$1.89/hour on RunPod](https://www.runpod.io/pricing) * An RTX A4000 starts at \~$0.32/hour * Small CPU-only VMs are also competitively priced (e.g. 4 vCPU/8 GB for \~$86/month) ### 2\. Microsoft Azure – Enterprise-Ready Cloud with Broad Services ![][image2] *Via [Azure](https://www.googleadservices.com/pagead/aclk?sa=L&ai=DChcSEwjkk7y6iJuMAxXdjGgJHYFtDW0YABAAGgJ3Zg&ae=2&aspm=1&co=1&ase=5&gclid=CjwKCAjwnPS-BhBxEiwAZjMF0oDkcmAe11i8KFRSqdUgNflY3gQxdKpG9Ar_JIpJnSLiE3DT5f6-wxoC8TQQAvD_BwE&ohost=www.google.com&cid=CAESVuD2kDPbGMypYplz9JFL4R53zhv6rPQCIXelMfbICuTLr2dyHPrqJc5w7fK2IIqwl-kf1ctv46bhsd_mrb5FKxpKqNSwA6dpfJgf1Kut4exqDN1OTZ7C&sig=AOD64_3ny7Qu_tl4Vt4BmwAuQSJ3kvs35A&q&adurl&ved=2ahUKEwivsre6iJuMAxX7Q6QEHST0EY0Q0Qx6BAgJEAE)* Microsoft Azure is a top-tier alternative to GCP, well-suited for enterprises and organizations that require a comprehensive set of cloud services, global scale, and tight integration with Microsoft’s software ecosystem. If your workloads run heavily on Windows Server, .NET, or you already use tools like Active Directory and Office 365, Azure provides a seamless environment. It’s also a leader in hybrid cloud deployments, allowing businesses to span on-premises data centers and the cloud (via Azure Stack) with ease. In 2025, Azure has also distinguished itself in the AI space through its partnership with OpenAI – making it a great choice for companies looking to leverage cutting-edge AI models under an enterprise umbrella. #### Azure Key Features: * Azure offers a wide range of services—from basic VMs/storage to advanced analytics, IoT, and machine learning—similar to AWS. * Developers benefit from an integrated environment with Visual Studio, Azure DevOps, and GitHub. * It emphasizes strong security, compliance, and identity management via Azure Active Directory. * With over 60 global regions, it ensures applications are hosted close to users. * Hybrid features like Azure Arc and ExpressRoute enable smooth on-prem integration. * Its AI/ML tools, including Azure Machine Learning and Azure OpenAI Service, provide access to advanced models like GPT‑4/4.5. #### Azure Limitations: * Scalable and flexible, easily handling growing workloads while offering a unified experience for developers using Microsoft tools. * Top-notch security and compliance with over 100 global certifications. * Seamless integration with enterprise software such as Office, Dynamics, and Windows. * A global network and extensive data center presence that ensure high availability and low latency worldwide. * Recognized as a powerhouse cloud that often matches or exceeds GCP in enterprise and AI capabilities. #### Azure Pricing: * **NV Series (Visualization):** For example, an NV6 instance (1× NVIDIA Tesla M60) is priced at around $0.95 per hour (on-demand, East US region). * **NC Series (Compute):** NC6 instances (1× NVIDIA Tesla K80/V100) run roughly between $0.90–$1.20 per hour, depending on the generation and region. * **ND Series (Deep Learning):** An ND40rs\_v2 instance (8× NVIDIA Tesla V100) is approximately $12.24 per hour in the East US 2 region. ### 3\. Amazon Web Services (AWS) – The All-In-One Cloud Leader ![][image3] Via [AWS](https://aws.amazon.com) AWS is the market leader in cloud computing and a natural alternative for anyone considering leaving GCP. It’s best for organizations that want the **widest range of cloud services**, superb global infrastructure, and a mature ecosystem with a huge community. AWS is often the default choice for large-scale cloud deployments and has a track record of serving everything from nimble startups to Fortune 500 companies. If you need a cloud platform that can do it all – and have the in-house expertise to manage it – AWS’s breadth and depth are unmatched. #### AWS Key Features: * **AWS provides over 200 cloud services** across compute, storage, database, networking, machine learning, and analytics. * **It offers both core services (like EC2, S3, RDS, Lambda**) and niche options (robotics APIs, satellite ground stations, quantum computing testbeds). * **With 33 regions and 100+ Availability Zones**, AWS ensures low latency, high availability, and multi-region disaster recovery. * **Continuous innovation is evident through new instance types** (e.g., Graviton ARM-based CPUs) and advanced AI services (Amazon Bedrock, GPU families including NVIDIA H100, custom AI chips). * **Extensive networking solutions such as AWS Global Accelerator** and CloudFront CDN minimize latency for global applications. #### AWS Limitations: * AWS’s vast service scope results in a steep learning curve, leaving new users feeling overwhelmed and prone to misconfigurations. * Management complexity arises from juggling multiple consoles and tools (web, CLI, and service-specific interfaces). * The flexible pricing model can lead to unexpectedly high costs without strict cost monitoring and governance. * Heavy reliance on AWS-specific services raises concerns about vendor lock-in. #### AWS Pricing: * AWS p3.2xlarge (1× NVIDIA V100, 16GB) costs about **$3.06 per hour** in the US East region. * AWS p3.16xlarge (8× NVIDIA V100, 16GB each) is priced roughly at **$24.48 per hour**. * AWS p4d.24xlarge (8× NVIDIA A100, 40GB each) runs for approximately **$32.77 per hour**. * AWS p5.48xlarge (8× NVIDIA H100, 80GB each) costs around **$98.32 per hour**. ### 4\. Alibaba Cloud – Cost-Effective Global Cloud (with an Asia Focus) ![][image4] *Via [Alibaba Cloud](https://www.alibabacloud.com/)* Alibaba Cloud (also known as Aliyun) is a leading cloud provider particularly strong in Asia-Pacific. It’s the top cloud platform in China and has expanded to many international regions as well. Businesses choose Alibaba Cloud as a GCP alternative for its **competitive pricing** and to better serve users in China or Southeast Asia (where Alibaba’s network can offer lower latency and compliance with local regulations). It’s a solid choice for startups and enterprises looking for a full-featured cloud that might be more cost-effective than AWS/Azure/GCP, especially if operating in markets where Alibaba has infrastructure. #### Alibaba Cloud Key Features: * **Offers a broad range of cloud services similar to AWS/GCP**—including compute (ECS), storage (OSS), databases, big data, AI services, and CDN—with additional proprietary solutions like the Hanguang AI accelerator for inference. * **Boasts robust big data platforms (MaxCompute, DataWorks)** that power massive data processing, leveraging Alibaba’s strong e-commerce background. * **Operates in 24+ regions with over 70 availability zones worldwide** (spanning Mainland China, Hong Kong, Singapore, Japan, Europe, the Middle East, and emerging markets like Mexico). * **Provides a rich ecosystem in China with integrated services** (e.g., Alipay, mini-program support) and tailored regulatory compliance tools (e.g., ICP license management). #### Alibaba Cloud Limitations: * Some Western companies express concerns over data storage with a Chinese provider, driven more by trust/policy issues than technical shortcomings. * Outside of Asia, its ecosystem and support network are smaller—with fewer data centers and limited third-party integrations compared to AWS or Azure. * Documentation quality may be inconsistent in non-Chinese languages, with some reviews noting gaps in languages like Dutch or Spanish. * Certain advanced services and partner marketplaces are not as mature as those of the major Western cloud providers. * Support quality can vary; while enterprise-level support is robust, smaller customers sometimes report language barriers or slower response times. #### Alibaba Cloud Pricing: * General-purpose VM instances are highly affordable, with on-demand prices around **$0.011 per hour** and spot instances as low as **$0.003 per hour**. . * OSS storage costs are competitive—standard storage is approximately **$0.021/GB**, while archive storage is around **$0.0025/GB**. ### 5\. Tencent Cloud – Scalable Cloud with China & Gaming Edge ![][image5] *Via [Tencent Cloud](https://www.tencentcloud.com/)* Tencent Cloud is another major Asia-based provider, part of tech giant Tencent. It is best for businesses that want strong infrastructure in China and Southeast Asia, especially for industries like gaming, social media, or mobile applications. It’s a top choice for companies looking to deploy services for Chinese users – if not using Alibaba, Tencent Cloud is the other local heavyweight. Beyond that, Tencent Cloud offers globally available services and can be an alternative to GCP for general workloads, often at **aggressive pricing**. It’s also known for its solutions tailored to media streaming and online games (low-latency networking, chat, live video, etc.), making it popular among game developers and entertainment platforms. #### Tencent Cloud Key Features: * Comprehensive services: IaaS (CVM), PaaS (Kubernetes, serverless), and GPU instances for AI. * High-speed private backbone with full ISP coverage in China. * 99.95% availability with multi-AZ redundancy and advanced DDoS protection. * Operates in \~21 regions with 50+ AZs (US, Europe, India, Thailand, etc.). * Deep integration with Tencent ecosystem (WeChat, fintech, AI services). #### Tencent Cloud Limitations: * Smaller international presence and fewer third-party integrations. * English documentation and support are less mature than AWS/Azure. * Complex setups (e.g., custom networking, NoSQL clusters) can be challenging. * Global compliance certifications are still evolving. #### Tencent Cloud Pricing: * Aggressive, flexible pricing (pay-as-you-go, subscriptions). * Standard 2 CPU/4 GB VM: \~$18–$20/month. * Outbound bandwidth: \~$0.08/GB. * Frequent promotions and free trials for added savings. ## **6\. Huawei Cloud – Secure Cloud with Global Ambitions** ![][image6] *Via [Huawei Cloud](https://www.huaweicloud.com/intl/en-us/)* Huawei Cloud is a fast-growing alternative, backed by Huawei’s telecom and enterprise technology expertise. It’s best for organizations that prioritize security, strong support, and a broad global presence beyond the traditional Western cloud regions. Huawei Cloud has made inroads in Asia, Africa, and Latin America, making it attractive for businesses operating in emerging markets or those that want to avoid dependency on US-based providers. Enterprises that value a **trusted, enterprise-grade product experience with excellent after-sales support** find Huawei Cloud appealing. It’s also a contender in the AI/ML space, given Huawei’s development of its own AI chips (Ascend series) and AI platform (ModelArts), which are integrated into their cloud. #### Huawei Cloud Key Features: * Full-spectrum services: ECS (compute), diverse storage (block, OBS, backup), managed databases, big data, and AI offerings. * Robust security out of the box with advanced WAF, DDoS mitigation, and encryption, plus best-practice hardening templates. * Developer-friendly ecosystem with a dedicated Developer Center and active support for open-source tools. * Global reach via the “KooVerse” network across 30+ regions and 80+ AZs (including emerging markets like South Africa, Brazil, Saudi Arabia) with 100+ compliance certifications. * Unique integrations: enterprise container platform, proprietary databases, and Ascend-powered AI chip instances for cost-efficient AI workloads. #### Huawei Cloud Limitations: * Limited presence in North America and fewer third-party integrations compared to AWS. * Some advanced features have a learning curve and offer limited customization. * Support response times can be slower for smaller customers; geopolitical factors may influence adoption. #### Huawei Cloud Pricing: * Compute: A 2 vCPU/8GB ECS costs about **$0.04/hour** on pay-per-use, dropping to around **$0.03/hour** on prepaid plans. * Storage: OBS standard at roughly **$0.022/GB** and archive at about **$0.002/GB**. * Data transfer: Outbound traffic priced near **$0.08/GB**. ### 7\. E2E Cloud – Budget-Friendly Cloud for Startups (India-Based) ![][image7] *Via E2E Cloud* E2E Cloud is an Indian provider that offers cloud infrastructure at very affordable rates. It’s best for startups, small-to-mid businesses, and AI enthusiasts who need **cost-effective compute (including GPUs)** with easy setup. If GCP (or AWS/Azure) costs are busting your budget, E2E Cloud positions itself as a lower-cost alternative that still delivers solid performance. #### E2E Cloud Key Features: * Offers core cloud services (virtual servers, block & object storage, load balancers) with a strong focus on GPU instances using top NVIDIA GPUs (H100, H200, A100, L40S) for AI/ML. * Provides a simple, developer-friendly platform with a clean web console and robust API automation. * Advertises “100% predictable pricing” with bundled resource packages and flexible billing (hourly or fixed monthly). * Data centers in India with recent US expansion, emphasizing high performance, customization, and “sovereign cloud” compliance for Indian data. #### E2E Cloud Limitations: * Smaller service catalog; fewer advanced offerings (e.g., IoT suites, advanced data warehousing) compared to GCP/AWS. * Geographic presence is mainly India (with limited US coverage), limiting global multi-region deployments. * Some users note occasional stability issues and room for improvement in support responsiveness. #### E2E Cloud Pricing: * Basic 2 vCPU/4GB VM: around ₹750–900 per month (\~$10–$12). * Popular 4 vCPU/8GB plan: roughly ₹1500–1800 per month (\~$18–$22). * GPU instances (e.g., NVIDIA A100 40GB) at approximately $1.50 per hour—significantly lower than competitors. * Bundled plans often include SSD storage and generous data transfer; intra-region bandwidth is free. ### 8\. Utho Cloud – Simplified Cloud with Low-Cost Plans (India’s Linode) ![][image8] *Via [Utho Cloud](https://utho.com/)* Utho Cloud is branded as “India’s first cloud platform” and presents itself as a home-grown alternative to Linode or DigitalOcean. It’s best for businesses and developers who want simple, no-frills cloud VMs, storage, and networking at significantly lower prices than GCP. Utho is particularly suited for small businesses, SaaS startups, and developers who prioritize **cost savings (up to 60% less)** and straightforward service over the vast complexity of hyperscalers. With data centers in India and beyond, it appeals to those targeting the Indian market and nearby regions with minimal latency. Essentially, if you want to “break free from vendor lock-in, slow speed, high cost, and complexity” – Utho’s value proposition aligns with that, focusing on simplicity and customer-centric support. #### Utho Cloud Key Features: * **Core cloud services** (VMs with shared/dedicated vCPUs, block/object storage, load balancers, GPU instances) tailored for web and backend apps. * **Similar to DigitalOcean/Linode**—simple, with essential features and no overwhelming managed services. * **Operates 7+ data centers globally**, with strong focus in India ensuring low latency for local users. * **Developer-friendly with a clean API**, web dashboard, SSH key provisioning, snapshots/backups, and pre-configured app images. * **Emphasizes customer-centric support** (in English and Hindi) with a proven track record among 22,000+ customers. #### Utho Cloud Limitations: * Fewer advanced managed services (like IoT or data warehousing) and a smaller global footprint—primarily India-focused—which may affect latency for users outside India. #### Utho Cloud Pricing: * Basic 1 CPU/1GB VM plans start around **$5–$6/month**. * Example package: 2 CPU, 1GB RAM, 25GB storage, 1TB bandwidth for about **$18/month**. * GPU instances (e.g., NVIDIA A100 40GB) at approximately **$1.50/hour**—significantly lower than comparable offerings. ### 9\. Linode (Akamai Connected Cloud) – Developer-Friendly Cloud with Flat Pricing ![][image9] ***Via [Lindode](https://www.linode.com/pricing/#:~:text=Cloud%20Computing%20Services%20,pricing%20consistent%20across%20data%20centers)*** Linode – now known as Akamai Connected Cloud after its acquisition – has long been a favorite for developers and SMBs who need reliable VMs and straightforward pricing. It’s best for those who want a **simple, stable, and well-supported cloud environment** without the complexity of GCP. If you’re running standard workloads like web servers, databases, or small-scale microservices, and you want to avoid deciphering complex pricing schemes, Linode is an excellent alternative. It’s particularly strong for small startups, hobby projects, and even production systems that require consistent performance at lower cost. #### Linode Key Features: * **Provides virtual Linux servers** (and limited Windows via Akamai) in plans from low-end Nanodes (1 CPU, 1GB) to high-memory/dedicated CPU instances. * **Offers complementary services**: block storage, S3‑compatible object storage, load balancers (NodeBalancers), managed Kubernetes (LKE), and managed databases. * **Developer-focused with an intuitive cloud manager**, full-featured CLI, and well-documented API. * **Flat, predictable pricing with generous** included outbound bandwidth and free static IPs/DNS management. * **Leverages Akamai’s global edge network**, operating in 11 core data center regions across North America, Europe, and Asia‑Pacific. #### Linode Strengths & Limitations: * Limited advanced managed services (e.g., data analytics, proprietary ML), fewer enterprise-grade networking features, and max instance sizes may not suit extremely large-scale deployments. #### Linode Pricing: * Nanode plan starts at **$5/month** (1 CPU, 1GB RAM). * Popular 4GB plan is **$20/month** for 2 vCPUs, 4GB RAM, 80GB SSD, and 4TB outbound transfer. * Object storage is **$5/month** for 250GB with 1TB outbound included. * Pricing is flat and predictable, billed hourly up to a monthly cap with no hidden fees. ### 10\. Heroku – PaaS for Developer Productivity and Rapid Deployment ![][image10] *Via [Heroku](https://www.heroku.com/)* Heroku is a Platform-as-a-Service (PaaS) rather than a traditional IaaS, and it’s best for developers and startups who want to **focus on code, not infrastructure**. If GCP feels too hands-on or you lack a dedicated ops team, Heroku can be an attractive alternative – it abstracts away servers, networking, and OS maintenance. Heroku is particularly beloved in the Ruby on Rails community and for any team that values quick deployments and easy scaling of web applications. It’s great for prototype-to-production flows, hackathons, or small teams that need to deploy **web apps, APIs, or background workers with minimal DevOps overhead**. #### Heroku Key Features: * **Git-Push Deployment:** Simplified app deployment by pushing code to Heroku; it builds and runs apps in lightweight containers called dynos. * **Multi-Language Support:** Native support for popular languages including Node.js, Python, Ruby, Java, Go, PHP, and more, through buildpacks. * **Automated App Management:** Manages scaling, logging, orchestration, and releases automatically, reducing the need for manual infrastructure management. * **Add-Ons Marketplace:** One-click integration for databases (Heroku Postgres, Redis), caching, monitoring, and other third-party services. * **CI/CD Integration:** Seamless integration with GitHub and continuous delivery tools, enabling pipelines and review apps for rapid development cycles. * **Managed Infrastructure:** Runs on AWS, offering reliable data center performance and security, while Heroku handles OS patching and system maintenance. * **Developer-Friendly Tools:** Provides a clean web dashboard, robust CLI, and well-documented API for easy app management and scaling. #### Heroku Limitations: * **High Cost at Scale:** Dyno pricing becomes very expensive as apps grow, often exceeding the cost of comparable raw VMs. * **Performance Constraints:** Limited memory and control may not suit large, complex, or resource-intensive applications. * **Limited Region Options:** Primarily available in US/EU, which can lead to higher latency for users in other regions. * **Slower Innovation:** Fewer major new features in recent years, potentially driving some teams to explore alternative PaaS options. #### Heroku Pricing: * **Eco Dynos:** $5 per month for 1,000 dyno hours (ideal for testing and non-critical apps; dynos sleep after inactivity). * **Basic Dynos:** Approximately $7 per month per dyno; always on, suited for small production apps. * **Standard Dynos:** Standard-1X at $25/month and Standard-2X at $50/month per dyno; supports horizontal scaling. * **Performance Dynos:** Range from about $250 to $1,500 per month per dyno for higher RAM and dedicated CPU; includes autoscaling. **Wrapping Up: Best Google Cloud Platform Examples** The cloud landscape in 2025 is more dynamic and diverse than ever. While Google Cloud Platform remains a strong contender, its complexity, pricing structure, and specific limitations have led many teams to explore other options. From enterprise giants like AWS and Azure to specialized platforms like RunPod, the market offers a wide range of alternatives tailored to different use cases—whether you need affordable GPU power for AI workloads, regional infrastructure for global compliance, or simplified environments for rapid app deployment. Each provider in this list brings unique strengths to the table, and the best choice ultimately depends on your team’s priorities—cost, performance, simplicity, support, or specialization. We encourage you to compare providers using the key factors outlined earlier and consider how each aligns with your technical and strategic goals. This article was created by the team at RunPod, where we’re passionate about helping developers and researchers get faster access to the compute they need—without breaking the bank. While we’re proud of what we offer, we also believe in transparency and giving users a clear view of their options. If [RunPod](https://www.runpod.io/) feels like a fit for your AI or machine learning workloads, we’d love to have you try it out. If not, we hope this guide helps you make an informed choice. Happy building. [image1]: [image2]: [image3]: [image4]: [image5]: [image6]: [image7]: [image8]: [image9]: [image10]:

Top 10 Google Cloud Platform Alternatives in 2025