How to choose the best cloud platform for AI
June 19, 2026 9 min read 49 views
Cloud is a home to Artificial Intelligence.
Choosing a cloud platform for AI means matching GPU availability, storage throughput, and compliance coverage to your specific workload — before the first model trains. According to McKinsey’s report, 72% of organizations now use AI in at least one business function, up from 55% in 2023 and 20% in 2017. That’s why decision-makers often grapple with multiple challenges:
- Which cloud providers align best with our AI ambitions?
- What cloud strategies will yield the most value?
- How can we make sure our choices remain economically viable in a constantly changing tech ecosystem?
This article dives into the heart of these questions. We’ll explore the nuances of different providers, examine key considerations for AI workloads, and share insights on how to balance performance with cost-effectiveness.
What to look for in a cloud AI platform: 5 key factors
As we discussed in our previous article, five key factors will impact your strategy in choosing a cloud AI platform: computational power, data storage, security and compliance, AI and ML ecosystem, and interoperability.
Computational power stands at the forefront of these considerations. AI workloads, particularly in areas such as deep learning and large language models, require substantial computing resources. The chosen cloud provider should offer access to high-performance GPUs and specialized AI accelerators. A provider with a diverse range of instance types, from general-purpose to AI-optimized, can accommodate various stages of AI development and deployment.
Closely tied to computational resources is the aspect of data storage and management. AI systems thrive on data, often requiring vast amounts of it for training and inference. The suitable cloud provider has robust and scalable storage solutions that can efficiently handle large datasets, including high data transfer speeds that can substantially impact AI workflow performance. The cloud platform should also offer tools for data preprocessing, labeling, and version control, crucial for maintaining data quality and reproducibility in AI projects.
Compliance and security form another critical pillar in the decision-making process. Since AI systems often deal with sensitive or proprietary data, the cloud provider should demonstrate strong security measures and compliance with relevant regulations. This includes encryption at rest and in transit, identity and access management, and adherence to standards such as GDPR, HIPAA, or industry-specific regulations. The provider’s track record in handling security incidents and transparency in communicating potential vulnerabilities are also important factors to consider.
The AI and Machine Learning ecosystem offered by the cloud provider is another key differentiator. This encompasses the range of pre-built AI services, Machine Learning frameworks, and development tools available on the platform. A rich ecosystem can accelerate AI development and deployment, containing everything from automated Machine Learning platforms to pre-trained models for common tasks. The provider’s commitment to the latest AI advancements and ability to integrate cutting-edge technologies can give enterprises a competitive edge.
Finally, interoperability and vendor lock-in are concerns that warrant serious attention. While it’s tempting to fully embrace a single provider’s ecosystem, this approach can lead to dependencies that are difficult and costly long-term. Companies need to evaluate a cloud provider’s support for open standards, containerization technologies, and hybrid or multi-cloud deployments. This flexibility allows businesses to distribute their AI workloads across diverse environments and facilitates easier migration when needed.
Comparing AI cloud platforms with AI Cloud Companion
Evaluating cloud platforms for AI can involve comparing dozens of services, infrastructure options, and pricing models across multiple providers. AI Cloud Companion simplifies this process by helping organizations test AI workloads against different cloud environments and identify the platforms best suited to their requirements.
AI Cloud Companion supports projects that work with image, text, audio, video, and multimodal data. It also covers generative AI use cases, including content generation, code generation, synthetic data creation, and image, video, and audio generation. The following sections explore the capabilities available through the platform.
Image processing
Our team works with projects on image processing, which encompasses face, text, or object detection. This can be typically used in autonomous vehicles, medical imaging, retail inventory management, quality control in manufacturing, and more. We can test for you which cloud platform will respond well to your requirements, for example, in situations where image data has low quality or there is partial visibility of an object of interest.
Document AI
With AI Cloud Companion, your organization can extract information from unstructured documents. The document AI capabilities include optical character recognition (OCR), key-value pair extraction, and tabular data extraction.
OCR converts text from images or scanned documents into machine-readable text. This allows you to process and analyze textual content that was previously inaccessible to computers.
Key-value pair extraction identifies and extracts key-value pairs from documents, such as names and addresses, email addresses, and phone numbers. For structured documents like invoices or forms, AI can find and extract key-value pairs, such as “Amount: $100,” etc.
Another important capability is tabular data extraction, which identifies and extracts data from tables and forms. This data can be later integrated into databases or other systems for analysis and reporting.
Video processing
Video processing involves the analysis and extraction of information from video data. AI-powered video processing can be used for purposes like human and object tracking. For example, in sports analytics, AI can track athletes’ movements for performance analysis. In autonomous vehicles, human and object tracking can be used for safe navigation around obstacles.
Text processing
AI Cloud Companion also supports several text processing capabilities, starting with sentiment analysis. This implies identifying and classifying the emotional tone of the text, such as positive, negative, or neutral (see Fig. 1). It’s particularly useful for understanding customer feedback or social media sentiment.
The next one is key phrase extraction. As a way to pinpoint the most important or relevant phrases within a text, it is helpful for summarizing lengthy documents, extracting keywords for search engine optimization, or categorizing content.
Last but not least, there is named entity recognition within Avenga’s AI Cloud Companion, a tool that classifies entities in a text, such as products, events, locations, organizations, etc. It’s essential for information extraction and knowledge graph construction and can also be used in question-answering systems.
Speech processing
AI Cloud Companion is also proficient in speech processing, a field that refers to the analysis and understanding of spoken language. One of the critical sub-branches within speech processing is speech-to-text. Speech-to-text technology converts spoken words into written text and stands behind transcription services, virtual assistants, and voice-controlled devices.
Avenga helps companies work with various datasets and estimate which AI model and cloud platform would be most suitable for their requirements. Factors such as the size and complexity of the dataset, desired accuracy, and language variety are essential in this estimation.
Generative AI
Companies experiment with integration of generative AI into their businesses in a quest for more productivity gains and accelerated digital transformation. You can use our AI Cloud Companion as a starting point for creating efficient and accurate generative AI tools. Our expertise and guidance can help you avoid common pitfalls and achieve faster results in generative AI adoption.
How AI Cloud Companion accelerates AI adoption
AI Cloud Companion accelerates AI adoption by helping organizations evaluate cloud platforms, identify suitable AI services, and validate use cases before deployment. This reduces the time, cost, and complexity associated with cloud provider selection and AI implementation.
AI adoption has become a critical differentiator for organizations. Companies often cannot afford lengthy evaluation periods or unsuccessful implementation attempts. AI Cloud Companion addresses this challenge by shortening the path from initial concept to deployment, helping organizations implement AI solutions more efficiently.
The platform’s ability to reduce time-to-market for AI initiatives delivers measurable business value. Instead of spending months evaluating different cloud providers and their AI capabilities, organizations can quickly identify and implement the solutions best suited to their needs. This rapid deployment capability, combined with data analysis features across various formats, enables faster and more informed decision-making.
Beyond time savings, AI Cloud Companion helps reduce research and implementation costs. Organizations can avoid costly trial-and-error approaches to cloud provider selection and AI model implementation. The platform’s guidance helps companies navigate common AI adoption challenges, particularly in complex scenarios such as multi-cloud strategies and generative AI projects.
AI Cloud Companion also supports digital transformation initiatives. It provides a structured path for AI implementation across business functions, including automated document processing and advanced video analytics. Organizations can deploy AI solutions across multiple departments, from routine administrative tasks to quality control and operational monitoring.
FAQ
Closing remarks
AI cloud services provide businesses with an opportunity to experiment with AI adoption at different scales, using cloud computing and AI capabilities across various workloads. While offerings from cloud AI platforms and AI cloud providers may seem similar, differences in AI infrastructure, scalability, and service depth can impact project outcomes.
To make an informed decision, businesses should evaluate AI workloads, required control, scalability needs, budget constraints, and the benefits of AI in their use cases. This helps identify suitable cloud AI solutions for long-term goals.
Or, organizations can lean on Avenga’s Cloud and test their AI hypothesis in a controlled environment before making a final commitment.
Use our AI Cloud Companion to explore alternative options and make an informed decision about the best cloud provider for AI capabilities: contact us.
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