Artificial intelligence is now a strategic priority for enterprise teams. Yet with dozens of cloud services, models, and development approaches available, selecting the right combination has become increasingly complex.
In this guide, we’ll provide a detailed overview of one of the most comprehensive AI ecosystems available: AWS AI services.
AWS AI services are a portfolio of managed AI and machine learning (ML) offerings from Amazon Web Services — including pre-trained AI APIs, generative AI services like Amazon Bedrock, and the SageMaker ML platform — that enable organizations to build, train, and deploy enterprise-grade AI solutions without managing the underlying infrastructure.
Bringing enterprise AWS AI/ML services into production
Enterprise AI adoption is growing. Production value still requires deliberate choices.
88%
of organizations use AI in at least one business function, while 71% regularly use generative AI.
McKinsey, The State of AI in 2025; Stanford HAI, AI Index Report 2026
$37 billion
in enterprise generative AI spending was reported for 2025, compared with $11.5 billion in 2024.
Menlo Ventures, 2025 State of Generative AI in the Enterprise
40%
of enterprise applications are forecast to include task-specific AI agents by the end of 2026, while the cited research reports that approximately 23% of organizations have scaled an agentic AI system into production.
Gartner, 2025–2026 Agentic AI Forecast; McKinsey, Global AI Survey 2025
Investment and adoption are moving quickly. The business case still depends on choosing a suitable use case, architecture, and route to production.
AWS offers one of the most comprehensive portfolios of managed AI and machine learning services. By tapping into its ecosystem, organizations can deliver AI solutions that align with their business goals.
What are AWS AI services?
AWS AI services are a collection of cloud-based AI and ML tools, platforms, and models, available as managed services.
Enterprises can use AWS managed AI services on a pay-as-you-go cloud consumption model, allowing them to leverage industry-leading AI tools without the complexity, cost, or time associated with building AI capabilities in-house. The result is accelerated AI adoption and the ability to scale intelligent applications across the organization.
AWS AI cloud services can be split into four main categories:
- Generative AI services: Services that enable organizations to build applications powered by foundation models, large language models (LLMs), and AI agents.
- Machine learning platforms: Services such as Amazon SageMaker that enable enterprises to develop, train, deploy, and manage custom ML models.
- Pre-trained AI services: A suite of ready-to-use AI capabilities that span common enterprise use cases — computer vision, speech recognition, translation, conversational AI, and more.
- AI infrastructure: Enterprise-grade hardware and cloud infrastructure that helps businesses scale AI workloads.
Whether the objective is deploying generative AI applications, developing custom ML models, or integrating pre-trained AI capabilities into existing systems, the key to success lies in selecting the right technologies for the right use cases.
This is where Intellias AWS AI consulting can help. As an AWS AI Services Competency partner, we work with organizations to deliver production-ready AI solutions that generate measurable business value.
What the AWS AI Services Competency means for buyers
AWS AI services offer access to industry-leading models and tools. But that alone does not guarantee the success of your AI initiative. In addition to great tech, proven expertise and experience are equally important.
This is where the Amazon AWS AI Services Competency certification comes in.
AWS awards this competency to partners that have demonstrated proven technical expertise and successful customer implementations using AWS AI services. Achieving this designation involves a rigorous technical validation process that assesses areas such as architecture, delivery methodology, and customer outcomes.
If you’re looking to work with an AWS AI partner, this competency acts as assurance from AWS itself that a partner has demonstrated its ability to deliver real-world enterprise solutions using the AWS AI ecosystem.
Intellias achieved AWS AI Services Competency in June 2026, joining a select group of AWS partners.
A practical view of AWS AI/ML services, from first use case to production scale
As an AWS AI Services Competency partner, we’ve outlined an extended guide for enterprise teams to get the maximum out of AWS AI/ML services.
Download the guide to get:
- An AWS AI services list as a categorized map
See how prominent services fit across the AWS AI services stack and when each type of capability may be appropriate. - A closer look at AWS generative AI services and AWS agentic AI services
Explore how goal-oriented agents differ from standalone generative AI tools and where they can support multi-step enterprise workflows. - Bedrock vs SageMaker comparison
Compare the platforms by purpose, use case, model approach, and technical requirements. - An outcome-led selection framework
Follow six common enterprise scenarios, including generative AI applications, custom ML models, business process automation, early-stage adoption, compliance-sensitive use cases, and long-term scale. - Proof from production environments
Review examples that connect architecture and service selection with faster validation, lower ownership costs, quicker access to insights, and accelerated campaign planning.
Use our guide to structure early-stage planning, challenge technology assumptions, and align business and technical stakeholders around a clearer route forward.
