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Evaluating AIaaS providers: 6-point criteria for success
Is your organization pursuing innovative AI deployments that consistently achieve organizational goals and compliance? Consider these criteria for selecting an AIaaS provider.
The technical advantages that AI as a service offers enterprises of all sizes are undeniable. But that doesn't mean every AIaaS provider meets the needs of every organization.
Organizations are adopting AI as a service (AIaaS) to offset the steep costs of on-premises deployments and IT skillset challenges while gaining AI-driven operational and business advantages. With platform-based GPUs, businesses can create custom models using proprietary data without costly infrastructure investments.
According to Grand View Research, the global AIaaS market was valued at approximately $16.08 billion in 2024 and is expected to grow at a CAGR of 36.1% from 2025 to 2030 to reach $105.04 billion.
However, successful AI deployments require synergy between the provider's platform capabilities and an organization's unique requirements. These elements extend to AI specialization, data compatibility, model updates, security protections and scalability. By evaluating a potential AIaaS provider based on specific criteria, businesses can ensure successful alignment with their AI deployment goals.
Why choose AIaaS?
AIaaS provides affordable and seamless cloud-based access to prebuilt data models, analytics tools, integrative APIs and 24/7 support channels. AIaaS enables users to experiment and pay only for essential compute and deployment resources, preventing exorbitant costs and delays related to AI hardware procurement. Organizations can also avoid the IT skillset challenges posed by AI and free up resources to explore other business and IT initiatives.
Consider the following business use cases:
- E-commerce. These businesses can adopt AI-powered image recognition for product recommendations and searches using computer vision, which recognizes visual cues and automates processes.
- Security. An AIaaS platform employs unique intrusion detection capabilities to recognize patterns and anomalies, perform real-time network analysis and dynamically adapt to infrastructure changes.
- Customer service. Organizations can train AI chatbots to respond to customer service requests across industries. These services require natural language processing (NLP) capabilities and rely heavily on data ingestion for model training and text data preparation for creating NLP algorithms.
To ensure success in AI adoptions, IT leaders need to define implementation goals with clear adoption strategies that accurately assess the compatibility between the provider's capabilities and the client infrastructure. By partnering with providers that offer ongoing support, updates and training, a business can innovate its AI deployments and consistently achieve desired goals in functionality and efficiency over time.
Like providers, client businesses focus on growth and expansion, so it's essential that an AIaaS provider demonstrates scalability at will. Moreover, features such as APIs are important for enabling different software applications and systems to interact and relay information. Finally, organizations need an AIaaS provider that can provide effective security and compliance features (i.e., role-based access control, zero trust, data encryption and cloud security posture management).
How to choose the right AIaaS provider
In general, AI implementations must focus on the following:
Vendor support for the above three points will ensure consistency and minimize disruptions as IT teams undertake each AI development stage. As organizations assess the feasibility of platform adoption, administrators and IT leaders also need to understand AI's disruptive potential for employees. To counter those effects, investments in AI training and upskilling can help offset workforce resistance and establish buy-in.
Use the following six criteria as a guide for evaluating an AIaaS provider:
1. Deployment goals
Planning and goals for AIaaS deployments are essential to avoid crippling drawbacks, like cost overruns or project failures. While AIaaS providers handle the burden of daily maintenance, model training and upgrades, IT teams need to access their provider's workload management software to fine-tune and monitor performance or restart workloads whenever necessary. By defining AIaaS project goals, enterprises can select an AIaaS provider that supports their AI deployments.
2. Compatibility
Compatibility is the AIaaS vendor's ability to provide data cleansing, data labeling, validations and compliance capabilities, matching an organization's technical requirements with the vendor's expertise and areas of specialization. It also ensures that the AIaaS platform supports the subscriber's data formats and volume. Moreover, if the data is sensitive, the provider should meet the regulatory compliance standards of the organization.
3. Integration
Seamless integrations between a subscriber and the vendor's platform facilitate smooth AI implementations without disrupting workflows. These integrations should extend to CRM, Extensible Authentication Protocol and multi-cloud deployments. Access to APIs further enables a business to integrate proprietary systems and tools with AI capabilities while requiring minimal IT resources.
4. Customization
An AIaaS provider should offer capabilities to tailor services and deliver data in their client's required formats. In addition to offering customization options for AI deployments across private, public and hybrid clouds, vendors should provide data labeling to ensure accurate, tailored data sets for training AI models.
5. Security
Organizations must consider robust security capabilities, such as end-to-end encryption, multifactor authentication and regular security updates when they select an AIaaS provider. The vendor should provide transparency so that subscribers can control their data lifecycle with thorough knowledge of where sensitive proprietary information resides.
6. Scalability
An AIaaS provider with scalable services offers the ability to expand an AI deployment as the business grows without requiring extensive rework or cost overruns for the subscriber. Moreover, with the right service-level agreement in place, an organization can ensure that the provider will have the necessary compute power and resources to meet service escalation as the subscriber's resource demands expand.
Top AIaaS providers
Major cloud providers now offer large portfolios of AI and ML services. These offerings can span from specialized pre-built APIs to comprehensive model development platforms, empowering organizations of all sizes. Popular capabilities include foundation model access, generative AI, agentic systems and traditional machine learning frameworks.
Take a quick look at some of the major providers' top AI services:
- AWS. Popular services include Rekognition, Comprehend, Lex, Amazon SageMaker AI, Amazon Bedrock and Amazon Q Developer.
- Microsoft Azure. Popular services include OpenAI Service, AI Foundry, AI Agent Service, AI Bot Service, AI Language, AI Search and AI Studio.
- Google Cloud. Popular services include Vertex AI, Gemini API, BigQuery ML, Natural Language API, Agent Assist and Dialogflow.
There are also more specialized options, such as the following in alphabetical order:
- IBM watsonx. IBM's portfolio of AI products provides various capabilities such as an AI developer studio, open data lakehouse for analytics, lifecycle governance for AI, AI assistants, as well as orchestration for AI assistants and agents.
- OpenAI. Largely known for ChatGPT, it can help developers build AI-powered applications for text generation, code completion, image creation, AI research, chatbots and more.
- SAS Viya. This cloud-native platform for artificial intelligence, analytics and data management offers purpose-built AI options, including agentic AI and pre-built models for specific industry needs. It is popular with industries such as banking, healthcare and retail.
Kerry Doyle writes about technology for a variety of publications and platforms. His current focus is on issues relevant to IT and enterprise leaders across a range of topics, from nanotech and cloud to distributed services and AI.
