AI as a service: Top AIaaS providers and platforms for business growth

AI as a Service (AIaaS) automates tasks that once took hours — from simple ones like transcribing interviews and automating lead nurturing sequences to complex data classifications.

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Our research shows that two-thirds of marketers use AI worldwide, and nearly three-quarters in the U.S. From creative to operational, AI is reshaping every corner of modern work. Businesses use AIaaS to automate workflows, personalize customer experiences, and gain predictive insights.

In this post, we’ll introduce AI as a Service and 15 providers you can explore today.

Table of Contents

• What is AI as a service?
• Types of AIaaS
• AIaaS Benefits
• Top AIaaS Providers and Platforms
• How to Choose the Right AIaaS Provider
• How Businesses are Leveraging AIaaS for Growth
• FAQs

What is AI as a service?

AI as a Service (AIaaS) is a cloud-based delivery model for artificial intelligence tools. Instead of hiring large data science teams or building infrastructure from scratch, businesses plug into ready-made AI models and tools on demand. Use cases can surface image and video analysis, chatbots and customer support, visual recognition, fraud detection, or managing ML models.

How does AIaaS work?

AIaaS runs on scalable cloud infrastructure, where providers host pre-trained and customizable models for machine learning, natural language processing, computer vision, and predictive analytics. Businesses access these models in two main ways:

• APIs let teams send data to the provider’s system and receive outputs such as predictions, classifications, or recommendations in real time.
• SDKs (Software Development Kits) give developers pre-built libraries and tools to integrate AI directly into their applications.

The provider’s cloud infrastructure powers these services with GPUs and TPUs, so companies don’t need to invest in costly hardware. Meanwhile, model management covers ongoing training, updates, and optimization, so users always work with accurate and up-to-date models.

AIaaS vs. Traditional AI

Unlike building AI from scratch, an AI platform as a service provides ready-made infrastructure and pre-trained models hosted in the cloud.

Let’s look at the two approaches side by side:

AI as a Service (AIaaS)	Traditional AI
Cloud-based, no hardware needed	On-premise, high hardware costs
APIs/SDKs for quick access	Built from scratch in-house
Pay-as-you-go pricing	Large upfront investment
Pre-trained + customizable models	Custom models only
Scales instantly	Scaling is limited by the infrastructure/staff
Fast deployment (hours–days)	Long cycles (months–years)
Risk free	Comes with high risks during the dev process

Types of AIaaS

Types of AIaaS solutions include AI agents, machine learning frameworks, cognitive computing APIs, and AI-powered analytics. Understanding them helps companies see where AI can fit into their workflows before evaluating specific providers.

1. Bots and AI Agents

Bots and AI agents focus on automating conversations and tasks through intelligent agents. They can interact with customers, answer questions, or perform actions based on natural language prompts. Beyond customer service, AI agents are increasingly popular for internal workflows.

2. Machine Learning Frameworks

Machine learning frameworks give businesses the tools to deploy custom models for deep learning, classical ML algorithms, or neural networks.

ML frameworks typically include features for data preparation, model selection, and monitoring. Instead of building complex infrastructure, businesses use these frameworks to speed up innovation and apply predictive models to real problems.

3. Cognitive Computing APIs

Cognitive APIs provide plug-and-play capabilities for specific AI tasks like understanding language or translation. They’re built for easy integration, so businesses can add advanced AI to their products without needing deep expertise.

4. AI-Powered Analytics

Analytics-driven AIaaS platforms focus on extracting insights from data. They combine traditional business intelligence with machine learning to uncover patterns, forecast outcomes, and support decision-making. This service helps organizations shift from basic reporting to predictive and prescriptive analytics that guide strategy.

AIaaS Benefits

The benefits of AIaaS are cost-effectiveness, scalability, rapid deployment, and easy integration. In addition, businesses gain better accessibility and reduced time to market.

Let’s break down each advantage.

Cost-effectiveness

AIaaS reduces the need for specialized AI talent and upfront investment. It removes costs by using subscription or pay-as-you-go pricing.

For instance, Forethought reduced ML costs up to 80% on Amazon SageMaker. Observe.AI cut costs 50%+ after optimizing SageMaker workloads, and EagleView reports 40–50% savings post-migration. The positive effect is so huge that mature businesses can’t sleep on AIaaS.

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Scalability

Auto-scale training and inference globally without provisioning hardware is another benefit.

Cloud spending trends show why AIaaS scales so well. For instance, IDC found that shared cloud infrastructure spending jumped 44% year-over-year in Q1 2024, now making up over half of all infrastructure spend.

Businesses are moving to shared resources because they can expand or shrink capacity instantly — the same elasticity that makes AIaaS practical at any size.

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Accessibility

AIaaS makes advanced tools available beyond technical teams. McKinsey reports that more than three-quarters of organizations now use AI in at least one business function.

Companies retrain staff, create new AI-related roles, and use APIs and SDKs to lower the barrier to entry. These services make AI accessible to both small teams and large enterprises.

Reduced Time to Market

Teams measure AI success by speed: 64% track productivity and 55% track time saved, which is exactly what AIaaS delivers. It cuts development cycles from weeks to hours and accelerates time to market.

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For example, Google Cloud’s Vertex AI has shown document processing times drop from a full week to less than two hours, while Observe.AI reports similar gains, reducing model work from a week to just a few hours.

Lower Risk

AIaaS enables businesses to access AI capabilities without building in-house infrastructure. That reduces risk by shifting security, uptime, patching, and model operations to the provider. The pay-as-you-go model also limits financial exposure while testing ROI.

A 2025 Google Cloud report notes that many organizations now prefer provider-managed AI infrastructure specifically to cut risks around security and system reliability.

With that, let’s explore the 15 best AIaaS solutions on the market for different business needs.

Top AIaaS Providers and Platforms

AIaaS providers include Microsoft Azure, Google Cloud, AWS, IBM, HubSpot, and more. They don’t fall into the same categories. The list below is grouped into four main types to make examples clearer.

Cloud AI Platforms

Cloud AI platforms are cloud-based environments that provide tools to build, train, and deploy AI models. They include:

• Data storage.
• Machine learning frameworks.
• And prebuilt AI services like vision, language, and speech.

Let’s go through the five best tools in this category.

1. Google Cloud AI — for natural language, vision, and machine learning.

Google Cloud AI offers a suite of AI and ML services that cover natural language, vision, and machine learning. It lets businesses integrate AI directly into analytics, applications, and workflows. Features include:

• Vertex AI, an end-to-end platform for training, deploying, and managing ML models.
• AutoML, which provides custom model training with minimal coding.
• BigQuery ML, machine learning directly integrated into SQL-based analytics.
• Vision API, or image and video analysis, including object detection and facial recognition.
• Dialogflow, conversational AI interfaces such as chatbots and virtual assistants.

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Pricing: Pay-as-you-go, with free tiers for many APIs. Vertex AI charges for compute hours and storage, while Vision API starts around $1.50 per 1,000 units.

Teams can use Google Cloud’s pricing calculator to get a custom price for each use case.

What I like: The combination of AutoML and BigQuery ML makes it easier for mid-sized teams to adopt AI without hiring a lot of data science staff.

Case Study: P&G

Procter & Gamble (P&G) has been using Google Cloud to deliver more personalized consumer experiences. With AI services, they can:

• Help shoppers find the right products in local stores and on their preferred channels.
• Store and analyze massive amounts of brand and marketing data.
• Use BigQuery to create unified consumer profiles and build omnichannel journeys.

This tech also powers personalized products like Lumi by Pampers, which tracks baby sleep and diaper changes, and the Oral-B iO toothbrush with real-time feedback to improve brushing.

2. AWS AI — for machine learning, vision, speech, and natural language.

Amazon Web Services provides one of the largest AIaaS portfolios, covering machine learning, vision, speech, and natural language. Features include:

• Amazon SageMaker, which offers full ML lifecycle management, from training to deployment.
• Amazon Rekognition, which provides image and video analysis with features like object detection and face recognition.
• Amazon Comprehend, which does natural language processing for sentiment, key phrases, and classification.
• Amazon Lex, a conversational AI for building chatbots and voice assistants.
• Amazon Polly, a text-to-speech service that turns written text into lifelike speech.

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Pricing: Usage-based. SageMaker charges for instance hours, storage, and data transfer. Rekognition starts at $1 per 1,000 images, while Polly is about $4 per 1 million characters. Free tiers are available.

AWS also offers a flexible pricing model and a calculator.

What I like: AWS AI stands out for its scale and breadth. No other provider offers as many pre-trained APIs and managed services under one platform.

3. Microsoft Azure AI — for machine learning and cognitive APIs.

Azure AI combines machine learning, cognitive APIs, and developer tools into one ecosystem. Beyond that, Azure adds intelligence to applications and creates digital models of real-world environments. Offerings include:

• Azure Machine Learning, a platform for building, training, and deploying ML models.
• Azure Cognitive Services, APIs for vision, speech, and natural language tasks.
• Azure AI Search, semantic and vector search across enterprise data.
• Azure Digital Twins, digital models for physical environments and IoT.
• Azure Spatial Anchors, immersive mixed-reality experiences.

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Pricing: Usage-based and product-based. Cognitive Services start at about $1.50 per 1,000 text records. ML services are billed per compute and storage hour. Estimate your expenses with the Azure pricing calculator.

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What I like: Azure feels easy to work with if you already use Microsoft products like 365 or Dynamics, since the integrations are built-in.

Case Study: TomTom’s Digital Cockpit

Most people know TomTom for its early GPS devices — the go-to tech for getting from point A to B. Today, the company is reinventing itself with Azure-powered AI.

TomTom built the Digital Cockpit — an in-car infotainment system that automakers can fully customize. Drivers can interact with their cars directly, without relying on a smartphone.

The results are impressive: the product team shrank from 10 people to just 3, while query response times dropped from 12 seconds to 2.5 seconds.

4. Alibaba Cloud — for ecommerce vision, speech, natural language, and analytics.

Alibaba Cloud focuses heavily on AI services for ecommerce, logistics, and large-scale enterprise operations. It supports vision, speech, natural language, and analytics, with a strong presence in the Asia-Pacific region. Features include:

• Machine learning platform for AI (PAI), a drag-and-drop ML environment for training and deployment.
• Image search and recognition, a visual AI for product search, quality inspection, and surveillance.
• Natural language processing, or tools for text classification, sentiment analysis, and translation.
• Speech recognition and synthesis, or real-time transcription and voice services.
• Intelligent analytics, predictive insights for supply chain, finance, and manufacturing.

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Pricing: Pay-as-you-go with region-based pricing. NLP services start at roughly $0.015 per 1,000 characters; image search and vision APIs start at about $0.002 per image. Free usage quotas are included in most services.

Use the dynamic pricing calculator to get the final quote.

What I like: Localized solutions and cost efficiency, making it a strong option for companies operating in China and APAC.

5. Oracle Cloud Infrastructure (OCI) — for image recognition and anomaly detection.

Oracle Cloud Infrastructure extends Oracle’s enterprise software into AIaaS, offering tools for natural language processing, image recognition, and automated model training. It’s built for enterprises that need both high performance and hybrid deployment. Features include:

• OCI Language, which includes sentiment analysis, entity extraction, and text classification.
• OCI Vision, or image recognition and document analysis.
• OCI Anomaly Detection, which offers real-time detection of unusual data patterns.
• OCI Data Science, a collaborative environment for ML training and deployment.
• AutoML, for automated model building and optimization.

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Pricing: Image analysis starts at about $0.25 per 1,000 images. Other AI services follow the same per-request pricing model.

Estimate your cost with Oracle’s OCI services pricing calculator.

What I like: Its integration with Oracle databases and ERP systems, making it simple for existing Oracle customers to add AI without migrating away from core infrastructure.

Machine Learning Platforms and Automation

Machine learning platforms and automation are services that let teams develop, train, and manage models while automating routine tasks. They speed up workflows and reduce the need for manual intervention in the ML lifecycle.

Here are four of the best from this category.

6. BigML — for predictive modeling.

BigML is a cloud-based machine learning platform designed to make advanced modeling accessible to businesses and researchers. It supports supervised and unsupervised learning through a web interface and API. Offerings include:

• Classification and regression, or predictive modeling from structured data.
• Anomaly detection, that provides identification of unusual data patterns.
• Clustering and topic modeling, which groups of datasets and text for analysis.
• Time series forecasting, which offers trend prediction based on historical data.
• REST API that allows integration of ML models into applications.

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Pricing: A 14-day free tier is available for small datasets up to 64MB. Paid subscriptions start at around $30/month, scaling with dataset size and task limits. Private deployments begin at approximately $55,000/year.

Get your cost estimate with the BigML calculator.

What I like: The clear interface and automation options make BigML one of the easiest ML platforms to adopt.

7. Altair RapidMiner — for modeling and deployment in a single environment.

Altair RapidMiner is a data science and machine learning platform that combines data preparation, modeling, and deployment in a single environment. This solution is a good fit for organizations that need end-to-end data science with automation and visual workflows. Altair RapidMiner includes:

• Data engineering, which transforms and prepares datasets.
• Model building, which allows teams to create machine learning and deep learning models.
• MLOps, which offers deployment and monitoring of models in production.
• Visual analytics, a drag-and-drop workflow design with visualization.

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Pricing: RapidMiner offers a 30-day free version for almost all its solutions with limited functionality. Pricing is undisclosed — contact their sales team for a custom quote.

What I like: The visual interface lowers the entry barrier for analysts, while still supporting complex enterprise deployments.

Case Study: Fraud Detection

Healthcare fraud is notoriously hard to spot for patients, providers, and businesses. According to CIOCoverage, one healthcare company turned to the Altair RapidMiner platform to scale data collection and flag fraud more effectively.

The results: a $20 million fraud case was uncovered. The system now helps the company detect and prioritize high-risk cases, cut inspection time, monitor fraud patterns, and prevent future fraud.

8. H2O.ai — for automated feature engineering.

H2O.ai is an open-source and enterprise machine learning platform focused on automation and scalability for data science teams. Core features include:

• Driverless AI, an automated feature for engineering, model training, and interpretability.
• H2O-3, an open-source library for generalized linear models, random forests, GBMs, and deep learning.
• AutoML, which enables automatic algorithm selection and tuning.
• AI Cloud, a platform for deploying and scaling ML models.
• AI AppStore, which has pre-built applications for common business use cases.

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Pricing: H2O-3 is open-source and free to use. Driverless AI and AI Cloud are available under enterprise licenses, with pricing customized by deployment size (commonly starting in the tens of thousands per year).

What I like: It includes a feature store with metadata, drift detection, and automatic recommendations, which helps with collaboration, governance, and tracking of feature life cycles.

9. DataRobot — for modeling, deployment, and governance.

DataRobot is an enterprise AI platform focused on automating the full lifecycle of predictive and generative AI. It provides tools for modeling, deployment, governance, and observability across environments. DataRobot’s features include:

• Predictive modeling, which provides support for supervised and unsupervised learning.
• Time series modeling, that includes multiseries and segmented forecasting, nowcasting, and time-aware partitioning.

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Pricing: Public pricing is not listed in detail on the official site; enterprise licensing and custom quotes are required.

What I like: The strong focus on Prediction Explanations (SHAP/XEMP) and robust model monitoring tools gives confidence when deploying AI in production.

Conversational AI and Business Applications

Conversational AI and business applications are tools that power chatbots, virtual assistants, and customer support agents using natural language processing. These tools help businesses handle inquiries and improve customer interactions at scale.

Let’s go through the four leading options in this category.

10. IBM Watson — for domain-specific modeling.

IBM Watson is a suite of AI services designed for enterprise applications. Its strengths lie in conversational AI, domain-specific modeling, and understanding language. IBM Watson is often used in healthcare, finance, and law, where customization is critical. Key features include:

• Watson Assistant, a conversational AI for chatbots and virtual agents.
• Watson Discovery, a document and knowledge search with NLP.
• Watson Knowledge Studio, a domain-specific model training.
• Watson Speech Services, which offers speech-to-text and text-to-speech APIs.

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Pricing: Undisclosed. Every product comes with a tailored fit pricing model. Book a 30-min call with their sales rep to discuss your options.

What I like: The ability to train Watson on domain-specific data makes it far more adaptable than many “one-size-fits-all” conversational AI service providers.

Case Study: LegalMation

Legal work often comes with mountains of paperwork. LegalMation, a suite of AI tools for lawyers, partnered with IBM Watson to cut down on time spent drafting early-phase legal documents.

With help from subject matter experts, the team used Watson Knowledge Studio and Watson Natural Language Understanding to build a domain-specific model trained on legal language and concepts.

The impact was huge: drafting time dropped from 6 to 10 hours to under 2 minutes, and costs fell by roughly 80%.

11. HubSpot Breeze AI — for automating sales, marketing, and customer success workflows.

Breeze is HubSpot’s collection of AIaaS tools integrated directly into its CRM platform. It combines assistants, agents, and embedded AI features to help teams automate customer service tasks. Breeze’s service features include:

• Breeze Assistant, a personal AI companion that connects to CRM data to support meeting prep, content creation, and analysis.
• Breeze Agents, which allows teams to automatically match knowledge base articles with support tickets.
• Breeze Studio, a no-code environment to customize and deploy agents tailored to specific business needs.

Pricing: Get started with Breeze Assistant and select Breeze features for free in HubSpot. Breeze Agents and advanced AI capabilities are available in premium editions of HubSpot’s software.

What I like: Versatility and ease of use of Breeze Agents and workflows for end-to-end customer experience. Start chatting with a bot and describe the goal you want to reach. From there, Breeze can process data and offer suggestions.

Case Study: Breeze’s Customer Agent in Action

Little Help Agency adopted Breeze’s Customer Agent to automate routine support queries. The agent streamlined responses, reduced manual workload, and allowed the support team to focus on technical cases.

Moreover, We Are Girls Club relied on Breeze’s Content Agent to accelerate campaign execution. Tasks that previously took one to two weeks with multiple people were reduced to minutes. According to Jen Spencer, the company’s vice president, the shift has enabled faster launches and stronger conversion rates.

Try it out for free.

12. ServiceNow AI — for HR and customer service automation.

ServiceNow embeds AI across its Now Platform to automate workflows in IT, HR, and customer service. Its focus is on productivity, case resolution, and predictive insights. Key features include:

• Now Assist, a generative AI for case summaries, chat replies, and knowledge articles.
• Virtual Agent, a fully conversational AI for employee and customer support.
• Performance analytics in a forecasting and trend dashboard.

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Pricing: AI features are bundled into ServiceNow platform editions. Add-ons like Now Assist for Customer Service start at $125/agent/month (billed annually). Other AI features are priced by module.

What I like: Virtual Agent plus Now Assist make IT, HR, and support ticket handling far faster without needing extra tools.

Case Study: Coca-Cola Hellenic Bottling Company (CCHBC)

CCHBC, one of the world’s largest bottling companies, upgraded its IT operations and employee experience by moving from a legacy system to the ServiceNow platform. The old setup made it difficult to track issues, automate processes, and deliver a smooth employee experience.

With ServiceNow’s AI-powered solutions, the company saw major gains:

• 20% increase in project efficiency.
• Critical incidents resolved in three-and-a-half to four hours on average.
• 150,000 hours returned to employees.

This shift shows how AI can transform IT from a slow, ticket-heavy process into a faster, more streamlined system.

13. OpenAI — for image generation, reasoning, and semantic search.

OpenAI provides advanced generative AI models accessible through APIs, including GPT for text, DALL·E for images, and Whisper for speech. Developers use these models to build applications for content creation, code generation, and conversational interfaces. Offerings include:

• GPT-5 and GPT-5 mini, large language models for text, reasoning, and chat.
• DALL·E 3, which provides image generation from text prompts.
• Whisper, which offers automatic speech recognition and transcription.
• Embeddings API for semantic search, classification, and recommendation.
• Codex, a code generation and natural language to code conversion.

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Pricing: GPT-5 starts at $1.250/1M tokens. GPT-5 mini is cheaper at $0.250/1M tokens. DALL·E image generation starts at $0.04 per image, Whisper at $0.006 per minute.

OpenAI offers a public listing of all models with their pricing and customization options.

What I like: The breadth of models — from text to images to speech — makes it the most versatile AIaaS provider for building new applications.

Some Companies That Use OpenAI Models

• Stripe leverages GPT-4 to streamline the user experience and combat fraud.
• Jasper AI‘s engine combines a cross-section of the best models out there. The company uses OpenAI’s GPT-4, Anthropic, and Google's models to enrich the generated text with recent search data and mimic your brand voice.
• Duolingo uses GPT-4 for conversation practice and contextual feedback on mistakes.

Multimodal and Speech AI Platforms

Multimodal and speech AI platforms tools to process unstructured data across images, video, text, and audio. These solutions cover computer vision, transcription, sentiment detection, and entity recognition. As a result, businesses can analyze and automate information from multiple sources.

Here are two notable providers.

14. Clarifai — for computer vision and audio recognition.

Clarifai is an AI platform focused on computer vision, NLP, and audio recognition, providing pre-trained and custom models through its API. This solution supports large-scale classification, detection, and search tasks. Key features include:

• Vision AI, or image and video classification, face detection, or OCR.
• NLP AI, which offers sentiment analysis, entity recognition, text summarization.
• Audio AI, or speech-to-text and sound classification.
• Model Builder, which provides low-code tools to train custom models.
• Workflows that offer drag-and-drop pipelines for combining multiple models.

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Pricing: Clarifai offers a free tier with 1,000 operations/month. Paid plans start at $30/month for 10,000 operations, with enterprise pricing for higher workloads.

What I like: The workflow builder feels practical. A pipeline chaining OCR with text classification is noticeably faster than setting up separate models via API calls.

15. AssemblyAI — for speech intelligence.

AssemblyAI is an API-first platform for speech intelligence, providing transcription, speech-to-text, and audio analysis for developers. It focuses on turning unstructured audio into structured, searchable data. Notable AssemblyAI features include:

• Speech-to-text, which provides high-accuracy transcription with punctuation and formatting.
• Speaker diarization that separates different speakers in multi-voice audio.
• Sentiment analysis, which detection of emotions within conversations.
• Entity detection that recognizes names, dates, and key terms.
• Content moderation, which flags sensitive or inappropriate speech.

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Pricing: Transcription starts at $0.00025 per second (~$0.90 per audio hour). Advanced features like sentiment analysis and entity detection are billed separately per second of audio.

What I like: Support for word-level timestamps and confidence scores makes downstream analytics like call summarization or QA scoring much more reliable.

How to Choose the Right AIaaS Provider

Picking the right AIaaS provider means finding the perfect fit for each team’s long-term goals. Here are five factors to weigh:

1. Use case fit. Draft a list of use cases where the team needs AIaaS. Then, make sure the provider covers core objectives, whether that’s for vision, speech, analytics, or end-to-end automation.
2. Integration and compatibility. Check how easily the platform connects with the existing tech stack (CRM, data warehouse, cloud environment). Smooth integration reduces setup time.
3. Scalability and performance. Look at benchmarks and case studies to see if the provider can handle your data size, latency requirements, and future growth. Talk to their expert and take notes for each provider — compare.
4. Pricing and transparency. Compare pricing tiers carefully. Some charge per API call, others per seat or compute usage. Ask their team to create an explicit pricing breakdown. Transparent costs prevent surprise bills.
5. Security and compliance. Verify that the provider meets your industry standards (GDPR, HIPAA, SOC 2). Strong governance is non-negotiable for sensitive data.

How Businesses are Leveraging AIaaS for Growth

Coca-Cola

Coca-Cola's vending machines powered with AI analytics and its collaboration with ChatGPT and OpenAI are straightforward examples of how businesses can be creative with AIaaS.

Want to hear this story? I found it fascinating.

Coca-Cola and AI Vending Machines

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Coca-Cola Bottlers Japan (CCBJ), Asia's leading Coca-Cola bottler, turned to data analytics to optimize product distribution in its 700,000 vending machines across the region. They developed a predictive model to determine optimal vending machine locations, the right product lineup within each machine, pricing strategy, and expected sales volume.

In doing so, Coca-Cola utilized Google's Vertex AI, BigQuery analytics data warehouse, and AutoML for tabular data. This AIaaS implementation highlighted how machine learning could drive operational efficiency.

Coca-Cola and OpenAI

Coca-Cola pioneered a partnership with OpenAI, utilizing its DALL-E2 model and ChatGPT for innovative marketing activities. That included its AI-powered “Masterpiece” campaign.

The campaign wove together iconic artworks from different eras, narrating the journey of a Coca-Cola bottle as it travels to a student seeking inspiration. This amalgamation of live-action shots, digital effects, and AI was created by Electric Theatre Collective's VFX team and the creative agency Blitzworks.

Perhutani

Perhutani, a state-owned forest management company in Indonesia, oversees around 1.3 million hectares of forest across Java and Madura.

Their problem was managing such a large, remote, and diverse area with limited staff.

To address this, Perhutani partnered with Alibaba Cloud to build Perhutani Digital Forest. They used generative AI, geospatial data tools, real-time analytics, and cloud infrastructure. They also deployed large language models (Qwen series, Wan vision-editing series), tools for data integration, and applications that can work offline in remote areas.

This partnership improved Perhutani’s ability to monitor deforestation risks and illegal logging. The Digital Forest project also kicked off the digitalization within the company to map carbon dioxide sequestration and oxygen production.

Trivago

An online travel agency was drowning in user-generated photos. Every day, travelers uploaded massive volumes of images that could inspire other users, showcase destinations, and drive bookings. But those photos weren’t organized or searchable.

To fix this, the agency turned to Clarifai, using its computer vision and machine learning platform. Clarifai’s AI could automatically detect landmarks, categorize destinations, and tag travel-related features across millions of photos. This transformed raw, unstructured visual data into something the agency could use to improve discovery and personalization.

Within one month, the company was able to tailor images to a traveler’s specific needs. That personalization led to a 10% reduction in visitor bounce rates and a 15% increase in conversion rates.

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FAQs

1. Is ChatGPT an example of AI as a Service?

Yes, ChatGPT is delivered through APIs and web apps, making it a clear example of artificial intelligence as a service. Instead of building and training large language models from scratch, businesses can plug into OpenAI’s infrastructure and use it instantly.

HubSpot’s Breeze AI works the same way. It’s an AIaaS offering embedded directly into the CRM, giving teams assistants, agents, and content tools without needing to manage the underlying models.

2. What’s the difference between AI as a Service (AIaaS) and Software as a Service (SaaS)?

AIaaS provides access to cloud-based machine learning models and APIs. SaaS delivers complete applications. For instance, HubSpot CRM is SaaS. Meanwhile, Breeze AI is its AIaaS layer that adds automation and intelligence to the core platform.

3. How much does AI as a Service typically cost?

Most AIaaS platforms charge per API call, operation, or compute usage. Pricing often starts with free tiers and scales into enterprise contracts. Breeze AI is included within HubSpot’s existing CRM pricing, removing the need for separate AI contracts.

4. Do I need technical expertise to use AI as a Service?

No, AIaaS users do not need to be technical experts to use the product. Many AI service providers offer low-code or no-code tools. Take Breeze AI. It’s designed for non-technical users, embedding AI features like content creation and reporting directly into HubSpot workflows.

5. What are the security considerations for using AI as a Service?

AI service providers must comply with standards such as GDPR and SOC 2. Sensitive data must be protected through encryption and compliance with laws like GDPR or HIPAA.

AIaaS: Key Points to Remember

AI as a Service (AIaaS) is a cloud-based model that delivers artificial intelligence tools and capabilities — such as machine learning, natural language processing, and predictive analytics. These capabilities are often delivered via APIs or web interfaces, eliminating the need for in-house expertise or infrastructure. Key benefits include:

• Rapid deployment.
• Scalability.
• Cost-effectiveness.
• And easy integration with existing business systems.

To get started, assess the business’ needs, compare provider capabilities, and explore real-world use cases to identify the best fit.

Editor's note: This post was originally published in July 2023 and has been updated for comprehensiveness.