Oracle Cloud Infrastructure Alternatives

4.3(36 Ratings)

Considering alternatives to Oracle Cloud Infrastructure? See what this market Oracle Cloud Infrastructure users also considered in their purchasing decision. When evaluating different solutions, potential buyers compare competencies in categories such as evaluation and contracting, integration and deployment, service and support, and specific product capabilities.

Check out real reviews verified by Gartner to see how Oracle Cloud Infrastructure compares to its competitors and find the best software or service for your organization.

Reviewed in Last 12 Months

How Alternatives Are Selected

1. Amazon Bedrock

By Amazon Web Services (AWS)

4.5

Amazon Bedrock delivers a robust and well-integrated solution for accessing multiple AI foundation models through a single API. The seamless integration with the AWS ecosystem is exceptional, and the managed infrastructure eliminates operational overhead. However, the pricing model can be prohibitive for startups or experimentation at scale. Overall, it's an outstanding enterprise solution with significant value for organizations already invested in AWS Read all insights and reviews for Amazon Bedrock

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2. OpenAI API

4.5

As a procurement manager with hands-on experience in enterprise choices and client/agency requirements, OpenAI's API is a nice jump up from the consumer ChatGPT experience. If you need to protect client IP, this is a good purchase in providing a closed-loop environment where user input is not used for model training by default.Read all insights and reviews for OpenAI API

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3. Vertex AI

4.5

Overall, my experience with Vertex AI has been very strong. The platform is mature, well-integrated with the broader Google Cloud ecosystem, and clearly designed for teams that want to move models from experimentation into production with minimal friction. Features like managed training, pipelines, model registry, and monitoring work reliably and save a lot of operational effort. That said, it does come with a learning curve, especially for teams new to GCP, and costs can escalate if usage isn’t carefully managed. Because of that, I wouldn’t call it “truly exceptional” in every scenario, but for organizations already invested in Google Cloud and serious about production-grade ML, Vertex AI delivers an outstanding overall experience.Read all insights and reviews for Vertex AI

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4. Microsoft Foundry

4.2

My experience with Microsoft Foundry has been largely positive. I primarily use it in conjunction with Azure Databricks, which has been a game changer in being able to handle heavy data prep and feature engineering. There is a bit of a learning curve for the interace, but being able to integrate feature stores in the foundry environment and the rest of our Azure data stack has expedited our analytical maturity for sure.Read all insights and reviews for Microsoft Foundry

Where Oracle Cloud Infrastructure Scored Higher

Easier to integrate and deploy

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5. Amazon Q Business

By Amazon Web Services (AWS)

4.4

We've started using Amazon Q to enhance the productivity of our developers and DevOps teams and to deliver high-quality software. It integrates easily with VS Code and provides inline code completion across a wide variety of languages. We are particularly impressed with the chat side bar, which helps us implement new technologies and understand how to integrate them into our existing code base. I would say Amazon Q has increased our delivery rate by more than 40% after 1 year of usage. We expect this to increase as people become more familiar with its usage.Read all insights and reviews for Amazon Q Business

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6. Amazon SageMaker AI

By Amazon Web Services (AWS)

4.4

My overall experience with Amazon SageMaker has been very positive.I especially appreciate how seamlessly it supports the entire machine learning lifecycle from data preparation and model development to training,tuning,deplloymeny,sand monitoring.The integration with AWS services like S3,Lambda,Cloudwatch, and IAM significantly simplifies workflows and makes the environment feel unified and efficient.Read all insights and reviews for Amazon SageMaker AI

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7. IBM watsonx.ai

By IBM

4.3

My experience has been very positive with IBM watsonx.ai. Our primary objective in adopting this platform was to build a scalable AI driven solution that could be deployed reliably in enterprise environments with strong governance and compliance. It's a very powerful and well designed AI solution. It has many functionalities that support both experimentation and production ready workflows. We saw significant productivity gains in how quickly models could be developed and monitored.Read all insights and reviews for IBM watsonx.ai

Where Oracle Cloud Infrastructure Scored Higher

Easier to integrate and deploy
Better evaluation and contracting

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8. Airia

4.4

main reason behind rating airia and what went well using it was it integrates ai agent, secure orchestra and governance with enterprises. it helps to easily roll out ai in business and scale it along the way business grows. and mainly what went well while working with airia is that the auditing and compliances are very well managed under any businesses. Read all insights and reviews for Airia

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