Today’s AI organizations are racing to deliver innovative solutions. That means many are choosing their stack based on what gets them up and running fastest, with the least amount of effort. But when it comes to selecting their data management layer, whether it's simply a vector database or also a graph database, there’s another factor that teams should consider, and that’s whether or not their data tools are cloud-agnostic.

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Benefits of Cloud-Agnostic Design Choices

Vector and graph databases have been growing rapidly due to their role in generative AI applications. While it might seem convenient to utilize your existing cloud provider’s database solutions such as AWS’ OpenSearch or Neptune, GCP’s BigQuery, Azure CosmosDB, this decision can have long-term implications as your business scales. This post explores four compelling reasons why building your advanced AI applications with a cloud-agnostic database from the outset is a wise choice.
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Minimize Costs 

As your company's data infrastructure evolves, the optimal distribution of workload and storage for cost efficiency will also change. For instance, a company may start with AWS for its general-purpose cloud services but later switch to GCP for its more cost-effective compute-optimized solutions.

Consider an early-stage fintech startup that initially finds AWS to be the most cost-effective for reaching their first $1MM in ARR. As the company grows and starts handling hundreds of terabytes of data, it may make financial sense to switch providers or even consider private deployments. Additionally, for startups, many cloud providers offer startup credit programs (like this one for GCP), which can significantly reduce costs by leveraging free credits across different providers.
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Hybrid Cloud to Avoid Migration Costs

Sometimes companies grow through acquisitions, with each acquired organization potentially bringing massive scale of data but on different cloud providers. It is hard to justify the cost of consolidation in such cases and it might be preferable to add an indexing layer on top to help redirect queries to the right cloud. Such situations require the indexing layer to be able to handle hybrid clouds and index data easily without further creating siloes or too many indirections.
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Facilitate Easy Data Movement

There are scenarios where engineers need to run workflows on local machines, such as for hybrid workloads involving hardware components. In other cases, building proof-of-concept models locally before transitioning to a cloud provider for training can be beneficial. Ensuring your code is machine- and cloud-agnostic facilitates these workflows.
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Ensure Access to GPU Supply

The global GPU shortage in 2023 highlighted the importance of diversifying cloud usage. Just as the COVID-19 pandemic led many industries to diversify their supply chains, AI companies dependent on GPU resources for critical workflows have been pushed to spread their cloud usage across multiple providers. Ensuring reliable GPU access means that your infrastructure must be flexible enough to tap into resources from various cloud providers.
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Future-Proof Your Business with a Cloud-Agnostic Multimodal Database

In today’s technological landscape, there are no excuses for modern databases not to be cloud-agnostic. Advances in containerization have made it possible for database providers to package dependencies and run seamlessly across different cloud platforms. As companies increasingly adopt multi-cloud strategies, a cloud-agnostic database becomes essential for maintaining flexibility in an unpredictable AI landscape.

At ApertureData, we built our database to be cloud-agnostic from the start. While achieving cloud agnosticism requires significant effort and investment, often at the expense of developing core features, the long-term benefits for consumers are invaluable. For more insights into our approach, check out our blog post, “Lessons Learned Building a Cloud-Agnostic Database.”

The AI race is still in its early stages, and the future is uncertain. However, for many AI organizations in large and small companies, adopting a flexible cloud strategy and choosing a cloud-agnostic database are crucial steps towards ensuring resilience and scalability in a rapidly evolving industry.

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If you’re interested in learning more about how ApertureDB works, reach out to us at team@aperturedata.io. Stay informed about our journey by subscribing to our blog.

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I want to acknowledge the insights and valuable edits from JJ Nguyen, Ali Asadpoor and Ian Yanusko.

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