The rise of generative AI, with OpenAI and launch of access to tools like ChatGPT, has empowered businesses to quickly realize the power of GenAI. Seeing the possibilities, most organizations that moved quickly and have relied on OpenAI to manage its AI models, are taking a step back.
Here’s what I often see: integrating directly with OpenAI was free, so why wouldn’t an organization want to try it. Those that have, moved quickly, and are now questioning security and data privacy.
It’s time to reevaluate: Why migrate?
Working with customers all over the globe, we’ve seen a consistent pattern with OpenAI creating several operational difficulties, such as:
- Unstable Updates and Policy Changes: OpenAI frequently updated its models and policies, which often led to disruptions in service. These changes can be unpredictable, and each new update requires retesting and reconfiguration of workflows to ensure compatibility. This results in downtime, causing issues with client experiences, and increases the operational burden on development teams.
- Data Privacy and Security Concerns: Especially for organizations in regulation-heavy industries like finance, data security is crucial. OpenAI’s policies around data handling and storage are not always transparent or robust enough to meet strict compliance requirements, including SOC 2 Type 2. This uncertainty raises concerns about data protection, especially when dealing with sensitive customer information.
- Limited Customization and Scalability: Although OpenAI provides general-purpose AI models, it lacks the flexibility to customize and scale specific AI models to accommodate unique workflows. OpenAI’s limitations in supporting custom models and its reliance on shared infrastructure makes it difficult to offer industry-specific AI solutions at scale.
- Performance Bottlenecks: As OpenAI’s infrastructure became increasingly popular, organizations have noticed performance issues during periods of high demand. The shared infrastructure has meant that peak usage can affect the performance of the users own operations. The unpredictability can lead to slower response times, particularly during high-volume periods, impacting user experience.
These challenges made it clear that continuing to rely on OpenAI can hinder an organization’s ability to scale, deliver reliable service, and meet compliance and security needs.
Example: Trisk Builds a More Capable, More Secure AI-Driven SaaS Migrating to AWS
For example, Trisk, a SaaS company that specializes in no-code AI-driven business process automation, faced these challenges and needed a better AI infrastructure, prompting them to seek a new solution. The team was impressed by AWS’s security and saw an opportunity to streamline their business and run their entire operation on AWS services.
Trisk partnered with AllCloud to migrate from OpenAI to an AI-driven architecture using AWS Bedrock, Lambda, and other AWS services. Together, Trisk and AllCloud built a solution that would provide advanced machine-learning models and cloud services on a secure, scalable, and reliable platform.
Security is critical: Make the move to AWS
The number one reason we see executives make the transition is security. We have clients all over the globe with strict compliance requirements, that cannot take on any risks of issues in highly related industries.
To continuously innovate and leverage the latest advancements in generative AI, it’s critical to choose an AI model recognized for performance and security, and consider the right strategy of building on a secure infrastructure.
With AllCloud, you can seamlessly migrate existing OpenAI workloads to Anthropic on Amazon Bedrock ensuring scalability, and robust security compliance with AWS standards.
Make it a smooth transition
AllCloud’s works with organization on a migration plan created to give you a clear understanding and roadmap for implementation, leveraging the power of Anthropic. We’ll take a deeper look at the pain paints you are experiencing, and what the move to AWS would look like. Learn more here.