Presented by MongoDB
We live in exciting times for app developers: the advent of democratizing innovations like generative AI (gen AI) and AI-powered coding assistance will lead to an explosion of new applications. Indeed, IDC predicts that over 750 million cloud-native applications will be created by 2025. But for many organizations, maintaining a regular cadence of competitive new products and services remains a challenge.
“On the one hand, organizations are under constant pressure to innovate and differentiate — and that pressure has increased because of generative AI and how disruptive or advantageous it could be for their business,” said Sahir Azam, chief product officer at MongoDB. “Yet, the cost of capital has gone up significantly. Teams are being asked to do this with fewer resources, more efficiency, and for less cost. So, there’s a real tension between the market disruption with gen AI on one hand, and cost-saving pressure and economic headwinds on the other. Balancing those two is top of mind.”
What’s more, developers are in short supply. As a result, it’s crucial that these valuable resources focus on solving their organization’s core challenges, rather than dealing with the complexity of traditional relational database systems. Prioritizing the developer mission is the best way for organizations to stay competitive, as well as the vendors they partner with.
“And that’s why we’re making sure we build technology solutions that are delightful for developers and serve their needs,” Azam added. “But we’re also supporting the most mission-critical, scalable and secure applications in the world.”
VentureBeat spoke with Azam about what organizations are prioritizing as they work to modernize their stacks, from ways AI is transforming the development process from the ground up, to revolutionizing the end user experience and tackling sprawl.
Moving faster with generative AI
Advances in AI, and generative AI in particular, are the biggest news in tech today. Developers and organizations are especially excited about the new AI-powered tools designed to increase productivity. These include everything from a chatbot that answers coding questions, to code generation assistants like Amazon CodeWhisperer and GitHub Copilot.
Azam shared some of the ways MongoDB is investing in AI. For one, he explained, the company has embedded AI into its developer tooling to make it easier for developers to write MongoDB code and queries according to the company’s best practices. MongoDB has also partnered with some of the major hyperscalers — the large-scale data centers that offer massive, on-demand computing resources. These partnerships are focused on optimizing large language model (LLM) training with internal knowledge of MongoDB’s own resources, including documentation, best practices and knowledge bases.
The AI boom also means tools are emerging to support an array of AI use cases. For instance, developers using public APIs like OpenAI and Azure AI need a tool to help them use their proprietary data to better customize their results — and RAG, or Retrieval-Augmentation Generation, was born. And for companies that build and train their own models, the vector database has emerged. Vector databases make it easier for machine learning models to remember previous inputs, making power search, recommendations and text generation use cases more effective.
“For most organizations, the challenge in bringing on these tools also means a whole new technology partner and brand-new technology to validate,” Azam explained. “Making sure it’s secure, stable, performant and so on puts major pressure on IT leaders — and adds yet more tech sprawl. To counter that challenge, we’ve focused on enabling vector database capability out of the box.”
For example, with Atlas Vector Search developers can build AI-powered experiences while accessing all the data they need through a unified and consistent developer experience. Because Atlas Vector Search is built on the MongoDB Atlas developer platform, customers can leverage it without the burden of finding, buying, installing and managing yet another component.
Other AI advances under MongoDB’s belt include new LLM capabilities in MongoDB Compass, which aid developers in MongoDB query writing, speeding up the development process and making sure the code is more accurate. Azam shared that they’ve also integrated gen AI into Atlas Charts, which helps build charts and graphs for the application’s dashboard so that developers can now use natural language to automatically generate queries.
“Typically, you would have to know MongoDB’s query language to generate those beautiful charts and graphs that you want to build in your app or put on your dashboard for your business to look at,” said Azam. “Now you can use natural language to automatically generate the query.”
Finally, MongoDB has begun to implement AI capabilities into its Relational Migrator tool, which significantly reduces the high cost of modernizing legacy. It analyzes the legacy database and then automatically generates new data schema and code to migrate to MongoDB Atlas, with no downtime required. From there, it generates optimized code for working with data in the new, modernized application.
Consolidating costs and tackling technology sprawl
After the wave of digital transformation that marked the past few years, organizations are now taking stock of their vendor relationships. Leaders see how overlapping vendor agreements are leading their teams to spend more time on maintenance than delivering business value.
“We’ve been through the era of the pandemic and a looser monetary policy where it was easy for organizations to spend a lot on technology, leverage whatever sprawl of tools they might have, even if they’re overlapping, even take on the cost and tax of integrating all those things together,” said Azam. “We’re now seeing organizations looking to consolidate costs with less vendors who can provide more capabilities so that they can save time and effort operationally.”
This is exactly why MongoDB has put a major focus on enabling these business needs.
“The developer data platform strategy has been an expansion of what MongoDB has been up to from day one, which is getting the data out of the way of developers building modern applications,” he explained. “With one interface, one language to learn, one environment, developers have what they need to build today’s applications faster, with significantly less sprawl.”
As a result, organizations spend less money and developers are more productive. They’re able to build any kind of application, and gain the flexibility to leverage multiple clouds, whether for differentiation or pricing benefits, or even run apps in their own data center.
The transformation of end-user experiences
“Every organization wants to be defined by the customer experiences they provide, and increasingly those customer experiences are driven by software,” Azam said. “MongoDB makes it easy for organizations to move fast, and to take an idea from inception to a globally scalable application that can serve those millions of users more easily than any other platform.”
On top of that, MongoDB does it in a truly multi-cloud way, which means a developer can build an application in a customer’s data center or run across all major public cloud platforms simultaneously when necessary (such as for regulatory reasons). Organizations can work with multiple infrastructure providers, and as necessary, take advantage of each provider’s differentiated services more easily, all with the flexibility of controlling and managing their data no matter where it needs to run.
Notably, Azam explained, MongoDB is the only company that’s combined all that complexity into a single developer data platform, not just for a single component of the application or database stack.
“If an organization is betting on a technology that’s in the data space, it’s likely a decision that they’ll live with for years, if not decades,” Azam said. “It’s incumbent on them to find technology that their developers love, that can help recruit talent but can also scale with the organization.”
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