Enhancing AI Observability with MongoDB and Langtrace

Puja Roy
June 9, 2025

Building high-performance AI applications isn’t just about choosing the right models—it’s also about understanding how they behave in real-world scenarios. Langtrace offers the tools necessary to gain deep insights into AI performance, ensuring efficiency, accuracy, and scalability.

San Francisco-based Langtrace AI was founded in 2024 with a mission of providing cutting-edge observability solutions for AI-driven applications. While still in its early stages, Langtrace AI has rapidly gained traction in the developer community, positioning itself as a key player in AI monitoring and optimization. Its open-source approach fosters collaboration, enabling organizations of all sizes to benefit from advanced tracing and evaluation capabilities.

The company’s flagship product, Langtrace AI, is an open-source observability tool designed for building applications and AI agents that leverage large language models (LLMs). Langtrace AI enables developers to collect and analyze traces and metrics, optimizing performance and accuracy. Built on OpenTelemetry standards, Langtrace AI offers real-time tracing, evaluations, and metrics for popular LLMs, frameworks, and vector databases, with integration support for both TypeScript and Python.

Beyond its core observability tools, Langtrace AI is continuously evolving to address the challenges of AI scalability and efficiency. By leveraging OpenTelemetry, the company ensures seamless interoperability with various observability vendors. Its strategic partnership with MongoDB enables enhanced database performance tracking and optimization, ensuring that AI applications remain efficient even under high computational loads.

Langtrace AI's technology stack

Langtrace AI is built on a streamlined—yet powerful—technology stack, designed for efficiency and scalability. Its SDK integrates OpenTelemetry libraries, ensuring tracing without disruptions. On the backend, MongoDB works with the rest of their tech stack, to manage metadata and trace storage effectively. For the client-side, Next.js powers the interface, utilizing cloud-deployed API functions to deliver robust performance and scalability.

Figure 1. How Langtrace AI uses MongoDB Atlas to power AI traceability and feedback loops
Diagram showing how Langtrace uses MongoDB Atlas. At the top left of the diagram is the Langtrace Client, which connects with the Langtrace feedback system, which is powered by MongoDB and MongoDB Atlas Search. This feedback system is connected to Slack. The Langtrace client also connects to the Langtrace backend and the Langtrace Customer's AI Code.

“We have been a MongoDB customer for the last three years and have primarily used MongoDB as our metadata store. Given our longstanding confidence in MongoDB's capabilities, we were thrilled to see the launch of MongoDB Atlas Vector Search and quickly integrated it into our feedback system, which is a RAG (retrieval-augmented generation) architecture that powers real-time feedback and insights from our users. Eventually, we added native support to trace MongoDB Atlas Vector Search to not only trace our feedback system but also to make it natively available to all MongoDB Atlas Vector Search customers by partnering officially with MongoDB.” Karthik Kalyanaraman, Co Founder and CTO, Langtrace AI.

Use cases and impact

The integration of Langtrace AI with MongoDB has proven transformative for developers using MongoDB Atlas Vector Search. As highlighted in Langtrace AI's MongoDB partnership announcement, our collaboration equips users with the tools needed to monitor and optimize AI applications, enhancing performance by tracking query efficiency, identifying bottlenecks, and improving model accuracy. The partnership enhances observability within the MongoDB ecosystem, facilitating faster, more reliable application development.

Integrating MongoDB Atlas with advanced observability tools like Langtrace AI offers a powerful approach to monitoring and optimizing AI-driven applications. By tracing every stage of the vector search process—from embedding generation to query execution—MongoDB Atlas provides deep insights that allow developers to fine-tune performance and ensure smooth, efficient system operations.

To explore how Langtrace AI integrates with MongoDB Atlas for real-time tracing and optimization of vector search operations, check out this insightful blog by Langtrace AI, where they walk through the process in detail.

Opportunities for growth and the evolving AI ecosystem

Looking ahead, Langtrace AI is excited about the prospects of expanding the collaboration with MongoDB. As developers craft sophisticated AI agents using MongoDB Atlas, the partnership aims to equip them with the advanced tools necessary to fully leverage these powerful database solutions. Together, both companies support developers in navigating increasingly complex AI workflows efficiently.

As the AI landscape shifts towards non-deterministic systems with real-time decision-making, the demand for advanced observability and developer tools intensifies. MongoDB is pivotal in this transformation, providing solutions that optimize AI-driven applications and ensuring seamless development as the ecosystem evolves.

Explore further

Interested in learning more about Langtrace AI and MongoDB partnership?

Start enhancing your AI applications today and experience the power of optimized observability.

To learn more about building AI-powered apps with MongoDB, check out our AI Learning Hub and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

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What I Wish I’d Known Before Becoming a Solutions Architect

My journey to becoming a solutions architect (SA) has been anything but straightforward. After working as an engineer in telecom, receiving my PhD in computer science, and spending time in the energy efficiency and finance industries, I joined MongoDB to work at the intersection of AI and data solutions, guiding enterprises to success with MongoDB’s flexible, scalable database platform. It’s a role that requires having both deep technical knowledge and business acumen, and while the nature of the SA role has evolved over time, one thing has remained constant: the need to understand people, their problems, and how the technology we use can solve them. As I reflect on my career journey, here are some key lessons I’ve learned about being an SA—and things I wish I’d known when I first started. 1. Influence comes from understanding In my earlier roles, I thought that presenting clients with a perfect technical solution was the key to success. However, I quickly learned that being a successful solutions architect requires much more than technical excellence. Instead, the solutions that you offer need to be aligned with customers’ business needs. You also need to understand the underlying challenges driving the conversation. In my role, I frequently work with clients facing complex data challenges, whether in real-time analytics, scaling operations, or AI applications. The first step is always understanding their business goals and technical pain points, which is more important than simply proposing the “best” solution. By stepping back and listening, you can not only better design a solution that addresses their needs but also gain their trust. I’ve found that the more I understand the context, the better I can guide clients through the complexities of data architecture—whether they're building on MongoDB Atlas, optimizing for performance, or leveraging our data products to drive innovation. What I wish I’d known: Influence doesn’t come from showing how much you know—it comes from showing how much you understand. Listening is your most powerful design tool. 2. Building champions drives success You can build the most scalable, secure, and elegant system in the world — but if it doesn’t align with stakeholder priorities, it will stall. In reality, architecture is rarely a purely technical exercise. Success depends on alignment with a diverse set of stakeholders, each with their own priorities. Whether you're collaborating with engineering teams, product managers, security specialists, or leadership, the key to success is to engage everyone early and often. Stakeholders are not just passive recipients of your solution; they are active participants who co-own the outcome. In many cases, your design will be shaped by their feedback, and finding a champion within the organization can make all the difference. This champion—whether from the technical side or the business side—will help advocate for your solution internally, align the team, and overcome any resistance. This is particularly important for MongoDB SAs because we’re often addressing diverse needs, from data privacy concerns to performance scalability. Building a strong internal advocate ensures that your design gains the necessary momentum and credibility within the client’s organization. What I wish I’d known: Success doesn’t come from being right—it comes from being aligned. Influence is earned through empathy, clarity, and trust. As a solutions architect, your greatest value is not just in solving technical problems—it’s in helping diverse teams pull in the same direction. And nothing accelerates that more than having a strong, trusted internal champion on your side. 3. Winning deals requires teamwork At MongoDB, we’re not just selling a product—we’re selling a solution. Winning deals involves close collaboration with Sales, Engineering, and Client Services. The most successful deals come when the entire team is aligned, from understanding the customer’s unique needs to crafting a solution that fits their long-term goals. You want to win? Here’s what that actually looks like: You prep with sales like it’s a final exam. Know the account history, know the politics, know what was promised six months ago that never landed. Be the person who connects past pain to future value. You do dry runs and anticipate the tough questions. Then you hand those questions to someone else on your team who can knock them out of the park. That’s trust. You turn strategy decks into conversations . A flashy diagram is great, but asking “Does this actually solve the headache you told us about last week?” — that’s where momentum starts. You loop in Professional Services early to pressure-test feasibility. You loop in CSMs to ask, “If we win this, what does success look like a year from now?” You help sales write the follow-up  — not just with a thank-you, but with a crisp summary of what we heard, what we proposed, and what comes next. You make the path forward obvious. One of the most valuable lessons I’ve learned is that winning a deal doesn’t rely solely on delivering a flawless demo. It’s the little things that matter—anticipating questions, making quick adjustments based on client feedback, and being agile in your communication. Being part of a unified team that works seamlessly together is the key to winning deals and ensuring client success. What I wish I’d known: Winning a deal is a series of micro-decisions made together, not a solo act. Great architecture doesn’t close a deal—great alignment does. Your best asset isn’t the system you design—it’s the trust you build with your team and the confidence you project to your client that we’ve got this. Together. 4. You don’t have to know everything When I first transitioned into this role, I felt the pressure to master every piece of the tech stack—especially at MongoDB, where our solutions touch on everything from cloud data platforms to AI, real-time data processing, and beyond. It was overwhelming to think that I needed to be an expert in all of it. But here’s the truth: As a solutions architect, your real value lies not in knowing every detail, but in understanding how the pieces fit together. You don’t need to be the deepest expert in each technology—what’s important is knowing how MongoDB’s platform integrates with client needs and when to bring in the right specialists. The role is about connecting the dots, asking the right questions, and collaborating across teams. The more you embrace curiosity and rely on your colleagues, the better your solutions will be. What I wish I’d known: Mastery isn’t about knowing all the answers. It’s about knowing which questions to ask, and who to ask them to. Focus on principles, patterns, and clarity. Let go of the pressure to be the smartest person at the table—you’re there to make the table work better together. Curiosity is your compass, and collaboration is your fuel. 5. Architecture lives beyond the diagram When most people think of a solutions architect, they picture designing systems, building technical architectures, and drawing elegant diagrams. While that’s part of the job, the true value lies in how well those designs are communicated, understood, and adopted by the client. Specifically, your architecture needs to work in real-world scenarios. You’re not just drawing idealized diagrams on a whiteboard—you’re helping clients translate those ideas into actionable steps. That means clear communication, whether through shared documentation, interactive walkthroughs, or concise explanations. Understanding your client’s needs and constraints is just as important as the technical design itself. And when it comes to sizing and scaling, MongoDB’s flexibility makes it easy to adapt and grow as the business evolves. What I wish I knew: Architecture doesn’t end at the diagram—it begins there. The real value is realized in how well the design is communicated, contextualized, sized, and adopted. Use whatever format helps people get it. And before you document the system, understand the system of people and infrastructure you’re building it for. 6. It’s not just about data Data may be the foundation of my work as a solutions architect, but the real magic happens when you connect with people. Being a great architect means being a great communicator, listener, and facilitator. You’ll frequently find yourself between business leaders seeking faster insights and developers looking for the right data model. Translating these needs and building consensus is a big part of the role. The solutions we design are only valuable if they meet the diverse needs of the client’s teams. Whether it’s simplifying data operations, optimizing query performance, or enabling AI-driven insights, your ability to connect with stakeholders and address their unique challenges is key. Emotional intelligence, empathy, and collaboration are essential. What I wish I’d known: Being a great architect means being a great communicator, listener, and facilitator. Emotional intelligence is your secret weapon. The more time you invest in understanding your stakeholders’ pain points, motivations, and language, the more successful your architecture will be—because people will actually use it. 7. The job is constantly evolving and so are you The field of data architecture is rapidly evolving, and MongoDB is at the forefront of this change. From cloud migrations to AI-driven data products, the technology landscape is always shifting. As a solutions architect, you have to be adaptable and prepared for the next big change. At MongoDB, we work with cutting-edge technologies and constantly adapt to new trends, whether it’s AI, machine learning, or serverless computing. The key is to embrace change and continuously learn. The more you stay curious and open to new ideas, the more you’ll grow in your role and your ability to drive client success. As MongoDB continues to innovate, the learning curve is steep, but that’s what keeps the job exciting. What I wish I knew: You don’t “arrive” as a solutions architect—you evolve. And that evolution doesn’t stop. But everything you learn builds on itself. No effort is wasted. Every challenge adds depth. Every mistake adds clarity. The technologies may change, but the thinking compounds—and that’s what makes you valuable over the long run. It’s not just a role–it’s a journey Reflecting on my path to becoming a solutions architect at MongoDB, I realize that the journey is far from linear. From network protocols to financial systems and AI-driven data solutions, each role added a new layer to my experience. Becoming a solutions architect didn’t mean leaving behind my past—it meant integrating it into a broader perspective. At MongoDB, every day brings new challenges and opportunities. Whether you’re designing a solution for a global enterprise or helping a startup scale their data operations, the core of the job remains the same: solving problems, connecting people, and helping others succeed. And as you grow in the role, you’ll find that the most powerful thing you bring to the table isn’t just your expertise—it’s your ability to keep learning, to show up with intention, and to simplify complexity for everyone around you. To anyone stepping into this role at MongoDB: welcome. The journey is just beginning! Join our talent community for the latest MongoDB culture and careers content.

June 5, 2025
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Now in Public Preview: The MongoDB for IntelliJ Plugin

The MongoDB for IntelliJ plugin empowers Java developers to build and ship applications quickly and confidently by enhancing the Database Explorer experience in the IntelliJ IDEA. After first announcing the plugin in private preview at .local London in the fall of 2024, we’ve partnered with our friends at JetBrains to release a new and improved experience in public preview. Using the MongoDB for IntelliJ plugin, developers can analyze their application code alongside their database, accelerating query development, validating accuracy, and highlighting anti-patterns with proactive performance insights. What’s in the MongoDB for IntelliJ plugin? As part of the public preview, we’re committed to ensuring that the MongoDB for IntelliJ plugin not only meets developers' technical requirements but also paves the way for a seamless developer experience with MongoDB Atlas . The MongoDB for IntelliJ plugin Public Preview offers developers the following capabilities: Field-level autocompletion for Java queries - Auto-suggests field names from MongoDB collections as developers write queries. Schema and type validation - Surfaces inline warnings when query values don’t match the expected field type based on the collection schema, and validates that a field exists in your collection’s schema. Java query execution in IntelliJ console - Allows developers to test Java queries with a single click without needing to switch tools or translate syntax. Proactive anti-pattern detection - Identifies potential performance issues (such as a query missing an index) and provides inline warnings and documentation links. Spring and Java driver support - Supports query syntax across popular Java patterns, criteria API, and aggregation patterns. Code smarter with your AI - Plugin-generated linting insights help your in-IDE AI assistant detect and resolve code issues. Figure 1. Code smarter with your AI. Benefits of using the official MongoDB for IntelliJ plugin Java development often involves working with complex, evolving data models, making MongoDB’s flexible document model an ideal choice for Java applications' data layer. The plugin provides developers with a unified experience for building with MongoDB directly inside IntelliJ, enabling faster and more focused development. By eliminating the need to switch between IntelliJ and external tools, the plugin streamlines query development and testing workflows. Features like field-level autocomplete and inline schema validation reduces errors before runtime, allowing developers to build and validate MongoDB queries with confidence and speed. Whether writing queries with the MongoDB Java driver, Spring Data, or aggregation pipelines, the plugin provides context-aware suggestions and real-time feedback that accelerate development. Additionally, the plugin proactively flags common MongoDB query anti-patterns—such as missing indexes or inefficient operators—within your line of code, helping teams catch performance issues before they hit production. With the ability to test queries directly in the IntelliJ MongoDB console and view execution metadata like query plans and durations, the plugin brings performance awareness and code correctness to where developers actually write the code for their applications. How to get started with the MongoDB for IntelliJ plugin You can get started using the MongoDB for IntelliJ plugin through the JetBrains marketplace . Questions? Feedback? Please post on our community forums or through UserVoice . We value your input as we continue to develop a compelling offering for the Java community.

June 17, 2025