Capital One’s Masterclass in Serverless Observability: Insights from AWS re:Invent 2024

At AWS re:Invent 2024, I attended a remarkable session in which Capital One shared its approach to serverless observability and how it has evolved through its ambitious cloud transformation. Pairing their insights with AWS’s latest innovations in observability revealed a sweeping picture of what it takes to thrive in a serverless-first architecture. Here’s the full story.

A Paradigm Shift: Observability in the Serverless Era

Traditional observability relies on monitoring primitives—compute, storage, networking, and security logs. Serverless, however, abstracts away these foundational elements, requiring organizations to rethink how they monitor applications. Capital One has embraced this challenge head-on, focusing instead on user interactions and business outcomes.

Capital One’s strategy goes beyond traditional metrics like P95 latency to uncover the unique experiences of individual users. Adopting an observability-driven development approach ensures every feature is designed with performance and capacity insights in mind, allowing them to deliver exceptional customer experiences.

Contrast this approach to collecting I/O performance from network and storage systems along with web server longs to infer a metric such as average transaction time.

AWS’s Innovations in Serverless Observability

AWS’s continuous product development is reflected in Capital One’s journey. At re:Invent, AWS unveiled several powerful tools to enhance serverless observability:

• SnapStart for Python and .NET: Reduces cold start times by seven times, ensuring faster execution for serverless functions.
• CloudWatch Live Tail: Offers real-time log streaming, allowing teams to debug issues as they occur.
• Application Signals: Provides detailed metrics for application performance, including user transaction patterns and bottlenecks.
• Transaction Search: Empowers developers to query structured logs with precision for deeper insights into system behavior.

These tools align with Capital One’s observability strategy, enabling them to monitor workloads efficiently while focusing on what truly matters—user satisfaction.

Capital One’s Lambda-First Philosophy

What sets Capital One apart is its disciplined approach to cloud transformation. They embraced a Lambda-first strategy, prioritizing serverless whenever possible:

1. Lambda: The default choice for running application logic.
2. Fargate: For containerized workloads where Lambda isn’t suitable.
3. EC2: Only as a last resort for legacy or highly specific workloads.

This hierarchy reduces observability complexity by narrowing the range of architectures they need to monitor. Many enterprises struggle with the heterogeneity of legacy systems, containers, and cloud-native apps, but Capital One’s Lambda-first approach simplifies their monitoring stack.

Managing the Cost of Observability

Due to their granular nature, serverless architectures generate immense amounts of data, which can drive up costs for services like CloudWatch. Capital One has tackled this with a cost-efficient strategy:

• Log Archiving: Logs are moved from CloudWatch to cost-effective S3 tiers such as Standard-IA or Glacier, depending on relevance and retention needs. This approach balances cost savings with data accessibility.
• Retention Policies: By focusing on the most critical logs and applying intelligent retention policies, they ensure they’re only paying for data that delivers real value.

This strategy represents their “biggest knob” for controlling observability costs, demonstrating the importance of designing cost-saving measures into the observability stack from the outset.

Innovations in Observability Practices

Capital One pairs AWS’s observability tools with their own structured logging and application monitoring strategies:

• Structured Logging: They emphasize a standardized approach to logging, enabling seamless integration with CloudWatch and advanced querying for faster insights.
• Distributed Tracing with OpenTelemetry: They use tracing to follow requests across their serverless ecosystem, improving visibility into complex workflows.
• Synthetic Canaries: These are used to monitor application paths proactively, ensuring uptime and reliability.

A demo during the session highlighted the power of these tools in action. Developers used CloudWatch Live Tail and Application Signals to identify and fix issues in real-time, showcasing the effectiveness of combining structured logging with intelligent observability queries.

Cultural and Architectural Transformation

What makes Capital One’s observability journey particularly inspiring is the cultural transformation behind it. Their move to the cloud wasn’t just a technical shift—it required significant changes in governance, team practices, and architectural priorities.

Observability isn’t an afterthought at Capital One; it’s a core part of their development lifecycle. By embracing serverless-native observability tools and integrating them into every stage of development, they’ve ensured that their applications remain performant, scalable, and cost-efficient.

Lessons for Enterprises

Capital One’s journey offers invaluable lessons for enterprises navigating serverless:

1. Adopt a Clear Architectural Hierarchy: A Lambda-first strategy simplifies observability and reduces complexity.
2. Leverage Cost-Control Measures: Archiving logs to cost-efficient storage tiers can significantly reduce expenses.
3. Focus on Observability-Driven Development: Embedding observability into every stage of the lifecycle ensures better performance and reliability.
4. Utilize AWS Innovations: Tools like SnapStart, Application Signals, and CloudWatch Live Tail are game-changers for serverless monitoring.

Capital One’s example proves that observability is not just a technical challenge but a cultural one. By aligning tools, strategies, and team practices, enterprises can unlock the full potential of serverless architectures.

Capital One’s session at re:Invent underscored a critical truth: serverless observability is not about reacting to issues but anticipating them, optimizing performance, and delivering better user experiences. Their approach offers a clear blueprint for success for organizations embarking on a similar journey.