January 2026 Summaries

Apollo Client 4.1 introduces significant enhancements, including full support for the @stream directive, advancements in fragment watching capabilities, and improvements to cache handling. The update aligns with the latest GraphQL incremental delivery spec, providing new APIs to handle fragments efficiently, such as allowing arrays to be passed to the useFragment function. This facilitates the simultaneous watching of multiple cache entities, eliminating the need for hoisting item fragments or creating additional components. The release also addresses various bug fixes, such as correcting issues with incremental payloads and ensuring proper rerendering with @defer or @stream directives. Performance is notably improved by deduplicating watches and observables for the same cache objects, and other enhancements include improved client awareness through support for headers transport and the introduction of callback functions as context options on mutations.

Response caching has been introduced in the GraphOS Router to optimize GraphQL performance by reducing service load and improving latency. This new feature, a replacement for the older entity caching, is designed for federated GraphQL workloads and uses Redis as a backing store, allowing for both root-level and granular entity caching. It addresses the unique caching challenges in GraphQL, such as mixed TTLs and safe data sharing, by deriving TTLs from HTTP Cache-Control headers and supporting active invalidation through cache tags. A case study with Dow Jones demonstrates significant performance improvements, including reduced backend service load and improved response times. The feature is available on various GraphOS plans, requiring Router v2.10.0 or later, and includes tools for observability and debugging to ensure effectiveness. This new caching approach builds on previous entity caching work by integrating detailed cache key inspections and supporting Redis cluster deployments.

Securing Apollo Federation subgraphs is critical to maintaining a robust security framework as AI tools increasingly expose vulnerabilities in publicly accessible services. The architectural design of Apollo Federation dictates that subgraphs remain internal services accessed only through a central router, which serves as the core point for security enforcement by providing access control, demand management, and operation safelisting. This internal-only access is crucial to prevent bypassing security measures and exposing sensitive coordination mechanisms. Key security practices include keeping subgraphs inaccessible from the public internet, disabling introspection in production, and implementing authentication and authorization at the router level. Additional measures such as data validation, pagination, setting operation limits, and monitoring through observability tools are recommended to safeguard the infrastructure. The overarching principle is that subgraphs must only be accessed via the router to enable centralized governance and protect against potential API sprawl and exploitation.

Capital One's transformation of its data marketplace was driven by a product mindset and embedded governance, leveraging Apollo and GraphQL Federation to create a cohesive and discoverable platform. This initiative addressed the complexity of large data ecosystems by providing a single source of truth and improving data discoverability, speed, and orchestration across the enterprise. The implementation focused on five core pillars—Publish, Manage, Find, Use, and Govern—ensuring data quality, compliance, and observability. By engaging producers and consumers and iterating based on feedback, Capital One standardized its approach to data access, enabling faster deployments and reducing the need for extensive approval processes. This strategy not only improved operational efficiency but also demonstrated that integrated governance could accelerate rather than hinder progress, turning data management from a bottleneck into a strategic asset and setting the stage for future innovations and growth in AI-powered applications.

N-able achieved remarkable growth by transitioning to Apollo GraphQL, overcoming challenges of fragmented systems and excessive API calls, which frustrated their customers. Initially planning for modest adoption, they quickly expanded to 14 subgraphs with 85 contributors and 300 Apollo Studio users within a year. Central to this transformation was Dhanik Alkegama, whose team focused on governance that accelerated rather than hindered development, emphasizing mandatory Apollo certification to harmonize understanding and collaboration across teams. This approach enabled efficient management of subgraphs, reduced process bottlenecks, and streamlined workflows, allowing a single query to replace hundreds of REST calls. N-able's strategy included embedding governance into workflows and designing for exponential adoption, fostering an environment where innovation thrived without sacrificing stability. As they continue to evolve, N-able aims to make their graph the default entry point for all customers, further reducing reliance on REST APIs and enhancing collaboration across teams.