Pains

Implementing schema federation or stitching across multiple services is complex, slow, and difficult to secure. Federation introduces fragility and inter-domain dependencies that are hard to manage and debug at scale.

Terraform lacks a robust, built-in testing framework for HCL. Unit testing is difficult and integration testing (spinning up real infrastructure) is slow and expensive. Teams resort to embedding validation scripts or hacks, leading to accidental infrastructure drifts reaching production.

TypeScript's type system prevents developers from using certain valid JavaScript patterns. For example, nested function definitions within objects don't work properly, forcing developers to use less natural workarounds like the builder pattern.

Integrating Three.js with Next.js creates compatibility issues that are difficult to resolve, breaking expected library functionality and causing significant frustration during development.

Docker's native volume management lacks comprehensive enterprise-grade stateful operations. Data integrity guarantees, backups, encryption at rest, and cross-host replication cannot be reliably accomplished using only Docker volume commands. Organizations must adopt complex external orchestration systems like Kubernetes to meet production stateful workload requirements.

Serverless cold starts are a major performance pain point on Vercel. Documentation suggests using Edge, but Edge functions don't support Node.js APIs, making them impractical for most use cases, and support for cold start mitigation is severely lacking.

TypeScript's type checking guarantees internal correctness but cannot protect against untrusted external API inputs, backend response drift, corrupted local storage, malformed environment variables, or user-generated content. At scale, this gap between compile-time and runtime safety becomes critical.

As an in-memory store, Redis requires all data to reside in RAM, limiting total dataset size by available memory. Large datasets consume significant memory overhead per instance, creating cost and performance pressure when data grows beyond infrastructure limits.

GraphQL queries can become increasingly complex as projects grow, with deeply nested queries and over-fetching of fields leading to poor performance, extensive database joins, and slow execution times. Query complexity assessment is difficult and clients can crater performance without guardrails.

PostgreSQL query execution plans can become unstable, causing previously performing queries to suddenly degrade. Developers must use advanced tools like Query Plan Management (QPM) and pg_hint_plan to ensure consistent query performance.

The Rust compiler's incremental engine has gaps where certain compilation parts are not incremental or not cached optimally. For example, expansion of derive proc macros is not currently cached, causing unnecessary recompilation.

LangChain abstracts over provider details, but developers encounter situations where specific chains don't work with certain provider APIs despite the abstraction promise. Hidden limitations and lack of transparency force developers to care about underlying implementation details.

Large organizations adopting Docker at scale face organizational challenges including the need to retrain staff, re-architect legacy systems, and manage increased complexity of microservices architectures. This transition is fraught with difficulties despite clear deployment and scalability benefits.

Setting up AWS Cognito for SSO is unreliable with multiple bugs and non-functional components. The integration experience is messy and frustrating, lacking stability and clarity.

PostgreSQL lacks native horizontal scalability features. When instance sizes become insufficient, teams experience downtime during scaling operations. Aurora vacuuming and scaling issues persist, and teams desire alternatives like CockroachDB that support true horizontal scaling without downtime.

Storage I/O performance is the primary performance concern (24%), followed by model/data loading times (23%). For AI/ML workloads, storage costs have become the dominant concern (50% cite as primary), reflecting enormous data requirements of training datasets and model checkpoints.

Improper use of Redis commands that modify data (INCR, HSET, etc.) without proper locking mechanisms can lead to race conditions where concurrent updates from multiple clients overwrite each other, causing data inconsistency.

Using the same Kubernetes manifests across development, staging, and production without environment-specific customization leads to instability, poor performance, and security gaps. Environment factors like traffic patterns, scaling needs, and access control differ significantly.

33% of respondents cite securing applications and integrating third-party tracing systems as pain points. Security has emerged as the #1 concern for DoK workloads, driven by complexity of securing distributed data workloads and regulatory compliance.

Upgrading to Next.js 16 causes significant, unexpected increases in build times and output bundle sizes. Projects that previously built quickly now consume more CI/CD resources and produce larger bundles, harming Core Web Vitals.

PostgreSQL does not offer native multi-region replication capabilities. Organizations must rely on logical replication and third-party tools like pglogical or BDR, increasing vendor dependency, expertise requirements, and operational overhead while creating potential vendor lock-in risks.

Gemini API enforces a hard cap of 1000 requests per hour, which is insufficient for production-scale applications. Solo developers can manage, but scaling immediately hits walls triggering '429 Too Many Requests' errors.

When something goes wrong in an AWS environment, it is difficult to identify root causes and determine fixes. Error handling across distributed services is complex and time-consuming.

Redis Cluster cannot execute atomic transactions when keys are distributed across different nodes, requiring developers to carefully plan key distribution strategies or use alternative solutions for applications requiring transactional integrity.