Pains

Developers struggle with integrating GPT Actions because it requires working with third-party APIs that have varying parameters and authentication methods. This complexity increases error likelihood and requires deep understanding of both GPT Actions framework and external APIs.

Supabase cannot act as an OpenID Connect Provider, preventing federation of identity to other systems and blocking participation in the OAuth-based ecosystem that AI agents rely on for integrations.

React's client-side rendering model introduces XSS vulnerabilities from improperly sanitized JSX content, bypassing PHP's native sanitization. Additionally, heavy reliance on npm packages increases exposure to supply-chain threats and malicious code in third-party dependencies.

PostgreSQL is not optimal for applications requiring real-time or near-real-time analytics. For massive single datasets (billions of rows, hundreds of gigabytes) with frequent joins, queries can take hours. PostgreSQL lacks native columnar storage support, necessitating non-core extensions and increasing architectural complexity.

Neon's Node.js SDK v19+ introduces a breaking change in how the HTTP query template function can be called. Calling it as a conventional function (with parentheses) is now an SQL injection risk and throws an error, requiring developers to update their applications.

Redis master-slave replication has only one master handling writes, creating a critical single point of failure. The clustering solution needed for redundancy was not production-ready at the time of these reports.

Custom admin panels that work for small teams degrade rapidly as the user base or data grows, leading to performance issues, broken queries, and unexpected feature failures. Significant rebuilding is often required if scalability wasn't planned from day one.

Raw GPT models have no knowledge of a company's specific business, products, or policies. Developers must build complex Retrieval-Augmented Generation (RAG) systems to dynamically fetch and feed the right information from help centers, tickets, and documentation in real-time, requiring significant ongoing maintenance.

SQLite uses file-level locking that locks the entire database during write operations, preventing concurrent writes. This becomes a critical bottleneck in applications with background workers, asynchronous operations, or high-frequency write patterns, and can easily lead to deadlocks.

Railway does not offer GPU-based hardware, which is a critical blocker for organizations that need to migrate certain workloads (e.g., ML inference, heavy computation) to the platform.

Go's garbage collector is unpredictable and unsuitable for latency-sensitive environments like high-frequency trading or real-time analytics. GOMEMLIMIT is described as unreliable, allowing requests 10x over the limit.

94% of organizations with agents in production have implemented observability tooling because agents cannot be trusted without visibility into execution traces and reasoning. Observability is a blocker for production deployment despite 89% adoption attempts.

Redis lacks robust security features out of the box and is accessible to anyone who can connect by default. Developers must manually implement firewalls, ACLs, SSL/TLS encryption, and other security measures.

The connectors and plumbing between language models and backend business systems are unreliable, causing agents to fail mid-task. This is not a model capability issue but an infrastructure and integration problem.

When AI agents fail, developers have no unified visibility across the entire stack. They must stitch together logs from the agent framework, hosting platform, LLM provider, and third-party APIs, creating a debugging nightmare. This makes it impossible to determine whether failures stem from tool calls, prompts, memory logic, model timeouts, or hallucinations.

PostgreSQL experiences table corruption problems that can result in data integrity issues. This was significant enough to motivate organizations like Uber to evaluate alternative databases.

Gemini API enforces unexplained geographic restrictions preventing developers in certain regions from even requesting API keys. This creates impossible barriers for multinational enterprises, where half of development teams cannot access the API while the other half are stuck in approval queues.

Rate limits for Gemini API are managed in Google AI Studio rather than the Google Cloud Console, creating a split configuration burden. API keys generated in Cloud default to restrictive Tier 1 limits, requiring manual re-import and upgrade requests that take days and cause production downtime.

The build toolchain completes compilation while silently omitting code without throwing errors. Developers see 'successful' builds that are actually missing critical bits, making debugging extremely difficult and leading to runtime failures.

Google AI Studio API has been unreliable for extended periods (2+ weeks) while the status page reports normal operation. OpenRouter reliability graphs show significant problems especially on Pro model, creating false confidence for developers.

Misconfigured IAM roles and permissions leave AWS environments vulnerable to unauthorized access. Developers must carefully manage user access and permissions to prevent security breaches.

Redis persistence through RDB snapshots and AOF (Append-Only Files) can fail to prevent data loss during crashes or unexpected failures. These mechanisms are unreliable for mission-critical workloads where data loss is unacceptable, especially when persistence is disabled for performance.

Third-party cookies are effectively dead for tracking and cross-site authentication flows. Chrome's deprecation timeline has shifted, but Safari and Firefox blocked them years ago. Developers who architected authentication around third-party cookie assumptions now face breaking changes across browsers.

31% of organizations still run unsupported Kubernetes versions, missing vital security and performance patches. Each skipped release compounds technical debt and increases API breakage risks when eventually upgrading.