Pains

As Stripe adds more features and diversifies into Atlas, Capital, and Platform of Platforms, the API complexity grows. This erodes Stripe's original advantage of being super simple to set up.

Supabase's complexity creates barriers for novice developers, compounded by limited community support and insufficient documentation tailored to beginners. The platform requires significant SQL and database knowledge to use effectively.

Clerk requires paid tiers to enable multi-factor authentication, passkeys, and impersonation features that competitors offer free or include by default, forcing developers to pay for baseline security functionality.

Supabase support sometimes refuses to help with complex PostgreSQL issues, claiming they are database questions rather than platform questions. This can leave developers stuck without recourse.

Clerk is tightly coupled to Next.js with pre-built UI components optimized for that framework. Integration with other frameworks or backend authentication scenarios is difficult and less well-supported.

As MongoDB databases grow in size and complexity, queries become slow and inefficient. Developers must fine-tune queries, indexes, and database architecture to ensure optimal performance, but this is time-consuming and error-prone.

Supabase lacks guides for structured database development. Developers must create workarounds like master GitHub repos of SQL commands and custom documentation, making collaboration and migration difficult.

Debugging RLS policies requires diving into the Supabase dashboard, making them disconnected from the main codebase. Complex policies are time-consuming to debug and misconfiguration can expose data.

Database compute scaling is not automatic and requires manual intervention to handle traffic spikes, creating operational burden and potential downtime risk during unexpected load increases.

Data storage and transit costs consume a significant portion of cloud expenditures. Understanding and optimizing these costs is complex due to variable pricing based on location, capacity, and transfer patterns.

Azure DevOps Server requires significant infrastructure setup and operational overhead, particularly for smaller teams. It has limited scalability for large projects compared to Azure DevOps Services (cloud version).

DynamoDB's on-demand pricing model can lead to unexpected expenses with variable workloads. Provisioned mode requires careful capacity planning to avoid throttling or waste, and cost monitoring is complex without proper tooling configuration.

Supabase client does not support raw SQL queries or advanced PostgreSQL features. Developers must work around these limitations by creating stored procedures or views, which places application logic in the database and requires leaving the editor to modify code stored in the GUI.

Developers fear vendor lock-in due to Supabase's deep integration with cloud providers and the high operational overhead and complexity of migration, limiting willingness to adopt for long-term projects.

Supabase's email authentication has undocumented limits that can leave users unable to authenticate, creating support headaches and potential user experience issues.

Supabase edge functions introduce performance degradation that can become a bottleneck in production applications, limiting their practical utility for performance-critical operations.

Developers must balance using the best resources for performance with managing costs. Finding the optimal resource configuration to meet both requirements is a constant struggle.

Failing to implement connection pooling is a common mistake in PostgreSQL deployments. Each connection consumes approximately 10MB of RAM, and applications that create new connections for each database operation can quickly exhaust server resources, leading to performance degradation and application failures.

As Python-based applications grow in complexity, technical debt accumulates, making it increasingly difficult to introduce new features or make updates. Developers must balance addressing technical debt with introducing innovative solutions.

Teams must manage multiple separate tools to handle different aspects of database operations, increasing complexity and reducing efficiency.

Junior developers have restricted access to databases, limiting their ability to work independently and requiring senior developer involvement for routine tasks.

Free and Pro plans only retain email data for 1 and 3 days respectively, making it difficult to diagnose deliverability issues or identify longer-term trends without upgrading or implementing custom logging infrastructure.

Email API providers impose rate limits (e.g., 10 requests/sec) that force developers to implement backend message queues (Redis) rather than sending directly from frontend, adding architectural complexity for production-scale applications.

Datadog makes it too easy to create monitors without guardrails. Teams quickly accumulate hundreds of alerts (300+ monitors reported) with no built-in alert quality scoring or deduplication. Reaching a healthy signal-to-noise ratio requires significant manual tuning over months.