Pains

Vercel's serverless functions have a 10-second timeout limit on free tier and 60-300 second limits on paid plans, causing issues with complex payment processing, long-running agents, and AI workloads. Documentation claims 300 seconds but functions timeout at 60 seconds under load. Edge functions have even stricter limits and lack full Node.js compatibility.

AWS billing is opaque and difficult to track. Hidden charges from services like EBS snapshots, NAT gateways, and Route 53 are hard to identify. Billing alerts arrive before invoices are sent, and AWS's pay-per-use model makes experimentation risky without proper monitoring.

Claude Code fills specification gaps with reasonable but contextually wrong assumptions (e.g., OAuth2 instead of required SAML SSO, individual auth instead of organization-based). The generated code looks correct in isolation but creates unmaintainable architectures that don't match actual business requirements.

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.

PostgreSQL does not support in-place major version upgrades. Upgrades require either dumping and restoring the entire dataset or setting up logical replication, with rigorous application compatibility testing required. Delaying upgrades increases complexity and risk, as outdated versions miss critical security patches, transforming routine maintenance into a complex, high-risk migration project.

Redis provides only eventual consistency through replication, which can introduce latency and inconsistency during network partitions. Replication mechanisms designed for basic redundancy fall short for applications demanding strong consistency or transactional guarantees in real-time scenarios.

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.

Libraries that assume React 17 or 18 create compatibility issues during React 19 migration. Dependencies often present larger migration barriers than React itself, requiring teams to audit the entire dependency tree before upgrading.

Retrieving files from S3 in emergency situations is difficult because public cloud data limits cause downloads to take up to 12 hours, preventing immediate access to critical content.

v0 has broken Git integration, manual code edits vanish during later generations, exports produce blank screens or incomplete projects (missing pages), and sharing projects with teams is problematic. Code that works in v0 often breaks in production.

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.

Incorrect Spring Security configuration easily leads to security breaches including exposing server data, improper authorization, and leaving default settings enabled. Security issues require vigilant code reviews.

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.

GitHub Actions requires explicitly naming required status checks, but in monorepos with dynamic pipelines, only relevant checks should be mandatory. If a PR only touches `api1` but `web-app1` checks aren't triggered, the PR cannot merge even though all relevant checks passed. This forces developers to run unnecessary pipelines just to satisfy merge requirements.

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.

Existing AI agent benchmarks (e.g., WebArena at 35.8% success) fail to predict production performance, creating false confidence. Real-world scenarios expose that benchmark performance is not fit for production use.

Static AI agents become stale quickly as customer preferences, market conditions, and regulations evolve. Without adaptability mechanisms, agents produce outdated recommendations, miss fraud patterns, and provide incorrect information, eroding trust and value.

Organizations apply AI agents to problems too complex for current capabilities, and many AI vendors overstate capabilities ('agent washing'). This sets projects up for failure when promised enterprise-grade outcomes don't materialize.

Vercel enforces strict concurrency caps that cause requests to be queued or throttled during traffic spikes. AI applications with many simultaneous function streams fail with 504/429 errors unless users upgrade to Enterprise, requiring expensive external scaling solutions.

Large monorepos and multi-repo setups hit massive BLOB count and ref limits. Cloud-hosted or shared disk storage creates exponential I/O transfer costs and infrastructure strain, making Git nearly unusable and driving operational budgets to unprecedented levels.

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.

AI applications using RAG (Retrieval-Augmented Generation) with large payloads quickly exceed Vercel's bandwidth quotas. Fetching large documents repeatedly or shuffling hundreds of gigabytes monthly triggers expensive overages that can cost hundreds of dollars.

Errors compound with each step in multi-step reasoning tasks. A 95% accurate AI agent drops to ~60% accuracy after 10 steps. Agents lack complex reasoning and metacognitive abilities needed for strategic decision-making.

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.