Railway

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.

Despite claiming to be open to feedback and transparent, Railway ignores numerous reports of critical problems across forums, Twitter, and support channels. Customers have reported GDPR breaches, medical data leaks, and lost revenue with minimal follow-up from the team.

Users report 150ms+ latency from traffic being misrouted through incorrect regions, SSL certificates stuck in 'Validating' state for weeks, and sudden ECONNREFUSED errors breaking service-to-service communication.

Railway experiences recurring incidents in build pipelines, deployment mechanisms, networking layers, and API availability. The platform's simplified design concentrates risk, limiting user ability to route around failures.

Logs arrive in the wrong order on Railway, making it impossible to trace the sequence of events that caused errors. When combined with the 500 logs/sec limit, critical log lines are dropped before they can be inspected, preventing basic debugging and root cause analysis.

Railway stops running services once the $5 trial credit is exhausted, even if the application was previously live and running in production. This creates service disruption for new users transitioning to paid plans.

Services with high load perform poorly within Railway's opinionated runtime environment due to restrictive cgroup settings and limited tunability. Critical limitations like file descriptor limits (stuck at 1000) cannot be modified, and promised VM runtime upgrades have stalled.

Railway's API is rate-limited to 1000 requests/hour and tied to individual accounts or teams, making it inadequate for resellers managing multiple clients. The API schema is also optimized for single-tenant use rather than multi-tenant operations.

Railway's configuration system (railway.json) only allows configuring one service at a time, making it tedious and error-prone to deploy multi-service architectures—a now-standard deployment pattern that competitors like Render handle seamlessly.

Railway lacks a native worker model for background jobs, async processing, and independent scheduled tasks. Developers must manually set up these as separate services, requiring additional configuration and ongoing management.

Railway's deployment flow is tightly coupled to GitHub, and GitLab integration is only planned with no estimated timeline. Developers using GitLab must work around this significant platform limitation.

Railway explicitly does not support SSH access or remote shell (like `rails console`) to running applications, blocking Ruby and Elixir developers who need interactive debugging and introspection capabilities due to the platform's immutability principle.

Railway does not provide built-in edge protection, Web Application Firewall (WAF), or DDoS mitigation out of the box. Developers must add extra layers (CDN, proxy, WAF) manually if their apps need strong security or resilience against bot traffic.

Railway lacks support for certain geographic regions (e.g., Algeria), which creates compliance and performance issues for teams that need to serve or store data in specific locations.

Railway exhibits variable and slow response times, particularly on free or low-tier plans. Some developers report that migrating apps (e.g., Django) from Railway due to latency concerns, especially for users in geographically distant regions.

Railway's containerized model introduces significant cold start delays when scaling up from zero instances, affecting latency-sensitive APIs and applications with variable traffic patterns.

Platform architecture requires substantial backend work to support critical features like private networking. Fundamental infrastructure improvements are needed before implementing features that enterprise customers require.

As a managed PaaS, Railway abstracts away infrastructure choices, limiting control over custom network topologies, specialized hardware, and OS-level tuning required by some organizations.

Custom plugins and database integrations are not supported and will take considerable time to implement. The team acknowledges this is a complex undertaking (Figma took six years) with no clear timeline for Railway.

Railway lacks native Android and iOS apps, creating a gap in the mobile-first developer workflow. As AI-powered coding tools (e.g., Cursor, Copilot) enable mobile development, Railway cannot support the end-to-end mobile deployment lifecycle.

Railway does not provide project migration functionality, making it difficult to reorganize or consolidate projects without manual workarounds.

Railway's interface and abstraction model are developer-centric, making it difficult for non-technical team members to navigate templates, integrations, and configuration without deep technical knowledge.

The template search feature in the Railway UI has poor performance, taking 3-4 seconds to search on reasonably powerful machines, causing unnecessary delays during project initialization.