Railway

Fly.io vs Railway: Deployment and Pricing Compared

A vendor comparison of two usage-based developer platforms, Fly.io and Railway, based on vendor pages and public repositories as of late July 2026. Railway is positioned as a repository-first, visual-canvas platform that auto-configures deployments, offers PR preview environments, and publishes named pricing tiers (Free, Hobby $5/mo minimum, Pro $20/mo minimum, Enterprise custom). Fly.io emphasizes CLI-driven control via flyctl and a documented catalog of infrastructure primitives (Managed Postgres, GPUs, Kubernetes, Sprites for agent workloads), pay-as-you-go billing, a pricing calculator, and paid add-ons including support from $29/mo and a HIPAA compliance package at $99/mo. Both platforms provide private networking, autoscaling, observability, and global deployments. The article presents feature matrices, pricing snapshots (as of 2026-07-29), and public repository activity measured on 2026-07-28 to help technical teams choose between workflow-first (Railway) and CLI-first (Fly.io) approaches.

Monetize MCP Servers in 10 Minutes

The post introduces mcp-billing-gateway, a reverse-proxy billing gateway that lets operators monetize MCP servers without changing their upstream code. The gateway handles authentication, per-call metering, Stripe subscription onboarding, prepaid credits, and x402 USDC payments on Base; it returns a 402 response when a caller lacks payment and forwards JSON‑RPC requests to the unchanged MCP server. The article provides a step-by-step walkthrough: register as an operator, register a server, configure pricing (per-call, subscription, or tiered), onboard callers, accept x402 crypto claims, and check revenue analytics. The author links to an interactive demo and SDK/demo repositories on GitHub and points to a live gateway endpoint hosted on Railway. Default revenue split is 85/15 (operator keeps 85%).

Modal CTO on Agent-Centric AI Infrastructure

Modal CTO Akshat Bubna discusses why AI agents require different infrastructure than traditional cloud stacks, describing Modal’s shift from developer experience to agent experience. The interview highlights Modal’s recent $355M Series C, its agent-focused primitives (sandboxes, elastic inference, GPU snapshotting, speculative decoding/DeFlash, Auto Endpoints), a 17-cloud capacity pool, and features for multi-node training, private IPv6 overlays and RDMA networking. Bubna explains autoscaling challenges for bursty inference and RL rollouts (which can require very large numbers of sandboxes), Modal’s open-source work on DeFlash/speculative decoding, and the company’s product focus on making frontier-level inference and agent deployment easier to adopt.