Fly.io vs E2B

Architecture: Edge Platform vs Agent Sandbox

Fly.io and E2B start from opposite ends of the infrastructure stack. Fly.io is a full application platform — you deploy web servers, databases, background workers, and APIs on globally distributed hardware-virtualized Machines. Its value proposition is latency reduction through geographic proximity: your app runs in the region closest to each user. This makes it ideal for the agentic web, where AI agents making real-time decisions need low-latency access to both compute and data.

E2B, by contrast, is not an application platform at all. It is an execution layer — a place where AI agents send code to be run in isolation. Each E2B sandbox is a lightweight Firecracker microVM that spins up in roughly 150 milliseconds, executes code, and can be destroyed without side effects. The platform is designed for the specific pattern of LLM-generated code execution, where safety and ephemerality are paramount.

The launch of Fly.io Sprites in January 2026 blurred this boundary. Sprites are stateful sandbox VMs with persistent filesystems and checkpoint/restore capabilities — features clearly aimed at the AI agent use case. However, Sprites lack the native agent framework integrations and SDK ergonomics that make E2B a drop-in solution for agent developers.

Developer Experience and SDK Ecosystem

E2B wins decisively on developer experience for AI agent builders. Its Python and JavaScript SDKs let developers spin up a sandbox, execute code, read files, and tear down the environment in a few lines of code. The Code Interpreter SDK further simplifies common patterns like data analysis and vibe coding workflows. E2B integrates natively with LangChain, CrewAI, the OpenAI Agents SDK, and other major agent frameworks — meaning agent developers can add sandboxed execution without building any infrastructure glue.

Fly.io's developer experience is optimized for application deployment, not agent orchestration. Its CLI tool (flyctl) and Machines API are powerful but general-purpose. Sprites introduced some agent-friendly features — including pre-installed skills that teach Claude how the sandbox works — but the integration surface is narrower. Developers using Fly.io for agent sandboxing will typically need to build more of the orchestration layer themselves.

Persistence and State Management

This is where Fly.io Sprites offer a genuine advantage. Each Sprite gets a 100GB NVMe filesystem that persists indefinitely between sessions, with checkpoint/restore completing in approximately 300ms. The last five checkpoints are mounted and directly accessible. For AI agents that need to maintain long-running project state — coding assistants working on multi-day tasks, for example — this persistent model eliminates the overhead of reconstructing state on each invocation.

E2B takes the opposite approach: sandboxes are ephemeral by default, with sessions lasting up to 24 hours. Sessions can be paused and resumed within their maximum duration, but there is no indefinite persistence. This design is intentional — for most code execution use cases, ephemerality is a feature, not a limitation. It ensures clean environments, prevents state drift, and simplifies security. But for use cases requiring continuity across sessions, E2B requires external state management.

Pricing and Cost Efficiency

Both platforms use per-second billing, but their pricing structures reflect different assumptions about workload patterns. E2B bundles RAM into its CPU price at approximately $0.05 per hour for a 1 vCPU sandbox, making costs predictable for short-lived execution tasks. Fly.io Sprites bill separately for CPU ($0.07/hour) and memory ($0.04375/GB-hour), with the advantage of zero charges during idle periods.

For high-volume, short-lived sandbox execution — the pattern most common in agentic AI applications — E2B's simpler pricing and faster cold starts translate to lower costs. For longer-running, persistent environments where idle time is significant, Fly.io's idle-when-inactive model can be more economical. The choice depends on workload duration and concurrency patterns.

Security Model and Isolation

Both platforms provide hardware-level VM isolation, but their security models serve different threat profiles. E2B is designed for the specific threat of untrusted AI-generated code execution. Every sandbox is fully isolated, cannot reach production systems, and is destroyed after use. This model directly addresses the safety requirements of the agentic economy: agents must be able to take real actions without risk.

Fly.io provides standard cloud VM isolation, which is robust but not specifically designed for the untrusted-code-execution threat model. Sprites add sandbox-style isolation, but the persistent filesystem model means that state can accumulate across sessions — a feature that is useful for development workflows but introduces a different security surface than E2B's clean-slate approach.

Enterprise Readiness and Ecosystem

E2B's enterprise traction is remarkable for a company of its size. With 88% of the Fortune 100 on its platform and partnerships with AI leaders like Hugging Face, Perplexity, and Manus, E2B has become the de facto standard for sandboxed agent execution. Its open-source core (Apache 2.0) reduces vendor lock-in concerns, and planned features like secrets vaults and fleet orchestration tools signal continued investment in enterprise needs.

Fly.io has strong adoption among startups and independent developers — a natural fit for its positioning as infrastructure for the creator economy. However, its enterprise features are more general-purpose, and it does not have the same depth of agent-specific tooling. For teams building AI agent infrastructure at scale, E2B offers a more targeted and proven solution.