​​Railway alternatives for AI agent sandboxes and cloud code execution in 2026

You deployed your first coding agent on Railway because shipping took minutes. Push to Git, get a URL. Then the workload changed. The agent now spins up dozens of short-lived environments per session. It sits idle waiting on large language model (LLM) responses. It also executes code no human reviewed.

Railway offers two separate product surfaces. Railway Services run containerized applications, APIs, workers, and databases. Railway Sandboxes are short-lived, programmatically controlled VMs for code execution. They are available through Priority Boarding, with breaking changes possible and plan-based concurrency limits.

Railway's Serverless feature applies to Services, not Sandboxes. After 10 minutes without outbound traffic, a Service may sleep. The first request wakes it and can experience a cold-boot delay. Railway also notes that slept Services may, in remote cases, require a rebuild.

Railway has experienced outages tied to hard Google Cloud dependencies. Those dependencies sit in its control plane. Railway's official incident report documents that risk. Production agent workloads tend to outgrow these constraints. App hosting on Railway can still work fine.

This guide covers five Railway alternatives for AI agent sandboxes and cloud code execution. Each review compares isolation, standby behavior, pricing, networking, SDK coverage, and production limits.

TL;DR:

• Railway's sweet spot: Railway is strong for hosting applications and databases but has limitations for AI agents.
• Blaxel for coding agents: Blaxel offers a first-class solution for coding agents with fast standby and isolated environments.
• Fly.io for DIY teams: Fly.io provides raw infrastructure control but requires manual agent orchestration.
• Daytona for breadth: Daytona excels with broad SDK coverage and GPU passthrough capabilities.
• Modal for GPU Python: Modal is tailored for Python teams needing GPU inference within their agent workflow.

1. Blaxel

Blaxel infrastructure is the infrastructure for autonomous agents. It is the execution layer for AI agents that run code in production. Railway adapts a general Platform as a Service (PaaS) to agent workloads. Blaxel built its sandbox lifecycle, networking, and APIs around agents from inception.

Blaxel is a first-class sandbox provider in the OpenAI Agents SDK. When configured for an agent run, Blaxel Sandboxes provide the isolated compute environment for executing commands and working with files.

Key features

Blaxel's core features focus on stateful execution, idle cost control, and production networking:

• Perpetual standby: Blaxel keeps full filesystem and memory state in standby. Standby has zero idle compute cost.
• Sub-25ms resume: Sandboxes resume from standby in under 25 milliseconds. They return with their exact previous state.
• Network-based auto-shutdown: Sandboxes drop to standby after roughly 15 seconds of network inactivity. Teams don't manage the lifecycle manually.
• Agent Drive: Agent Drive, in private preview, is a distributed filesystem. Multiple sandboxes can mount it with concurrent read-write access.
• Production networking: Custom domains, dedicated egress gateways in private preview, and proxy secrets injection support production agent traffic.

These features matter when agents pause often but still need their environment back immediately.

Pros

• Blaxel's strongest advantages apply to coding, pull request review, and data analysis agents. Its lifecycle was built for agent code execution rather than adapted from app hosting. Each sandbox gets microVM isolation with its own kernel. AI-generated code can't escape to the host through a shared kernel.
• Preview URLs let users see generated code as it builds. That fits coding agents that render apps during a session. The Build0 case study reports an 80% sandbox cost reduction after switching. Blaxel also supports Health Insurance Portability and Accountability Act (HIPAA) compliance through a Business Associate Agreement.
• These strengths point to production agent workloads, not generic web application hosting.

Cons

• Blaxel has a narrower language story than some platforms in this list. First-class SDKs cover Python, TypeScript, and Go. Other languages integrate through the REST API. Teams on less-common languages must use REST calls instead of a native SDK.
• These tradeoffs matter most when the host application is Ruby, Java, or Rust.

Pricing

Where possible, figures below reflect roughly 1 vCPU with 2 GB RAM. Providers bill on different units.

Free: Up to $200 in free credits plus usage costs. Pre-configured sandbox tiers and usage-based pricing: See Blaxel's pricing page for the most up-to-date pricing information. Available add-ons: Email support, live Slack support, HIPAA compliance.

Blaxel doesn't publish a directly comparable 1 vCPU and 2 GB hourly rate. Compare total cost using active time, standby time, snapshot storage, and add-ons. Blaxel's pricing model is most useful when idle time dominates active execution time.

Who is Blaxel best for?

Blaxel fits teams running coding agents in production. These agents need isolated execution, stateful sessions, and fast previews. PR review agents are the next-best fit. Reviews happen sporadically during the day. Data analyst agents executing generated Python and SQL are a third strong fit.

2. Fly.io

Fly.io platform runs Docker containers as Firecracker microVMs across a broad global footprint. Fly Machines boot in about 300ms. Fly markets them for "even the sketchiest user-generated (or LLM-generated) code." In July 2026, Fly closed a $25 million Series D funding round. It also installed a new CEO. The company announced a strategic shift toward "Computers for Agents."

Key features

Fly.io gives teams low-level primitives for building their own agent infrastructure:

• MicroVM runtime: Firecracker microVMs provide roughly 300ms boots. They also support sub-second start or stop behavior.
• Suspend and resume: Suspend and resume preserves memory state. It returns in hundreds of milliseconds. Snapshots are discarded during new deployments.
• Private networking: WireGuard networking uses 6PN, Fly.io's private IPv6 network, with internal DNS.
• Per-second billing: Started Machines bill per second while they run.

Fly's primitives are flexible, but teams own the agent-specific orchestration above them.

Pros

• Fly.io works well for teams that want infrastructure control. Firecracker isolation suits hostile or AI-generated code. Fly Volumes persist across stop, start, suspend, resume, and restarts. Fly.io also has a wide regional footprint for placing compute near users.
• These strengths fit teams with infrastructure capacity to build missing agent platform pieces.

Cons

• Fly.io's limitations matter when teams need managed agent infrastructure rather than raw Machines. Community reports show capacity failures in certain data centers. In those cases, Machines can't be placed where teams want them.
• Billing has several surfaces. Charges include $0.15/GB/month for stopped-machine rootfs. Rootfs means the stopped Machine root filesystem. Volume snapshot charges add another surface.
• Fly.io gives control, but that control comes with more capacity and cost management work.

Pricing

As of July 2026, a shared-cpu-1x Machine with 256 MB costs $1.94/month. It is billed per second. Performance-1x with 2 GB costs $31/month. That performance-1x size is the closest baseline match. Volumes cost $0.15/GB/month whether the Machine runs or not. There's no free tier for new users.

Who is Fly.io best for?

Fly.io suits teams that want raw, fast-launching microVMs. Those teams must build orchestration themselves. That includes routing, idle logic, log streaming, and filesystem checkpoints.

3. Daytona

Daytona sandboxes repositioned from developer environments to AI agent sandboxes during 2025. It raised a $24 million Series A led by FirstMark in February 2026. Container sandboxes create in under 90 milliseconds. Dedicated Linux VM and Windows options are also available. In June 2026, Daytona moved its production codebase to closed source.

Key features

Daytona combines fast container creation with broader language SDK coverage:

• Fast creation: Container sandbox creation completes in under 90ms.
• Default lifecycle: Auto-stop happens after 15 minutes of inactivity. Container sandboxes auto-archive after 7 days.
• GPU passthrough: GPU sandboxes use full Virtual Function I/O (VFIO) passthrough. Each sandbox gets one dedicated physical GPU.
• SDK coverage: SDKs cover Python, TypeScript, Ruby, Go, and Java.

Daytona's feature set fits teams that value SDK breadth or dedicated GPU passthrough.

Pros

• Daytona's advantages center on language coverage and GPU access. It supports five languages, plus REST and CLI access. GPU passthrough gives each sandbox a physical GPU without shared tenancy. Daytona also provides $200 in free compute credits without a credit card.
• These strengths are useful when agents need Ruby, Go, Java, or dedicated GPU passthrough.

Cons

• Daytona's tradeoffs come from its closed-source transition and open issue history. The previously public Daytona repository says it is no longer maintained. Open issues document template failures and GitHub connection errors.
• Package installation also deserves testing. An open issue documents apt-get failures inside sandboxes. The Python SDK has a serialization bug affecting snapshot JSON output. Open issues also flag authentication gaps for DockerHub and GitHub Container Registry workflows.
• These limitations deserve testing when package installation and template reliability sit in the agent path.

Pricing

As of July 2026, vCPU costs $0.0504/hour. Memory costs $0.0162/GiB/hour. Storage costs $0.000108/GiB/hour, with the first 5 GiB free. At the 1 vCPU and 2 GB baseline, active compute costs about $0.083/hour. H100 GPUs run $3.95/hour. Enterprise plans with single sign-on (SSO) go through sales. Audit logs and bring your own cloud (BYOC) also go through sales.

Who is Daytona best for?

Daytona fits teams that need GPU passthrough or SDKs in Ruby, Go, or Java. The $200 in free compute credits require no credit card. That lowers the trial barrier. H100 GPUs run $3.95/hour for teams that need dedicated passthrough.

Teams running untrusted code for many tenants should weigh the container-default isolation carefully. Buyers with enterprise compliance gates should note Daytona holds Service Organization Control, or SOC 2 Type I. Daytona's Trust Center also lists ISO/IEC 27001 among its compliance items.

4. Modal

Modal platform is a Python-native serverless GPU platform. Its Sandboxes product reached general availability on January 21, 2025. Sandboxes run on gVisor isolation, which intercepts syscalls in user space. It doesn't give each workload its own kernel. Real-kernel VM Sandboxes are in VM Sandboxes alpha. Modal raised a $355 million Series C in May 2026. The round valued Modal at $4.65 billion. Annualized revenue had passed $300 million.

Key features

Modal's sandbox features sit inside its Python and GPU workflow:

• GPU sandboxes: GPU-attached sandboxes run on Modal. H100 pricing is listed at $0.001097/second.
• Configurable lifetime: Sandbox lifetime is configurable up to 24 hours. Idle timeouts have existed since SDK 1.1.4.
• Snapshots: Filesystem snapshots support configurable TTL. Memory snapshots are in early preview and expire 7 days after creation.
• Egress control: Outbound domain allowlists control sandbox egress in SDK 1.5.0.

Modal is strongest when sandboxed execution and GPU inference live in the same Python stack.

Pros

• Modal's advantages matter most to Python teams with GPU-heavy workloads. GPU workloads and sandboxed code execution run on one platform. Sandboxes have been generally available since January 2025. The decorator SDK also gives Python teams a deep developer experience.
• These benefits fit inference-heavy agent stacks more than CPU-focused code execution fleets.

Cons

• Modal's tradeoffs reflect its GPU serverless orientation. Sandboxes bill at 3× standard Function rates because they force non-preemptible pricing. The decorator SDK also creates platform-specific code patterns. Moving workloads elsewhere requires rewriting serving logic.
• These constraints matter when the sandbox layer must remain portable across infrastructure providers.

Pricing

As of July 2026, sandbox CPU costs $0.00003942/core/second. Memory costs $0.00000672/GiB/second. At the 1 vCPU and 2 GB baseline, active compute maps to about $0.12/hour. That assumes 1 vCPU is treated as 0.5 physical core. It maps to about $0.19/hour if billed as a full physical core. The Starter plan is $0 with $30/month in credits. Team is $250/month with $100/month in credits. A HIPAA BAA is Enterprise-only.

Who is Modal best for?

Modal fits Python teams whose agents need GPU inference in the hot path. That is the workload Modal was built for. Sandbox lifetime runs up to 24 hours, with idle timeouts since SDK 1.1.4. Teams needing durable sandbox state face early-preview memory snapshots. They expire after 7 days. Snapshotting terminates the sandbox, and background processes aren't restored.

5. E2B

E2B sandboxes is a sandbox provider that runs Firecracker microVMs. It offers Python and JavaScript SDKs. It remains a notable AI sandbox reference point. E2B raised a $21 million Series A in July 2025. Its lifecycle model targets development-stage sessions. Sandboxes default to a 5-minute timeout. They pause at roughly 4 seconds per GiB of RAM. Pause and resume behavior returns in about 1 second.

Key features

E2B focuses on Firecracker isolation and development-stage persistence:

• MicroVM defaults: Firecracker microVM isolation starts with 2 vCPU and 512 MiB RAM defaults.
• Pause behavior: Pause saves filesystem and memory. Paused sandboxes are kept indefinitely with no TTL.
• Auto-resume: Supported activity can auto-resume sandboxes when memory snapshots are used.
• Egress lists: Egress lists support IPs, CIDR blocks, and hostnames through allowOut and denyOut.

E2B's model works well for short development loops that need hardware isolation.

Pros

• E2B's strengths are clearest for prototyping and development-stage agent sessions. Firecracker microVMs give each sandbox its own kernel. Paused sandboxes remain until explicitly killed.
• These strengths make E2B a common reference point during early agent infrastructure evaluation.

Cons

• E2B's limitations show up around duration caps, minimum spend, and SDK reliability. Hobby allows 1 hour, and Pro allows 24 hours. Issue #873 says sandboxes cannot run longer than 24 hours. Pro costs $150/month before compute usage. It also has a 100-concurrent-sandbox cap, versus 20 on Hobby.
• Reliability issues deserve attention before production use. Issue #884 describes filesystem changes lost after subsequent resumes. Open issues cover HTTP/2 errors, Retry-After loss, and commands.run hangs. Issue #1301 says JS SDK uploadFile buffers the build context in memory.
• These risks matter when a prototype moves into higher-concurrency production usage.

Pricing

As of July 2026, Hobby is $0 with $100 in one-time credits. Pro is $150/month with custom CPU and RAM sizing. Compute costs $0.000014/second per vCPU. RAM costs $0.0000045/GiB/second. At the 1 vCPU and 2 GB baseline, active compute runs about $0.083/hour. That compute charge sits on top of the Pro floor.

Who is E2B best for?

E2B suits teams prototyping agents and iterating in development. Its short session windows match that workflow. Enterprise buyers should note that E2B's official documentation doesn't appear to publicly list SOC 2 information. The absence of common compliance materials can slow procurement with regulated customers.

Railway alternatives at a glance

The table below compresses the platform reviews into four comparison dimensions. Use it to shortlist options before running workload tests.

The main split is managed agent infrastructure versus raw compute primitives. The right choice depends on how much orchestration your team wants to own.

How to choose among Railway alternatives for AI agent sandboxes

Match the execution layer to your workload's isolation and idle-cost profile. Agents that run untrusted, AI-generated code in production need hardware-enforced isolation. Agents that pause between user turns need state that survives idle periods. They also need to avoid compute charges while waiting.

Blaxel is the infrastructure for autonomous agents. Instead of session-based environments that expire, Blaxel provides the execution layer agents depend on to run, connect, and operate at scale. Isolated micro-VMs boot in milliseconds, auto-suspend when idle, resume in 25 milliseconds, and persist state indefinitely.

Speed, persistence, networking, and granular control ship as native capabilities of the runtime itself, not features layered on top. Predictable economics are the natural outcome of infrastructure designed to scale to zero and resume instantly. Nielsen pegs 100ms as the ceiling for a response that feels instant. Sub-25ms resume stays well under that threshold.

Beyond sandboxes, the Blaxel platform includes Batch Jobs for parallel fan-out processing, Volumes for guaranteed long-term persistence, and Agent Drive in private preview for shared context across agents and sessions. Start by measuring what your agents cost on Railway today. Include idle time, cold starts, and rebuild behavior.

Then run the same workload on the strongest shortlisted platform. If the workload needs persistent state, fast resume, and microVM isolation, evaluate Blaxel with free credits. You can also request a technical architecture review.

FAQs

Can Railway Sandboxes run production AI agent workloads?

Not yet with production guarantees. Railway Sandboxes are in Priority Boarding beta. The docs state plainly that breaking changes may occur. Concurrent sandboxes are hard-capped at 10 on Free, 50 on Hobby, and 100 on Pro. The Railway Sandboxes cap constrains high-concurrency agent fleets. Agents that need stable APIs, higher concurrency, and contractual reliability should evaluate a purpose-built alternative.

Why does microVM isolation matter for AI-generated code?

Nobody reviewed AI-generated code before execution. Containers share the host kernel. A kernel exploit inside one workload can reach the host and neighboring tenants. MicroVMs like Firecracker run a separate kernel per sandbox. An exploit stays contained inside that boundary. gVisor sits between containers and microVMs. It intercepts syscalls in user space with stronger isolation than containers. MicroVMs provide hardware-enforced boundaries.

How does idle billing differ across these platforms?

Fly.io charges $0.15/GB/month for stopped-machine roots. Daytona leaves instances running 15 minutes before auto-stop. Modal's memory snapshots are early preview with a 7-day expiry. E2B keeps paused sandboxes at no compute charge. You are only billed while sandboxes are running. The Pro plan at $150/month mainly raises maximum continuous runtime from 1 hour to 24 hours. Blaxel suspends after about 15 seconds. It charges only snapshot storage during standby.

Which Railway alternative is best for coding agents?

For production coding agents that need persistent state and fast resume, Blaxel is the strongest fit. Coding agents need secure code execution, real-time previews, and state that survives user pauses. Perpetual standby means a user's project can resume later without re-cloning everything. Modal is the better fit when the agent's core loop is GPU inference rather than code execution.