Fly.io Sprites Alternatives for AI Agent Sandboxes

Production coding agents need more than a sandbox. They need state that survives sessions, fast resume for live interaction, and co-located hosting to cut tool-call latency. Fly.io Sprites launched in January 2026 with persistent Linux VMs and KVM-based isolation.

It ships 100 GB sparse NVMe storage and checkpoint-and-restore as a first-class primitive. Idle Sprites bill only for cold storage at $0.02/GB-month. For async coding agents that don't blink at a one-second cold start, Sprites changed the conversation.

Teams running interactive agents or multi-tenant production fleets hit limits quickly. Restore latency lands in the second range. VM configuration is narrower than dedicated platforms, and there's no integrated agent hosting. Agent logic still has to live somewhere else.

This guide covers where Sprites falls short for production agent workloads. Then it walks through five alternatives that close those gaps differently: Blaxel, E2B, Modal, Vercel Sandbox, and Cloudflare Containers.

Where Fly.io Sprites falls short for production AI agents

Sprites gets several things right. KVM-based isolation gives each Sprite a hardware-virtualized boundary. The sparse NVMe volume persists data across sessions, and checkpoint-and-restore treats state capture as routine rather than an escape hatch.

Model Context Protocol (MCP) support at sprites.dev/mcp lets agents create, checkpoint, and manage Sprites through tool calls. Fly.io's security overview lists SOC 2 Type 2, a HIPAA BAA on request, and ISO 27001-certified datacenters. Sprites-specific audit scope isn't yet confirmed.

The core idea that agents deserve "real computers" is compelling. The limitations below show up once teams move from prototype to production.

Restore speed can miss real-time interaction targets

Sprites restore from checkpoint in roughly a second, and initial creation takes about one to two seconds. For async coding agents reviewing pull requests in the background, that's fine. Interactive agent experiences are a weaker fit.

Jakob Nielsen's usability research established 100ms as the ceiling where users feel a system is reacting instantaneously. That's a general UX reference, not a direct sandbox benchmark. Still, it explains why second-range restore delays feel noticeable in live coding assistants and interactive data analysis workflows.

Narrower VM configuration than dedicated sandbox platforms

Sprites bills only for what gets used. The published memory range is narrower than dedicated sandbox platforms with explicit tier selection. Tier-based platforms give teams wider configuration options without sales calls.

No co-located agent hosting or integrated agent stack

Sprites provides the sandbox while the agent runtime lives elsewhere, on your own infrastructure or with a separate provider. The Sprite network uses an fdf:: IPv6 prefix on spr0, separate from Fly's standard 6PN fdaa:: range. Every tool call adds network roundtrip latency. The platform also lacks MCP server hosting, batch job infrastructure, and a unified model gateway. Teams building complete agent products end up stitching multiple providers together.

Sandbox-focused scope without production agent tooling

Sprites excels at persistent sandboxed execution. But coding agents, PR review agents, and similar production workloads need persistent execution plus hosting. A sandbox alone often isn't enough. The platform lacks co-located hosting to cut tool-call latency. It also lacks batch job processing for background work and a unified model gateway for LLM access. Even file transfer requires a workaround: running WebDAV inside the Sprite, since there's no native file transfer. Teams building multi-tenant agent platforms end up adding infrastructure layers around Sprites.

Fly.io Sprites alternatives at a glance

The five platforms below address Sprites' production gaps through different architectural choices. Some share the persistent-VM philosophy; others take an ephemeral approach with faster creation. The table maps core capabilities against the limitations covered above.

The main tradeoff is persistence and agent-specific infrastructure versus faster ephemeral creation and ecosystem integration.

1. Blaxel

Blaxel is a perpetual sandbox platform using Firecracker microVMs. Sandboxes resume from standby in under 25ms, stay in standby indefinitely, and cost nothing in compute while idle. Like Sprites, Blaxel treats sandboxes as persistent environments where agents get a real computing runtime. Unlike Sprites, Blaxel pairs sandboxes with Agents Hosting, MCP Servers Hosting, Batch Jobs, and a Model Gateway. All run on the same backbone. Teams building complete agent products don't stitch providers together.

Blaxel is also a first-class sandbox provider in the OpenAI Agents SDK. The SDK builds on OpenAI's Codex harness, with Blaxel Sandboxes handling the execution layer.

Key features

• Sub-25ms resume from standby: Sandboxes resume from standby in under 25ms with complete filesystem and process state preserved. Initial creation from a template takes 200 to 600ms. This favors live coding assistants and PR review workflows where second-range delays break the experience.
• Perpetual standby: Sandboxes enter standby with filesystem and process state preserved indefinitely. Resume via saved sandbox ID with no checkpoint management overhead, and standby has no upper time bound.
• MicroVM isolation: Firecracker-based microVMs (the same technology behind AWS Lambda) give each sandbox its own kernel with hardware-enforced boundaries.
• Co-located agent hosting: Deploy agent logic on the same infrastructure as sandboxes. That removes the extra network hop Sprites cannot avoid.
• MCP Servers Hosting: Pre-built MCP integrations and support for custom MCP servers, deployed on the same infrastructure as sandboxes and agents.
• Batch Jobs: Parallel execution of background tasks for fan-out async workloads where boot latency is less critical than throughput.

Pros and cons

Pros:

• Sub-25ms resume keeps interactive agents inside the threshold where users perceive instant response
• Co-located agent hosting removes the network hop that sandbox-only platforms can't avoid
• Full stack in one platform: Sandboxes, Agents Hosting, Batch Jobs, MCP Servers Hosting, and Model Gateway
• Sandboxes return to standby after 15 seconds of network inactivity, with no manual lifecycle management
• SOC 2 Type II, ISO 27001, and HIPAA BAA available

Cons:

• CPU-focused infrastructure does not currently offer GPU-enabled sandboxes for inference or training
• Supports Python, TypeScript, and Go SDKs; agent hosting supports Python and TypeScript only
• No air-gapped deployment; on-premise limited to private endpoint connectivity and bring-your-own-metal

Who Blaxel is best for

Teams building production coding agents, PR review agents, and data analysis agents that need more than a sandbox. Webflow, Vybe, and Strapi run coding agents on Blaxel. Delty and Jazzberry use it for PR review. The pull is persistent sandboxes paired with co-located hosting in one platform.

Pricing

• Free credits are available to get started
• Pre-configured sandbox tiers with usage-based pricing. See Blaxel's pricing page for current rates.
• Available add-ons: email support, live Slack support, HIPAA compliance

2. E2B

E2B is an open-source AI sandbox platform using Firecracker microVMs with fast boot times. E2B takes an ephemeral-first approach with fast template-based creation, SDKs in multiple languages, and a dedicated MCP gateway.

Key features

• Firecracker microVMs: Same hardware-level isolation model as Blaxel. An OverlayFS disk architecture enables efficient template-based provisioning.
• Fast boot: Sandboxes are created on demand from templates or snapshots rather than maintained as always-on persistent VMs.
• Python and TypeScript SDKs: Core SDKs with OpenAI Agents SDK integration (Python only currently, TypeScript forthcoming).
• MCP gateway: First-class MCP support with a quickstart, available servers, and custom template/server options.
• Persistence via pause: Sandboxes can be paused and resumed with filesystem and process state preserved. Auto-pause available on timeout.

Pros and cons

Pros:

• Fast creation from templates
• Open-source infrastructure repo on GitHub
• MCP gateway with multiple server options
• Pause and resume for state preservation
• Bring your own cloud (BYOC) and self-hosted options
• High concurrency limits on Pro with add-ons

Cons:

• Wall-clock billing means you pay full CPU rate during I/O wait, including LLM response time and external API calls
• SOC 2, ISO 27001, and HIPAA are not confirmed in E2B's public documentation
• No co-located agent hosting
• Session duration is more constrained than Sprites' persistent model

Who E2B is best for

Teams that prefer fresh-start sandboxes over Sprites' persistent model and want faster creation and a broader SDK ecosystem. Strong fit for evaluation pipelines and short-lived sessions where the agent builds a fresh environment each time. Less suited for long-lived stateful workflows, I/O-heavy agents (where wall-clock billing inflates costs during LLM waits), or teams that need SOC 2 in procurement.

3. Modal

Modal is a serverless compute platform with native GPU support and a Python-first developer experience. It provides GPU compute for agents running inference or training alongside code execution.

Key features

• GPU-native compute: A100 and H100 (40 GB and 80 GB variants) available in Sandboxes at standard GPU rates. Also B200, H200, L40S, and others. Up to eight GPUs per container.
• Python-first: Decorator-based deployment with the primary SDK in Python. JavaScript and Go SDKs in alpha.
• gVisor isolation: System call interception at the userspace level, different from Sprites' KVM and the Firecracker microVMs used by E2B and Vercel. Each compute job is containerized and virtualized through gVisor.
• Sandbox lifecycle: Configurable timeout up to 24 hours per run. Filesystem Snapshots recommended for longer workloads.

Pros and cons

Pros:

• Native GPU support for inference and training inside sandboxes
• Python-native developer experience
• SOC 2 on all plans
• Volumes v2 (beta) with concurrent writes from hundreds of containers
• Recurring free compute credits on the Starter plan

Cons:

• gVisor provides userspace syscall interception, not hardware-enforced VM isolation, which matters for teams with strict isolation requirements
• Sandbox runs are time-bounded
• Sandbox compute rates are approximately 3x higher than standard function rates
• HIPAA BAA available on Enterprise only
• No co-located agent hosting
• No MCP support

Who Modal is best for

Teams whose agents need GPU capabilities for inference or training alongside code execution. If your coding agent generates code and then runs a local model for evaluation, Modal handles both workloads. Less suited for teams prioritizing hardware-level VM isolation, or for persistent stateful environments beyond a single bounded run.

4. Vercel Sandbox

Vercel Sandbox runs Firecracker microVMs with millisecond startup, Active CPU pricing, and snapshotting. It uses the same isolation technology as E2B and is optimized for shorter-lived execution with strong billing efficiency for I/O-heavy agents.

Key features

• Firecracker microVMs: Each sandbox runs inside its own Firecracker microVM with an isolated filesystem and network. Code inside a sandbox can't access host environment variables or database connections.
• Millisecond startup: Vercel documents sandbox startup as "in milliseconds" without publishing a precise number.
• Active CPU pricing: CPU billed only during active execution, not during I/O wait. For agents spending significant time waiting on LLM API responses, this saves versus wall-clock billing.
• Snapshotting and persistent sandboxes: Snapshotting is documented as a production feature, though Vercel does not explicitly label it as GA in the cited sources. Persistent Sandboxes, which auto-save state on stop and resume without manual snapshot management, are in beta.
• Credential brokering: Credentials injected on egressing traffic outside the sandbox boundary. API keys never enter the sandbox scope. Available on Pro and Enterprise plans.

Pros and cons

Pros:

• SOC 2 Type II confirmed
• Active CPU billing excludes I/O wait
• Credential brokering via egress proxy prevents key exfiltration
• Open-source SDK
• High concurrency limits on Pro

Cons:

• Session duration is capped on standard plans, while Sprites offers a more persistent filesystem model
• Persistent Sandboxes still in beta
• Memory billed at full wall-clock rate; only CPU gets the Active billing treatment
• No co-located agent hosting
• No MCP support
• ISO 27001 and HIPAA are not confirmed in Vercel's official documentation cited here

Who Vercel Sandbox is best for

Teams already in the Vercel ecosystem that need Firecracker-level isolation for ephemeral execution. The pricing model favors I/O-heavy agents and shorter coding-agent sessions. Less suited for long-lived persistent agent environments or teams that need MCP integrations.

5. Cloudflare Containers

Cloudflare Containers is an edge-first container platform with placement spanning Cloudflare's global network, Active CPU pricing, and a Sandbox SDK. The isolation model differs from Sprites: containers are backed by Durable Objects with an unspecified underlying isolation technology, versus KVM-based VMs. Global reach and deep ecosystem integration are the main draw.

Key features

• Global edge network: Workers deploy across many cities. Containers use a Region:Earth placement model where Cloudflare automatically places each instance in the optimal location.
• Active CPU pricing: CPU billed at actual utilization percentage. Charges stop when a container goes to sleep.
• Sandbox SDK: TypeScript SDK (@cloudflare/sandbox) with filesystem operations, process management, and container lifecycle control.
• Deep ecosystem: R2 storage via FUSE mount, Durable Objects (core to the container architecture), Workers AI, and Agents SDK integration. Outbound traffic can be intercepted or blocked via enableInternet for sandboxing.

Pros and cons

Pros:

• Massive global edge network for low-latency placement
• No stated hard session cap in the cited docs (configurable sleepAfter property)
• Active CPU billing at actual utilization percentage
• Cloudflare maintains SOC 2 Type II reports for in-scope services and ISO 27001 covering its platform
• Multiple instance types from lite to larger standard tiers
• Rich ecosystem with R2, Durable Objects, and Workers AI

Cons:

• Isolation model undocumented at the technology level: no mention of microVM, gVisor, or shared kernel in official docs
• Security boundary around the Durable Object backing each container instance is unspecified
• Filesystem is ephemeral on sleep: all disk resets to the container image on wake, and filesystem snapshots are listed as "coming soon"
• Requires a paid Workers plan as a baseline
• Layered billing can complicate cost modeling
• No MCP support

Who Cloudflare Containers is best for

Teams already invested in the Cloudflare ecosystem (R2, Workers, Durable Objects) that need global edge deployment for ephemeral execution. Active CPU pricing favors I/O-heavy agents that run close to end users across many regions. Less suited for teams requiring hardware-level isolation guarantees, or MCP integrations. Blocker for teams requiring persistent stateful environments (the filesystem resets on sleep).

How to choose the right Fly.io Sprites alternative

Map your primary Sprites pain point to the Blaxel capability that addresses it most directly.

Prioritize resume speed for real-time interactions

Sprites' second-range checkpoint restore feels noticeable in live coding assistants and interactive data analysis. Blaxel resumes from standby in under 25ms with full filesystem and process state preserved. That keeps interactive agents inside the Nielsen 100ms threshold where users perceive instant response, with no checkpoint management overhead.

Stop paying for idle compute between agent invocations

Sprites bills storage on idle VMs while keeping checkpoint state ready. Blaxel goes further: sandboxes return to standby after 15 seconds of network inactivity and stay there indefinitely with zero compute cost. Filesystem, memory, and running processes resume exactly where they stopped, no snapshot lifecycle to manage.

Run untrusted AI-generated code in a multi-tenant platform

Sprites uses KVM, which gives hardware isolation per VM. Blaxel uses the same hardware isolation tier with Firecracker microVMs (the technology behind AWS Lambda) and adds SOC 2 Type II, ISO 27001, and a HIPAA BAA out of the box. For platforms running untrusted code from many tenants, that compliance posture clears procurement faster.

Build a full agent product beyond sandbox isolation

Blaxel is the only platform here that pairs perpetual sandboxes with a co-located agent stack: Agents Hosting, MCP Servers Hosting, Batch Jobs, and Model Gateway on the same backbone. Teams building coding agents or PR review agents adopt this shape to avoid the multi-provider stitching that Sprites requires for those workloads.

Ship enterprise-ready infrastructure without DIY tooling

Sprites' product-specific audit scope isn't yet confirmed, and features like custom domains, dedicated egress IPs, and secrets injection are DIY on Fly's primitives. Blaxel ships managed custom domains, dedicated egress gateways in private preview for static outbound IPs, and secrets injection via proxy routing. Enterprise procurement teams get the documentation and controls they need without back-and-forth.

Move past Fly.io Sprites for production AI agents

Production agents on Sprites accept second-range restore latency, DIY snapshot lifecycle, and a sandbox without an agent stack. For async PR review during development, that's manageable. For interactive coding assistants, multi-tenant agent platforms, or anything where users wait on the response, those gaps compound into product-quality problems.

Blaxel closes those gaps. Sandboxes resume from standby in under 25ms with full filesystem and process state preserved. Perpetual standby costs nothing in compute while idle. Firecracker microVMs give each workload hardware-enforced isolation. The full agent stack runs on the same backbone: Agents Hosting, MCP Servers Hosting, Batch Jobs, and Model Gateway.

Book a demo to see Blaxel in production, or start building for free with free credits.

Frequently asked questions

What's the difference between Fly.io Sprites and a dedicated AI sandbox platform?

Sprites are persistent Linux VMs with KVM-based isolation, designed as general-purpose "real computers" for agents. Dedicated sandbox platforms add agent-specific tooling like faster resume, co-located agent hosting, MCP server hosting, and batch jobs. Sprites give you the VM. Dedicated platforms give you the VM plus the surrounding infrastructure agents need in production.

How fast does an AI agent sandbox need to resume?

For interactive coding assistants and live data analysis, sub-100ms resume keeps the experience feeling instantaneous. That matches Jakob Nielsen's UX threshold. Async PR review agents tolerate one to two seconds without breaking the experience. Background batch agents tolerate longer. Match resume speed to whether a human waits on the response or the agent runs unattended.

Why do production AI agents need microVM isolation instead of containers?

Containers share the host kernel, so a kernel exploit from one tenant's code can reach others. AI-generated code is untrusted by definition, and container escape vulnerabilities are documented in CVE databases. MicroVMs run each workload in its own kernel with hardware-enforced boundaries. For multi-tenant agent platforms running untrusted code, that boundary matters more than container speed advantages.

What is perpetual standby and why does it matter for AI agents?

Perpetual standby keeps a sandbox dormant indefinitely with filesystem and process state preserved, charging zero compute while idle. Blaxel offers this; other sandbox providers cap standby at 7 to 30 days or delete sandboxes. Agents with sporadic traffic patterns like PR reviews, coding sessions, and data analysis benefit most. Standby preserves cloned repos and warmed processes between invocations without paying for idle time.

When does an AI agent sandbox need GPU support?

Agents need GPU sandboxes when they run reinforcement learning or model training inside the sandbox itself. A coding agent that generates Python and runs a small evaluation model locally needs GPU compute. Most production coding agents send inference to a model API and run only CPU-bound code in the sandbox. Tests, builds, and scripts perform better on CPU-focused infrastructure.

For teams that need production agent infrastructure beyond sandbox isolation, Blaxel combines sub-25ms resume from standby, perpetual standby with zero compute cost while idle, microVM isolation, and a co-located agent stack in one platform. Book a demo to see it in production, or start building free with free credits.