Blaxel vs Fly.io for AI agent sandbox deployment

Your coding agent works. Then a user leaves for an hour and returns to a dead session. An ephemeral root filesystem or failed suspend restore can force a cold start. The agent then re-clones the repo.

Compare these platforms across three areas: lifecycle and resume behavior, networking and integration effort, and cost. Fly.io ships general-purpose container infrastructure. Its per-user environment blueprint covers warm pools and per-tenant app isolation. Fly.io's Firecracker architecture explains its microVM model.

Blaxel's microVM architecture also uses Firecracker. Blaxel builds its sandboxes around perpetual standby. Filesystem and memory state remain available while the sandbox stays in standby. Standby has zero compute cost, but it isn't guaranteed durable storage.

This article compares platform behavior, pricing, and migration tradeoffs.

TL;DR:

• Shared foundation, different lifecycle: Both platforms use Firecracker microVMs, but Blaxel manages standby automatically while Fly.io requires manual or proxy-configured suspend.
• Blaxel for stateful agents: Blaxel preserves filesystem and memory in perpetual standby with sub-25ms resume and zero idle compute cost.
• Fly.io for existing stacks: Teams already on Fly.io get an incremental path through Sprites and keep existing tooling and billing.
• Cost model divergence: Fly.io bills while machines are started and charges root filesystem storage when stopped. Blaxel bills only active GB-seconds.
• Decision driver: Choose based on whether your agents need managed lifecycle and instant resume or general-purpose infrastructure you orchestrate yourself.

What is Fly.io?

The Fly.io platform is a public cloud service. It converts containers into Firecracker microVMs. Its core primitive is the Fly Machine, a single VM.

In January 2026, Fly.io launched Sprites. This separate agent-oriented product provides first-class checkpoint and restore.

What is Blaxel?

Blaxel is the infrastructure foundation for autonomous agents. Its sandboxes run on Firecracker-based microVMs. Filesystem and process access are available through REST API and SDKs. The platform snapshots filesystem and memory state during standby. Standby duration is unlimited, but Blaxel doesn't guarantee durable data persistence. Use Volumes for long-term data retention.

The compute products are Sandboxes and Batch Jobs. Storage includes Volumes and Agent Drive documentation, which is in private preview. Networking includes custom domains and dedicated egress gateways in private preview. The egress gateway documentation covers static outbound IPs. Blaxel also provides proxy secrets injection.

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

Head-to-head feature comparison

The Fly.io column distinguishes plain Fly Machines from Sprites where their behavior differs. Both vendors publish their own resume figures. These figures measure standby restore, not end-to-end sandbox readiness. Benchmark your own workload before choosing either platform.

Based on those criteria, pick Blaxel for production coding agents, pull request (PR) review agents, or data analysis agents. Those workloads need persistent state and instant resume. Teams with agent work inside an existing Fly.io app should stay put.

When Blaxel is the better choice

Blaxel wins when production agent execution needs state across sessions.

• Coding agents with long user sessions: Blaxel keeps filesystem and memory state available during perpetual standby. Returning users can continue without another repository clone.
• Fleets without lifecycle plumbing: Fly.io's blueprint recommends pre-provisioned warm pools because Machine creation isn't instantaneous. Blaxel sandboxes enter standby automatically after traffic stops.
• Programmatic control: Plain Fly Machines expose API execution and support commands through flyctl. Examples include fly machine run <image> [command] and fly machine exec. Startup command overrides provide another option. An HTTP server is required only when your app exposes an HTTP service. Blaxel exposes filesystem and process control through REST and SDKs.
• Multi-tenant secrets and egress: Blaxel's proxy secrets injection is in public preview. It resolves secrets server-side, so the sandbox never receives raw values. Domain filtering is also in public preview. Dedicated egress gateways are in private preview and control outbound traffic.

Together, these capabilities determine two metrics an engineering leader tracks. One is cost per idle session. The other is whether returning users wait for a cold rebuild.

When Fly.io may fit better

• Existing Fly.io investment: For some teams, agent execution is a side feature of an existing Fly.io app. In that case, switching costs outweigh the gain.
• Preserved tooling: An existing image build pipeline and CLI-driven deployments keep working.
• Co-located data: Managed Postgres runs within the same Fly.io organization.
• Language coverage tradeoff: Blaxel ships Python, TypeScript, and Go SDKs. Other host languages must call the REST API directly.

Teams already operating on Fly.io get an incremental path through Sprites:

• Fast creation and checkpointing: Sprites create in 1 to 2 seconds and checkpoint in approximately 300ms.
• Included storage and idle metering: They carry 100 GB of storage and meter idle time automatically.
• Consistent operations: Existing Fly.io teams keep the same CLI and invoice.

Plain Machines and Sprites each carry tradeoffs:

• Plain Machines lack a checkpoint workflow: Plain Machines support command execution, but they lack Sprites' first-class checkpoint workflow.
• Custom plumbing for PR review agents: A PR review agent on plain Machines requires custom snapshot scheduling, restore verification, and testing.
• No Dockerfile support on Sprites: Sprites provide that workflow but don't accept Dockerfiles, so existing image pipelines need another build path.
• Smaller organizational change: Fly.io-centered billing and on-call practices can still make Sprites the smaller change. That may matter more than adopting a new vendor.

Pricing comparison

Fly.io billing meters usage per second without a usage-plan minimum. Blaxel bills active compute by GB-second with no base subscription. Its pricing page provides current rates and available tiers.

The comparison below uses 2 GB RAM. Fly.io's configuration includes 1 shared vCPU, while the Blaxel rate doesn't establish an equivalent 1-vCPU allocation. Treat this as a memory-matched comparison, not an exact CPU match. The table covers plain Fly Machines and excludes Sprites pricing.

On Fly Machines, you call suspend or configure Fly Proxy automation. Blaxel starts standby automatically and stops compute billing. This lifecycle difference matters most for bursty sessions.

For example, Build0 cut sandbox costs by 80 percent after switching. The Build0 case study describes the result.

Key differentiators and objection handling

Two objections come up frequently. One concerns the shared Firecracker primitive. The other concerns Fly.io's lower per-second rates. Each objection is valid within a specific workload context.

"Both platforms use Firecracker, so isolation is a wash"

At the VM boundary, yes. Each workload gets its own kernel behind hardware virtualization. That isolation is necessary for running AI-generated code. Three layers around the microVM differ:

• Lifecycle management: Blaxel manages standby automatically. Fly.io describes Machine suspend snapshots as disposable. The platform may cold-start when resume isn't possible.
• Egress control: Blaxel provides domain filtering and dedicated egress gateways in private preview. Fly.io network policies can restrict or block Machine egress.
• Tenant separation: Each Blaxel sandbox runs in its own microVM. Fly.io teams assemble per-customer separation through per-customer apps. Its blueprint warns about placing compromised user environments within one app.

Matching Blaxel's lifecycle behavior on plain Machines requires two pieces of custom code. One calls suspend in the request path. Another handles cold starts when resume fails. Both platforms share a Firecracker caveat: resuming twice from one snapshot can replicate identifiers and cached random values.

"Fly.io's per-second compute rates look cheaper"

They can be cheaper for continuously running services. However, session-based sandboxes often run in short bursts. Fly Machines bill while started. Suspension can be manual or automatic through configured Fly Proxy autostop and autostart.

A 2 GB sandbox waiting for a model response bills like one compiling code. A fleet keeps metering until configured automation or application code suspends each Machine. Stopped Machines still bill root filesystem storage at $0.15/GB monthly.

Blaxel bills compute only for active GB-seconds, while storage bills separately. Waiting for a model response incurs no compute charge after standby begins. Between pull requests, a PR review sandbox enters standby. Its filesystem remains available, so the next review can skip another repository clone.

How to start your Blaxel vs Fly.io evaluation

For a full application stack, Fly.io is a capable platform. For production AI agent sandboxes, lifecycle behavior drives unit economics and user experience. Test whether state survives idle periods and measure wake time under realistic load.

Blaxel sandboxes resume from standby in under 25ms with memory state intact. Standby is perpetual and network-triggered. Sandboxes enter standby after roughly 15 seconds of network inactivity. They retain filesystem and memory state while remaining in standby. This duration doesn't replace durable storage guarantees. Use Volumes for long-term retention.

An agent can resume mid-task instead of rebuilding context from the repository. Python, TypeScript, and Go SDKs cover filesystem and process operations. Integration uses a client library rather than custom lifecycle infrastructure.

The Blaxel product stack includes Sandboxes, Volumes, Agent Drive in private preview, Batch Jobs, and managed networking. The fastest evaluation uses your own agent and real traffic. Sign up free and compare active compute, storage, resume behavior, and integration effort.

FAQs about Blaxel vs Fly.io for AI agent sandboxes

These answers clarify the lifecycle, product, compliance, and trial-cost differences that affect an engineering evaluation.

Can Fly Machines preserve agent state between sessions?

Partially, with caveats. Fly.io suspend snapshots memory, but those snapshots aren't guaranteed to persist. A failed restore may cause a cold start. Persistent Volumes protect selected durable data but don't preserve complete memory state.

Blaxel standby preserves filesystem and memory state while the sandbox remains in standby. Blaxel doesn't guarantee long-term data persistence through standby alone. Use Volumes when data must persist durably. External connections, including database and HTTP pools, don't survive standby restoration.

During evaluation, start a representative process, create uncommitted files, and leave the environment idle. Reconnect and verify the process tree, working directory, and files. Then test a full restart separately from a successful suspend restore. This distinguishes standby behavior from durable storage.

Repeat the test with database clients and outbound HTTP connections so your agent handles reconnection correctly. Track how often each platform resumes successfully, how often it rebuilds state, and how long users wait in either case.

Does Fly.io offer a purpose-built agent sandbox product?

Yes. Sprites provide checkpoint and restore capabilities. They also meter idle time automatically. Sprites remain separate from plain Fly Machines and don't accept Dockerfiles. Their private networking questions remain open. Third-party measurements place warm wake in the hundreds of milliseconds.

Blaxel includes filesystem and process control within its core platform. Managed networking adds egress controls and secrets injection. Teams should compare both products using their actual repository, process tree, and network dependencies.

Run the same agent workflow through each product and record environment creation, warm wake, checkpoint restoration, and recovery after an interrupted restore. Check whether existing image builds need changes because Sprites don't accept Dockerfiles. Test outbound allowlists, secret delivery, custom domains, and private networking requirements before selecting a product. These checks reveal whether a faster migration path offsets differences in lifecycle behavior and networking support.

How do the platforms differ on compliance?

Blaxel holds SOC 2 Type II and ISO 27001 certifications. It also offers a Business Associate Agreement (BAA) as an add-on. Fly.io holds SOC 2 Type 2 and offers a $99/month HIPAA BAA. It doesn't hold an organizational ISO 27001 certification. Its datacenters operate within ISO 27001-certified facilities.

For healthcare and fintech procurement, compare organizational certifications, facility certifications, contractual coverage, and data-handling controls separately. Request the current audit reports and confirm which services fall within each certification's scope. Review whether the BAA covers the specific data paths your agent uses, including sandbox storage, logs, networking, and backups. Confirm retention policies for standby snapshots and durable volumes. Procurement teams should also verify access controls, incident-response commitments, and the handling of data after sandbox deletion. These requirements should inform procurement rather than replace lifecycle, networking, and workload testing.

What does it cost to try Blaxel against an existing Fly.io deployment?

Nothing up front. The free tier provides credits toward sandbox usage. Point an existing agent at a Blaxel sandbox through the Python or TypeScript SDK. Run one week of real traffic. Keep the repository, task mix, memory allocation, and storage footprint consistent across both platforms.

Then compare active GB-seconds, storage charges, resume failures, and engineering effort against your Fly.io deployment. Separate active execution from time spent waiting for models, databases, or user input.

Record root filesystem, volume, and snapshot charges independently so idle storage doesn't disappear inside the compute comparison. Include the time required to configure suspend behavior, restore handling, secrets, and egress controls. Finally, compare user-visible outcomes: repository clone frequency, failed resumes, and time until the agent can continue working. This trial structure produces a workload-specific cost comparison without treating unmatched CPU allocations or different lifecycle models as equivalent.