Best microVM platforms for AI agent isolation in 2026

Your coding agent writes a Python script and executes it, unreviewed. The session idles, so the cloned repo and dependencies vanish. The next tool call must re-clone and reinstall everything. If that prompt-injected script shares a host kernel, attacker-controlled system calls reach the host kernel. This increases exposure to kernel or runtime vulnerabilities affecting other tenants.

Repeated setup work adds latency across every tool call. Shared kernels also increase cross-tenant exposure when agents execute untrusted code. Meanwhile, always-on environments create idle charges while agents wait for users, models, or external APIs.

MicroVMs give each workload its own kernel behind a hardware boundary. Some agent sandbox platforms still default to containers. This roundup compares four platforms using verified isolation, lifecycle, limitation, and pricing details.

TL;DR:

• MicroVMs are the isolation baseline: Shared-kernel containers leave escape paths for untrusted AI-generated code. MicroVMs give each sandbox its own kernel behind hardware virtualization.
• Blaxel for persistent production agents: Perpetual standby with sub-25ms resume, zero idle compute, managed networking, and SOC 2 Type II, ISO 27001, and HIPAA coverage.
• Fly.io for regional DIY builds: Firecracker microVMs across a broad regional footprint, plus Sprites for managed agent lifecycle. Teams build remaining orchestration themselves.
• Daytona and E2B with caveats: Daytona's default class is a container; the VM class qualifies but lacks published resume figures. E2B offers open-source self-hosting but enforces 24-hour session caps on Pro.
• Idle cost decides the bill: Sporadic agents spend more time waiting than executing. Compare standby charges, stopped-state storage, and subscription floors alongside active compute rates.

How we chose these platforms

We evaluated each platform against seven production requirements:

• Isolation model: Shared host kernels differ from hardware virtualization boundaries. Documented runc escapes provide concrete evidence. runc is an Open Container Initiative container runtime. Examples include the CVE-2024-21626 advisory and CVE-2019-5736 disclosure.
• Standby and resume: This covers wake speed and whether measurements reflect sequential or burst concurrency.
• Persistent state: This covers files, memory, processes, and the platform’s retention period.
• Networking control: This includes egress filtering, dedicated IPs, secrets injection, and custom domains.
• SDK coverage: This distinguishes first-class software development kits from REST-only access.
• Idle billing: This measures charges while agents wait between tool calls. Those charges can outweigh active compute for sporadic workloads.
• Compliance coverage: This includes System and Organization Controls (SOC) 2 Type II and the Health Insurance Portability and Accountability Act (HIPAA). It also covers International Organization for Standardization (ISO) 27001 and data residency.

Together, these criteria expose security, latency, lifecycle, integration, and cost tradeoffs. Pricing comparisons use a 2 vCPU and 4 GB RAM baseline where vendors publish equivalent configurations. Daytona and E2B publish per-unit rates. Fly.io’s baseline uses shared CPUs. Blaxel meters active memory by gigabyte-second.

Competitor rates reflect published pricing available in May 2026. Platforms offering only shared-kernel containers were excluded. Daytona qualifies through its VM class. Vercel Sandbox, Together Code Sandbox, and Runloop also run microVMs. This comparison prioritizes platforms supporting long-lived sessions. Lifecycle gaps remain eligible when this article discloses them clearly.

Comparison table

The first table applies all seven evaluation criteria. “Not published” identifies gaps instead of implying complete vendor coverage.

Pricing uses the same 2 vCPU and 4 GB RAM baseline where possible:

Shared CPUs, VM-class uncertainty, and subscription floors prevent a pure hourly-rate comparison. Model storage and idle periods before selecting the lowest headline rate.

1. Blaxel

The Blaxel platform is the infrastructure foundation for autonomous agents. It provides the execution layer agent code depends on in production. Every sandbox is an individual Firecracker microVM with a RAM-based root filesystem.

Deleting a sandbox erases its volatile data immediately. Sandboxes that never enter standby can support Zero Data Retention (ZDR). ZDR keeps files and state in volatile memory rather than durable disk storage. The ZDR architecture explains that relationship.

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

Key features

Blaxel combines stateful compute with managed storage and networking. Perpetual standby preserves memory, processes, and filesystem state while the sandbox exists. Standby has no compute charge, although snapshot and volume storage remain billable. Sandboxes resume in under 25ms with processes intact.

Network shutdown moves sandboxes into standby after approximately 15 seconds without network activity. Teams don’t need separate lifecycle code. Managed custom domains, preview URLs, and proxy secrets injection protect production traffic. Dedicated egress gateways are available in private preview.

For storage, Agent Drive, in private preview, shares files across multiple sandboxes. Volumes provide durable block storage. These capabilities suit agents alternating between short compute bursts and long idle periods.

Pros

The strongest benefits center on persistent execution and integrated infrastructure:

• Idle efficiency: Standby keeps long agent sessions available without active compute charges.
• Managed networking: Custom domains, preview URLs, proxy secrets injection, and preview egress gateways come from the compute provider.
• Unified infrastructure: Compute, storage, and networking share one provider instead of three separate vendors.
• Compliance coverage: Blaxel supports SOC 2 Type II and ISO 27001. HIPAA support is available through a Business Associate Agreement.
• RAM-based storage: The root filesystem keeps volatile data off durable disk. This supports immediate erasure when the sandbox is deleted.

These benefits matter most when agents preserve context between intermittent tool calls.

Cons

Blaxel’s main limitations involve language coverage, accelerated compute, and deployment control:

• SDK languages: First-class SDKs cover Python, TypeScript, and Go. Ruby, Java, and Rust teams use the REST API and should budget for a thin HTTP client.
• No GPU instances: GPU training and large-model inference require another provider.
• Managed control plane: Blaxel offers Bring Your Own Metal (BYOM) and virtual private cloud (VPC) interconnect, but the managed control plane remains, so fully air-gapped, run-it-yourself deployment is unavailable.

Pricing

Blaxel uses consumption-based pricing without a base subscription. The Free option includes up to $200 in free credits plus usage costs. Pre-configured sandbox tiers and usage-based pricing appear on Blaxel’s pricing page. Available add-ons include email support, live Slack support, and HIPAA compliance.

The comparison table above normalizes the active memory rate for the shared baseline.

Who is Blaxel best for?

Coding agents are the primary fit. Pull request review and data analysis agents also benefit from preserved state. These workloads often leave long gaps between short compute bursts.

2. Fly.io

The Fly.io platform runs Machines and Sprites as Firecracker microVMs across a broad regional footprint. Fly.io launched Sprites in January 2026. Sprites are persistent Linux computers designed for AI agents.

Each Sprite creates in 1–2 seconds with 100 GB of durable storage. It sleeps automatically when inactive and has no published time limit. These properties reduce lifecycle work compared with building agent infrastructure directly on Machines.

Key features

Fly.io provides separate lifecycle models for Machines and Sprites. Every Machine and Sprite runs inside a hardware-isolated Firecracker microVM. Sprite checkpoints use transactional filesystem snapshots that take about 300ms and support git-like rollbacks. Each Sprite also has an independent HTTPS URL.

For the Machine lifecycle, Suspended Machines resume in a few hundred milliseconds. Cold starts run roughly two seconds or longer. Sprites reduce the routing and lifecycle work that raw Machines leave to engineering teams.

Pros

Fly.io’s strongest benefits come from regional placement and its newer Sprite abstraction:

• Regional placement: Agent compute can run near the application it serves.
• Managed Sprites: Sprites reduce the preview routing, provisioning, and shutdown work required by Machines.
• Compliance options: A HIPAA Business Associate Agreement is available as a paid add-on. Fly.io also provides a pre-signed General Data Protection Regulation (GDPR) data processing agreement.

These strengths fit teams already operating applications across Fly.io regions.

Cons

Fly.io’s documented limitations involve capacity, billing surfaces, and support access:

• Regional capacity: ORD and IAD are Fly.io region codes for U.S. data centers. Community reports document persistent reservation failures in ORD region capacity, and a separate IAD capacity report covers Machines with volumes.
• Stopped-state billing: Stopped Machines still incur root filesystem storage charges.
• Support access: Email support requires a paid support package, while community support remains available to all customers.

Teams should test required regions and include stopped storage in their cost model.

Pricing

Fly.io bills Machines by configuration and adds storage charges separately. A shared-cpu-2x Machine with 4 GB RAM uses per-second billing. Volume storage remains billable regardless of Machine state. Volume snapshot storage charges began on January 1, 2026. The HIPAA Business Associate Agreement is a monthly compliance add-on.

The low active rate uses shared rather than dedicated CPUs.

Who is Fly.io best for?

Fly.io fits teams that want agents near existing Fly.io applications. Sprites remain a recent product, so validate them against production workloads.

3. Daytona

The Daytona platform pivoted from developer environments to agent infrastructure in April 2025. It raised a Series A worth $24 million in February 2026.

Daytona offers Container, VM, and GPU isolation classes. Its default container class shares the host kernel. An independent security study found no seccomp filter on that class. Secure computing, or seccomp, filters which system calls a process can make. The VM class adds hardware virtualization and qualifies for this roundup.

Container sandboxes create in under 90ms. Daytona publishes no equivalent VM-class creation or resume figure. Teams evaluating untrusted code should benchmark the VM class directly.

Key features

Daytona emphasizes language coverage, GPU access, and outbound policy controls. It supports Python, TypeScript, Ruby, Go, and Java through five SDKs. Daytona also provides a command-line interface and REST API.

For accelerated workloads, GPU pricing lists H200, H100, and RTX-series instances beside CPU classes. Network controls support Classless Inter-Domain Routing (CIDR) ranges and domain allowlists. Daytona also offers block-all mode, proxy routing, and OpenTelemetry export.

These capabilities suit teams managing several languages or mixed CPU and GPU workloads.

Pros

Daytona’s primary advantages are broad SDK support and multiple compute classes:

• Language coverage: Daytona provides the widest first-class SDK coverage in this comparison.
• Stopped containers: Billing documentation states that stopped containers bill only for disk. Archived containers bill nothing.
• Compliance documentation: Daytona’s trust center lists SOC 2 Type I and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 27001 materials.

The container billing benefit doesn’t establish equivalent VM-class lifecycle behavior.

Cons

Daytona’s documented limitations come from open issues and its source transition:

• SDK serialization bug: A Python SDK issue reports that snapshot.to_json() can’t serialize datetime objects.
• Closed-source shift: Daytona moved its production codebase to closed source in 2025. The public repository notice says updates have stopped.
• Other open issues: These document template failures and problems involving custom resources. An authentication issue flags gaps involving DockerHub and GitHub Container Registry workflows.

Teams should reproduce these workflows with their selected isolation class and registry configuration.

Pricing

Daytona publishes per-unit rates rather than a fixed baseline instance. Daytona doesn’t publish a separate VM-class rate. vCPU costs $0.0504 hourly, while memory costs $0.0162 per GiB-hour. Storage costs $0.000108 per GiB-hour, and the first 5 GiB are free.

Billing occurs per second without a minimum period. Daytona includes $200 in free compute. Confirm whether VM-class pricing adds any multiplier before estimating production cost.

Who is Daytona best for?

Daytona fits teams seeking one vendor for CPU and GPU sandboxes. Teams that valued its earlier open-source posture should reassess the 2025 transition.

4. E2B

The E2B platform is the open-source reference point in this category. Each sandbox uses a Firecracker microVM. Its infrastructure architecture documents an approximately 80ms template snapshot load.

Continuous runtime has E2B billing limits of one hour on Hobby and 24 hours on Pro. Pausing and resuming resets that counter.

Key features

E2B combines snapshot restoration, desktop automation, and customer-cloud deployment. Lazy restoration loads memory pages from the template snapshot on demand. Resume doesn’t wait for the full memory image. Full-memory pause preserves running processes, while a filesystem-only mode skips memory restoration.

The e2b-desktop package supports computer-use and browser automation workloads. Bring your own cloud deploys sandboxes into customer VPCs on AWS and Google Cloud. Azure support is in development.

E2B lacks native managed custom domains and dedicated egress IPs. It documents custom-domain and IP-tunneling workarounds. This design favors teams prioritizing source access or customer-controlled cloud placement.

Pros

E2B’s strongest advantages are open infrastructure and deployment flexibility:

• Self-hosting: Open infrastructure supports self-hosting through Terraform, Nomad, and Consul.
• Paused billing: Paused sandboxes aren’t billed and remain available without automatic deletion.
• Framework integrations: Integrations include OpenAI, Anthropic, LangChain, and the Vercel AI SDK.
• Compliance coverage: E2B’s trust center lists SOC 2 and HIPAA.

Self-hosting increases control but transfers hardening, patching, and evidence collection to the customer.

Cons

E2B’s documented limitations affect runtime, snapshots, SDK reliability, and plan minimums:

• Runtime boundaries: Hobby and Pro plans enforce hard continuous-runtime boundaries.
• Snapshot bug: GitHub issue 884 reports lost filesystem changes after later resume cycles.
• SDK reliability: Open issues report HTTP/2 errors and lost Retry-After headers. Those failures can disrupt retry logic.
• Subscription floor: The Pro plan imposes a subscription floor before compute usage begins.

Production tests should include repeated pause cycles, burst concurrency, and retry handling.

Pricing

E2B combines a subscription tier with metered compute. E2B pricing lists a free Hobby plan with $100 credit. The plan supports 20 concurrent sandboxes. Pro adds a monthly subscription and supports 100–1,100 concurrent sandboxes.

Compute costs $0.000014 per vCPU-second plus $0.0000045 per GiB-second of RAM. Paused sandboxes don’t incur compute charges. The subscription floor matters most for small production workloads with limited active compute.

Who is E2B best for?

E2B fits teams prioritizing open infrastructure, self-hosting, or sandboxes inside their own VPC. Those deployment models require additional security and compliance work.

How to choose

If agents execute untrusted generated code, start with the isolation model. Confirm that the relevant product class uses a hardware boundary rather than a shared kernel.

Next, reproduce your actual lifecycle. Pause the environment, resume it repeatedly, and verify files, memory, processes, and network reconnection. Measure latency under expected burst concurrency instead of relying only on sequential figures.

Finally, calculate active compute, idle charges, storage, subscription floors, and support costs. Sporadic agents often spend more time waiting than executing. Therefore, idle policy can dominate the bill.

Get started with the best microVM platforms for AI agent isolation

The execution layer determines whether an agent that works in a demo survives production. State loss forces agents to re-clone repositories and reload datasets. Shared-kernel exposure also increases risk when generated code serves multiple tenants.

Blaxel fits production coding, pull request review, and data analysis agents needing persistent state. Its Firecracker sandboxes combine standby restoration with managed networking. Agent Drive, in private preview, shares context and artifacts across sessions. Volumes provide durable storage, while Batch Jobs handle fan-out processing.

Fly.io fits teams already operating distributed applications on its regional infrastructure. Daytona fits teams requiring broad SDK coverage or GPU sandboxes. E2B fits teams prioritizing open infrastructure, self-hosting, or customer-cloud deployment.

Measure your current resume behavior, idle cost, and isolation boundary first. Then compare the same workload across the shortlisted platforms. Teams matching Blaxel’s profile can sign up free and validate those measurements directly.

FAQs

Why do microVMs matter for isolating AI agent code?

Documented runc escapes have allowed container processes to reach host filesystems and gain root access. MicroVMs run a separate guest kernel for each workload. Guest system calls don’t directly reach the host kernel, reducing the host attack surface. Kernel-based Virtual Machine (KVM) hardware virtualization provides the boundary. Hypervisor, virtual-device, host-integration, and runtime vulnerabilities remain possible.

Containers remain common for trusted first-party code. Arbitrary, untrusted, or AI-generated code needs stronger isolation from the host and neighboring tenants.

How fast does a sandbox need to resume for production agents?

Coding agents make repeated tool calls during one interaction. Resume delay can therefore accumulate across file reads, tests, and patch operations.

Sub-second resume keeps interactive loops usable. Multi-second cold starts make coding and pull request review agents appear unresponsive. Benchmark the complete request path under realistic concurrency.

What happens to sandbox state when an agent goes idle?

Behavior varies by platform and product class. Blaxel standby preserves files, memory, and running processes while the sandbox exists. External network connections can still time out.

E2B’s full-memory pause preserves processes, but network connections must be re-established. Daytona documents filesystem persistence for stopped container sandboxes. It doesn’t publish equivalent VM-class lifecycle details in the reviewed documentation.