5 best sandbox platforms with managed networking for AI agents

Your coding agent works, and then an enterprise prospect sends the security questionnaire. Which static IPs should our firewall allowlist? How do you guarantee API keys never enter the code-execution environment? On most sandbox platforms, the honest answer involves a proxy VM and DNS you operate yourself. Credentials still pass as environment variables, which any process inside the sandbox can read.

Managed networking closes that gap. It covers the capabilities a platform ships natively. The key pieces are egress filtering and credential injection outside the sandbox. Static outbound IPs and custom domains matter too.

This roundup compares five sandbox platforms on those managed-networking capabilities. It also weighs isolation, standby economics, and compliance posture because those factors decide whether an agent product survives enterprise procurement.

TL;DR:

• Networking decides enterprise readiness: Static egress IPs, custom domains, proxy secrets injection, and egress filtering are common procurement requirements, not edge cases.
• Blaxel ships all four natively: Managed custom domains, dedicated egress gateways, proxy secrets injection, and domain filtering are platform primitives, not self-hosted workarounds.
• Fly.io for DIY with strong IPs: Cheap static egress IPs and network policies suit teams building their own sandbox orchestration layer.
• Partial coverage elsewhere: Modal gates static IPs and custom domains behind its Team plan. Daytona has secrets proxying but no confirmed static IPs. E2B requires self-hosted proxies for both.
• Isolation still matters: MicroVMs provide hardware-enforced boundaries for untrusted AI-generated code. Containers and gVisor offer weaker tenant separation.

What to look for in a sandbox platform with managed networking

Sandbox networking depends on where the platform enforces trust boundaries. The strongest setups keep credentials and outbound policy outside the code-execution environment, then expose only the destinations and identities the agent actually needs.

Seven criteria frame each review below:

• Proxy-based secrets injection: Credentials are injected at the network layer, outside the sandbox. Plaintext keys never enter the environment.
• Egress control: Domain and Classless Inter-Domain Routing (CIDR) allowlists restrict outbound traffic. Agent code can reach only approved destinations.
• Static egress IPs: Stable source IPs let enterprise firewalls and third-party APIs allowlist agent traffic.
• Custom domains: White-label URLs support previews and agent endpoints. Managed Transport Layer Security (TLS) removes certificate management work.
• Isolation model: MicroVM, gVisor, and container isolation create different boundaries. For untrusted AI-generated code, hardware-enforced boundaries matter.
• Standby economics: Idle cost, resume speed, and state survival shape fleet economics. Long-lived sessions make this difference visible.
• Compliance: System and Organization Controls 2 (SOC 2) Type II matters for enterprise security reviews. International Organization for Standardization (ISO) 27001, Health Insurance Portability and Accountability Act (HIPAA), and Business Associate Agreement (BAA) availability also matter.

These criteria separate sandbox execution from production agent infrastructure. They also expose which platforms require separate networking work.

1. Blaxel

Blaxel provides the infrastructure for autonomous agents. It combines compute, storage, and networking on one platform. Compute includes Sandboxes and Batch Jobs. Storage includes Agent Drive and Volumes. Networking includes custom domains, domain filtering, dedicated egress gateways, and proxy secrets injection.

Blaxel is the only platform here that combines managed custom domains with proxy secrets injection, dedicated egress gateways in private preview, and perpetual standby as agent-infrastructure primitives. Competitors treat at least one of those capabilities as a self-hosted workaround. Sandboxes run in microVMs built on Blaxel's custom fork of Firecracker. They stay in perpetual standby indefinitely with zero compute cost while idle.

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

Key features

Blaxel's feature set centers on production agent networking and persistent execution:

• Managed custom domains: Teams expose sandbox preview URLs through their own domains. Blaxel manages TLS certificates for those domains.
• Dedicated egress gateways: Blaxel provides dedicated egress gateways in private preview. They give sandboxes allowlistable static outbound IPs.
• Proxy secrets injection: The Blaxel proxy intercepts outbound HTTPS and injects headers, body fields, and secrets server-side. Plaintext secrets never enter the sandbox.
• Perpetual standby: Sandboxes resume in under 25ms with filesystem and memory state restored. Idle sandboxes carry no compute charge.
• Agent Drive: Agent Drive is in private preview. It gives teams a shared filesystem mountable to multiple sandboxes at once.

Those features make networking part of the platform, not separate infrastructure. Standby preserves filesystem and memory state. Volumes handle guaranteed long-term persistence when data must outlive a sandbox session.

Pros

• Blaxel's strongest fit is for teams that need enterprise networking without building the plumbing themselves. Custom domains, egress IPs, and secrets proxying ship as platform primitives. Competitors require separate infrastructure for at least one of them.
• The standby model also changes fleet economics. Sandboxes stay in standby indefinitely with zero compute charges, and there is no standby-duration cap. That matters when coding agents and PR review agents pause between customer actions but need to resume with state intact.
  Blaxel also supports enterprise security reviews with SOC 2 Type II, ISO 27001, HIPAA through a BAA, and Zero Data Retention (ZDR) for privacy-sensitive workloads. Blaxel's microVMs, built on a custom fork of Firecracker, give each sandbox its own kernel. That creates a stronger boundary for untrusted code than shared-kernel containers.

Cons

• The main tradeoff is language coverage. First-class SDKs cover Python, TypeScript SDK, and Go. Ruby, Java, and Rust teams use the REST API.
• That limitation matters less for teams already building agents in Python or TypeScript.

Pricing

Blaxel uses usage-based pricing. The Free plan includes up to $200 in free credits plus usage costs. Pre-configured sandbox tiers and usage-based pricing are available on Blaxel's pricing page, which has the most up-to-date pricing information. Available add-ons include email support, live Slack support, and HIPAA compliance.

For the shared 2 vCPU, 4 GB, one-hour baseline, Blaxel pricing depends on the selected sandbox tier. Standby carries no compute charge. That changes long-session economics.

Who is Blaxel best for?

Teams shipping coding agents, PR review agents, and data analysis agents need production networking early. Blaxel fits when those teams want managed domains, static egress, and proxy secrets. It also fits teams that want persistent sessions without owning orchestration. Teams needing guaranteed durable storage should pair standby with Volumes.

2. Fly.io

The Fly.io platform runs application code from container images in Firecracker microVMs. It has a broad regional footprint and strong networking primitives. Those primitives include static egress IPs, dedicated IPv4 addresses, and default-deny network policies.

The tradeoff is scope because Fly.io is a general compute platform, not a managed agent sandbox system. Teams assemble snapshots, lifecycle orchestration, and durable state themselves.

Key features

Fly.io gives teams low-level controls for building their own sandbox layer:

• Static egress IPs: Teams allocate egress IPs per app and region. The addresses persist across Machine destruction.
• Custom domains: Fly.io supports automatic Let's Encrypt certificates. Teams can also import custom certificates.
• Network policies: Creating one rule flips that traffic direction to deny-all. Fly recommends this posture for untrusted code.
• Suspend and resume: Firecracker snapshots capture full VM state. Eligibility depends on Machine configuration and resource limits.

These primitives are powerful when a team wants to own orchestration details. They also move agent-specific lifecycle work onto the engineering team.

Pros

• Fly.io's strengths come from general-purpose infrastructure breadth. Dedicated IPv4 costs $2/month. Static egress IPs cost about $3.60/month under Fly pricing docs. Firecracker microVMs provide a hardware-backed boundary, and billing is per second for running workloads.
  Fly.io also lists SOC 2 Type II and a pre-signed HIPAA BAA. That helps teams answer common procurement checks. Fly.io works well when static IP cost and regional control matter most.

Cons

• The main limitations reflect the work required to build agent infrastructure on Fly.io. Community threads document Machine allocation failures in some regions. One example cites ORD capacity errors in Fly.io's Chicago region. All customers have community support. Email support depends on paid support packages and some legacy plans. Stopped-machine root filesystems and volume snapshots add storage charges. Those costs matter for large sandbox fleets.
• These tradeoffs are manageable for infrastructure-heavy teams.

Pricing

Fly.io pricing maps cleanly to the shared baseline. Shared CPU 2x with 4 GB RAM costs about $0.0309/hour. Performance CPU 2x with 4 GB RAM costs about $0.0894/hour. New pay-as-you-go customers do not get a free tier, and annual reservations carry a 40% discount.

Network pricing matters for agent workloads that move artifacts or datasets between systems. Egress from North America and Europe is listed at $0.02/GB. For managed-networking buyers, Fly.io is strongest when static IPs dominate the decision.

Who is Fly.io best for?

Fly.io is best for teams that want to own sandbox orchestration. It fits teams that need cheap static IPs across many regions. Agent sessions above the documented suspend-eligibility RAM limit start cold instead. Choose it when infrastructure control matters more than managed agent primitives.

3. Modal

The Modal platform is a Python-native serverless GPU platform. Its Sandboxes product uses gVisor, a user-space kernel, for isolation. gVisor improves isolation over standard containers. MicroVMs still provide a stronger hardware-enforced boundary.

Modal's sandbox networking has matured with domain allowlisting, sandbox tunnel custom domains, and modal.Proxy for static outbound IPs. Both custom domains and static IPs require the $250/month Team plan.

Key features

Modal's sandbox networking fits teams already using its Python and GPU stack:

• Three egress modes: Modal supports full block, CIDR allowlist, and domain allowlist. Denials appear in App logs.
• Tunnels: Modal provides secure random HTTPS URLs for inbound traffic. Unencrypted TCP is also available.
• Static egress IPs: modal.Proxy objects give containers a stable outbound IP. The feature is on Team and Enterprise plans.
• Snapshots: Memory snapshots are in early preview. Filesystem snapshots have a documented time-to-live.

These features are strongest when Modal already runs the surrounding workload. Otherwise, teams inherit a Python-specific platform model for sandbox networking.

Pros

• Modal's advantages sit around Python workflows and GPU-adjacent development. Sandboxes sit alongside Modal functions and GPU workloads, so Python teams get one programming model. Domain-level egress filtering includes logged denials, which helps during security reviews.
• Modal also lists SOC 2 Type II. HIPAA BAA support is available on the Enterprise plan. Modal is most compelling when sandboxes are adjacent to existing Modal workloads.

Cons

• Modal's limitations are pricing and portability related. Modal documents a 1.5x multiplier for broad regions. Narrow regions such as us-west carry a 1.75x multiplier in its region selection docs. Sandboxes are billed at 3x standard Function rates. Modal also documents this multiplier for nonpreemptible workloads. Raising scaledown_window reduces cold starts. Modal's cold start docs explain that this keeps resources reserved longer. The decorator-based SDK creates platform-specific serving logic. Moving workloads elsewhere requires rewriting that layer.
• These tradeoffs matter more outside Python-first teams.

Pricing

Modal pricing has a base plan floor and usage rates. Starter is $0/month with $30/month in credits. Team is $250/month with $100/month in credits. At listed rates, 2 cores and 4 GB RAM cost about $0.3806/hour before regional multipliers.

The baseline uses $0.00003942/core/second for CPU. It also uses $0.00000672/GiB/second for memory from Modal pricing. Teams needing static IPs or custom domains should include the Team plan floor.

Who is Modal best for?

Modal is best for Python teams already running GPU workloads there. It fits when sandboxes belong beside Modal functions. Teams also need budget for the Team plan when static IPs or custom domains are required. Choose another platform when agent state and networking matter more than Modal adjacency.

4. Daytona

The Daytona platform shifted its messaging toward AI agent sandboxes in 2025. It describes itself as infrastructure for running code for AI agents. Sandboxes are a core runtime, and GPU sandbox options are available.

Daytona's secrets proxy is production-oriented. Placeholders are replaced during outbound HTTP(S) requests only when the destination matches a host allowlist. Requests to other destinations keep the placeholder unchanged. Daytona does not confirm fixed public egress IP support in the documentation covered for this comparison.

Key features

Daytona's strongest networking feature is proxy-based secrets handling:

• Proxy secrets injection: Plaintext secrets never enter the sandbox. Host allowlists and response scrubbing reduce exposure risk.
• Domain allowlists: Daytona documents domain allowlists with entry limits, wildcard support, and a full network block option.
• Signed preview URLs: Token-embedded links use a short default expiry and a longer configurable maximum.
• Snapshot options: Container sandboxes support filesystem-only snapshots. VM sandboxes support filesystem and memory snapshots.

Daytona fits teams that value secrets proxying more than static egress IPs. The static-IP gap should be checked before enterprise procurement starts.

Pros

• Daytona's advantages are strongest for container-sandbox workflows. It is one of the platforms here with proxy-based secrets injection, which keeps real credentials out of sandbox processes. Daytona also lists $200 in free credits. Startup credits are available up to $50,000.
• Daytona states that SOC 2 Type I and HIPAA certifications are in place. Buyers should verify current artifacts during procurement. These strengths make Daytona relevant for teams focused on secrets handling.

Cons

• Daytona's limitations come from its closed-source transition and open issue history. Daytona moved its production codebase closed-source. The closed-source update explains the change. Open issues document sandbox initialization failures. Those reports affect default templates and custom resources.
• Another issue documents package repository failures during apt-get update. A Python SDK issue reports an SDK serialization bug in snapshot.to_json(). Open issues also flag authentication documentation gaps for DockerHub and GitHub Container Registry (GHCR).

These issues matter most for teams moving from prototype to production.

Pricing

Daytona lists resource-based pricing, with compute, memory, and storage priced separately. The shared 2 vCPU and 4 GB RAM baseline depends on the current compute and memory rates. Storage is listed separately by GiB-hour, and the first 5 GiB are free.

Usage tiers gate resources and networking. Tier 1 and 2 network restrictions cannot be overridden per sandbox. Because static egress IP support is not confirmed, pricing alone should not drive enterprise-networking decisions.

Who is Daytona best for?

Daytona is best for container-sandbox teams that need proxy secrets. Teams that require static outbound IPs should verify Daytona's current networking documentation. Compliance-sensitive buyers should also verify current SOC 2 and HIPAA artifacts directly. It is a stronger fit for secrets handling than for full managed networking.

5. E2B

The E2B platform is an open-source AI sandbox platform. It runs sandboxes on Firecracker microVMs, with each sandbox getting its own kernel. E2B also offers a managed production service around its sandbox infrastructure and SDKs.

Its networking controls have grown through 2026. The platform supports allow and deny egress lists. It also supports live rule updates on running sandboxes. Per-host request transforms are in public beta. Custom domains require a Google Cloud Platform (GCP) VM running Caddy with Cloudflare DNS.

Key features

E2B's feature set is strong for teams that value open-source code and microVM isolation:

• Egress filtering: allowOut accepts IPs, CIDR blocks, and domains. Domains are not supported in denyOut.
• Live rule updates: updateNetwork changes egress rules on running sandboxes. The sandbox does not need to restart.
• Request transforms: The egress proxy injects headers on matching HTTP/HTTPS requests per host. This feature is in public beta.
• Pause and resume: Paused sandboxes can resume without a restart. Pause behavior depends on RAM.

E2B gives developers flexible sandbox controls with a familiar open-source posture. Production teams still need to account for networking workarounds.

Pros

• E2B's advantages are strongest during prototyping and developer-led evaluation. It has multi-language SDKs and strong mindshare in the AI sandbox category, which lowers evaluation friction. Firecracker microVMs also give each sandbox its own kernel, creating strong isolation for untrusted code.
• E2B supports public sandbox URLs. Optional Host-header masking helps route preview traffic. These strengths make E2B a common starting point for agent sandbox evaluation.

Cons

• E2B's limitations show up when sessions run longer or state must persist reliably. Continuous runtime is capped at 1 hour on Hobby and 24 hours on Pro. The cap appears in E2B billing materials. The Pro plan carries a $150/month floor. It also has a 100-concurrent-sandbox cap.
• GitHub issue #884 describes filesystem changes being lost after later resumes. The file persistence report matters for stateful agents. E2B's issue history includes concurrency errors, retry gaps, and command-run reliability reports. Those issues affect production automation.
• Teams should test long-lived sessions before relying on E2B for stateful production agents.

Pricing

E2B pricing combines a plan floor with per-second usage. Hobby is $0/month with $100 in one-time credits. Pro is $150/month, and Enterprise is custom. At listed rates, 2 vCPUs and 4 GB RAM cost about $0.1656/hour.

The baseline uses $0.000028/second for 2 vCPUs. It also uses $0.0000045/GiB/second for memory. The $150/month Pro floor matters when prototypes become production workloads.

Who is E2B best for?

E2B is best for teams prototyping agent products. It fits iterative sessions that stay inside runtime caps. E2B does not list SOC 2, HIPAA, or ISO 27001 status in the documentation covered for this comparison as of July 2026. Teams should test persistence and networking workarounds before committing production state.

Comparison table

Use this table to compare the decision criteria side by side. It compresses the platform sections into the tradeoffs buyers ask about first.

The table shows a split. Some platforms provide networking primitives. Others require teams to assemble production networking themselves.

How to choose

If your agents serve enterprise customers, native networking should dominate the decision. Static IPs and white-label domains are not edge cases in enterprise sales. They are common procurement questions.

That narrows the field to Blaxel and Fly.io for many teams. Blaxel fits teams that want managed agent infrastructure. Fly.io fits teams that want to build the sandbox layer themselves.

If sessions span hours or days, standby economics and state persistence decide the outcome. Idle billing and runtime caps compound across a fleet. For multi-tenant untrusted code, weight hardware-enforced microVM isolation over container defaults.

Choose the best sandbox platform with managed networking for AI agents

The execution layer decides whether networking is a product feature or an engineering project. Self-hosted proxy VMs, DNS workarounds, and credential-rotation scripts all become maintenance surfaces. Each surface adds another place for security reviews to find misconfiguration risk.

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. Networking, storage, and compute ship as built-in primitives of the runtime itself, not features layered on top.

Custom domains, dedicated egress gateways in private preview, and proxy secrets injection cover common enterprise networking requirements. MicroVM isolation, built on Blaxel's custom fork of Firecracker, gives every sandbox its own kernel. Agent Drive in private preview adds shared filesystem support for context across sandboxes and sessions. Volumes provide guaranteed long-term persistence when state must survive beyond standby.

Choose Blaxel when you want managed agent networking and long-lived sessions without owning orchestration. Choose Fly.io when your team prefers building that layer on general compute. Choose Modal when your sandbox workflow already lives inside a Python Modal stack. Choose Daytona when proxy secrets matter more than static IPs. Choose E2B when open-source sandboxing and fast prototyping are the priority.

FAQs about sandbox platforms with managed networking for AI agents

These questions clarify the decisions that usually surface during procurement and security review. They focus on secrets handling, static egress IPs, and isolation tradeoffs for agents that execute untrusted code.

Why inject secrets at the proxy layer instead of environment variables?

Anything inside the sandbox can read environment variables, including AI-generated code. A prompt-injection attack can execute code. It can also exfiltrate credentials stored there.

Proxy-based injection works differently. It intercepts outbound HTTPS and adds credentials at the network layer. The sandbox only sees placeholders, while upstream services receive real credentials.

That is why Blaxel and Daytona built secrets proxies. E2B has request transforms in public beta. It is not a full proxy-based secrets injection system.

Do AI agent sandboxes need static egress IPs?

AI agent sandboxes need static egress IPs when they call enterprise APIs. Customer systems often allowlist source IPs before accepting traffic.

One practitioner noted that MongoDB Atlas relies on IP allowlisting. Dynamic egress IPs can block that integration. Teams then need a stable IP, a CIDR range, or another supported network access method.

Among the platforms here, Fly.io sells static egress IPs directly. Modal offers static IP proxies on its Team plan. Blaxel offers dedicated egress gateways in private preview. Reviewed E2B and Daytona documentation does not confirm native static egress IP support.

Are containers safe enough for AI agent sandboxes?

Containers are the industry standard for running trusted software in multi-tenant settings. Executing arbitrary, untrusted, or AI-generated code changes the risk model.

Containers share kernels with the host. A kernel exploit inside one sandbox can reach the host and neighboring tenants. Firecracker microVMs give each workload its own guest kernel behind a hardware hypervisor boundary.

For multi-tenant agent products in regulated industries, microVMs are often safer. They provide a stronger isolation layer for higher-risk and compliance-sensitive workloads.