Workflow orchestration / ZibraLabs Distributed Compute alternative

A Zibralabs.ai alternative for the agent’s execution state

ZibraLabs describes distributed CPU and GPU clusters across hyperscalers and neoclouds for simulations, training, data processing, inference and long-horizon agents. Its published cluster and scheduling figures are targets. That large-workload positioning is distinct from a documented per-agent writable filesystem and pause lifecycle.

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Reviewed 2026-10-19 · Published by the Sandbox0 team

When ZibraLabs Distributed Compute fits

ZibraLabs fits teams discussing large parallel workloads, multi-provider capacity and distributed scheduling requirements with the vendor. Confirm actual availability, delivered scale and operational terms for the proposed deployment.

When to consider Sandbox0

Choose Sandbox0 for compatible Linux agents requiring a complete durable writable RootFS, automatic online growth and supported files while guest compute stays paused. Retain or procure required distributed accelerator infrastructure separately.

Capabilities

ZibraLabs Distributed Compute and Sandbox0: what persists?

Feature comparison with documentation and verification status
Environment and runtime lifetimeZibraLabs Distributed ComputeNot established in this reviewSandbox0ttl and hard_ttl default to 0 (disabled). Configure idle pause and deletion policies for the same persistent sandbox identity; an idle running sandbox still accrues compute charges.Read documentation →
Persistent filesZibraLabs Distributed ComputeThe public cluster/workload offer does not establish a standard retained writable per-agent guest disk.View evidence ↗Sandbox0The writable RootFS survives successful checkpoints, including software, system configuration, dependencies, and user data.Read documentation →
Whole RootFSZibraLabs Distributed ComputeDistributed execution and agent use-case positioning do not establish complete mutable OS filesystem continuity.View evidence ↗Sandbox0Persistence extends beyond /workspace to paths such as /usr/local, /etc, and /home. Runtime tmpfs and virtual filesystems are excluded.Read documentation →
Release compute, keep stateZibraLabs Distributed ComputeA general durable per-agent machine pause/resume contract is not established by the public offer.View evidence ↗Sandbox0Pause compute between tasks; resume on available capacity. Default resume starts fresh processes from the saved filesystem.Read documentation →
Memory and process stateZibraLabs Distributed ComputeThe reviewed public offer does not establish arbitrary guest RAM/process checkpoints.View evidence ↗Sandbox0Experimental memory checkpoints can preserve compatible process state. Use memory: true on both pause and resume on a compatible deployment. A filesystem checkpoint alone does not preserve running processes.Read documentation →

Online RootFS growth

On growth-enabled images, Sandbox0 automatically expands the writable RootFS as free space runs low, within a configured sparse logical ceiling up to 64 TiB. Agent processes keep running. Growth is enabled in the qualified Cloud region; confirm activation for other regions. Existing images and snapshots retain their captured storage geometry. The ceiling is logical capacity, not preallocated host disk.

The public target node/task scale concerns distributed compute, not automatic online expansion of a complete durable per-agent RootFS. Confirm storage growth independently from scheduler capacity.

Files while compute is paused

Sandbox0's Files API reads persistent files without resuming compute. Filesystem-only pauses also permit edits; retained-memory pauses are read-only and reject mutations with 409. SSH, SFTP, file watching, and runtime-only mounts require running compute.

See paused file operations →

Long-horizon agents are already an intended workload

The current site explicitly includes agents with high tool use running for hours and coordinating across calls. Confirm how those agents retain files, configuration and processes across tasks and compute interruptions.

Source 1 ↗

Published scale figures are targets, not acceptance results

ZibraLabs says it is targeting clusters from one hundred to fifty thousand nodes, millions of parallel in-flight tasks and sub-fifty-millisecond dispatch overhead. Those statements do not establish measured performance or generally available capacity for a customer workload. Validate the selected configuration and service scope.

Source 1 ↗

Per-agent disk and pause terms require an effective agreement

The public offer asks teams to discuss large CPU/GPU workloads but does not publish a numeric resource tariff or a general whole-root, memory-checkpoint or paused-file API contract. Clarify these requirements alongside the scheduler and compute plan before estimating a migration.

Source 1 ↗Source 2 ↗

Pricing

Compare billing models, then price your workload

CPU allocation, memory, billing minimums, disk, plan fees, and supported workload differ. These meters are not equal-performance machine quotes. Confirm region, resource units, and included allowances before estimating a total.

ZibraLabs Distributed Compute

Pricing reviewed 2026-10-19

Distributed CPU/GPU capacity
Workload-specific vendor quote
Rate source ↗
Retained state and storage
Confirm scope and billing in the deployment agreement
Rate source ↗
Long-horizon agent execution
No public numeric per-agent tariff established
Rate source ↗

Do not turn “cheapest” positioning or target scale into a fixed price, guaranteed saving or available capacity claim. Confirm compute, storage, orchestration, commitments and retained-state behavior for the selected workload.

Check current ZibraLabs Distributed Compute pricing ↗

Sandbox0

$0.015/GiB-hour compute (billed by the second at approximately $0.000004167/GiB-second), plus $0.02/GiB-month of retained incremental RootFS and memory checkpoint data. Shared base images and platform recovery retention are excluded; paused sandboxes accrue storage charges only.

Compute / GiB-hour
$0.015
Retained data / GiB-month
$0.02
Paused compute
$0.00

A 4 GiB agent, 100 running hours

$6.00 compute

4 GiB × 100 hours, billed by the second. Keeping 20 GiB of billable incremental data for a full billing month adds $0.40. CPU is derived from memory by the platform; choose a tier that fits your workload.

Understand storage billing →

Before migrating from ZibraLabs Distributed Compute

  1. Scope the actual cluster workload, current availability, target versus delivered scale and required filesystem/memory continuity.
  2. Retain needed distributed compute and export compatible Linux agent tools, configuration and data under the effective agreement.
  3. Test agent system retention, growth and paused files separately from cluster scheduling, parallel throughput and quoted service costs.

Common questions

Is Sandbox0 a drop-in ZibraLabs Distributed Compute replacement?

Evaluate API compatibility and product scope. These products serve different core workflows, so a change may require a different architecture.

Does keeping files also keep a running agent?

Filesystem state and volatile process state are separate. Sandbox0 requires explicit compatible memory checkpoints to preserve running processes; its default filesystem resume starts fresh processes.

Sources and review scope

The Boat team's Battleships directory is the discovery source. References below describe the product and plan discussed on this page. Check current availability, regional terms and billing units before migrating. Where the cited material does not establish a capability or price, the comparison identifies that uncertainty.

Battleships research record ↗zibralabs.ai/ ↗www.ycombinator.com/companies/zibra-labs ↗Sandbox0 RootFS capacity →

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