GPU clouds / Together AI GPU Clusters alternative

A Together AI GPU clusters alternative for the agent around training

Together AI GPU Clusters provides accelerator capacity with managed Kubernetes or Slurm and independent shared storage. Shared volumes survive cluster deletion, while node-local disks are explicitly ephemeral. Together’s model APIs and Code Sandbox are separate products with separate prices and state contracts.

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

When Together AI GPU Clusters fits

Together GPU Clusters fits distributed training and inference needing accelerator fleets, fast interconnects, cluster orchestration and shared filesystems. Retain these services where required; an agent sandbox does not replace a distributed GPU cluster.

When to consider Sandbox0

Consider Sandbox0 for compatible Linux agents preparing, evaluating or orchestrating cluster work, with a complete durable writable RootFS, enabled automatic online growth and supported paused RootFS operations.

Capabilities

Together AI GPU Clusters and Sandbox0: what persists?

Feature comparison with documentation and verification status
Environment and runtime lifetimeTogether AI GPU ClustersNot 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 filesTogether AI GPU ClustersStatic PV-backed shared storage persists independently across cluster deletion, node recreation and rescheduling.View evidence ↗Sandbox0The writable RootFS survives successful checkpoints, including software, system configuration, dependencies, and user data.Read documentation →
Whole RootFSTogether AI GPU ClustersShared mounted data persists, while local NVMe and node-local disks can be lost during migration or recreation; this is not retained complete node OS state.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 stateTogether AI GPU ClustersOn-demand clusters support stop/start with independently retained shared storage; reservations and credit exhaustion have separate lifecycle rules.View evidence ↗Sandbox0Pause compute between tasks; resume on available capacity. Default resume starts fresh processes from the saved filesystem.Read documentation →
Memory and process stateTogether AI GPU ClustersCluster stop/start and retained shared data do not establish durable arbitrary GPU/RAM process snapshots.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.

Together already supports freely increasing shared-volume capacity, with decreases handled by the account team. That storage scaling is separate from automatic online growth of each agent’s complete durable writable RootFS.

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.

Independent shared volumes remain available after cluster deletion and can be reattached to suitable compute. That does not establish a direct paused-node whole-root file API. Sandbox0 targets its durable paused RootFS under filesystem-only mutation and retained-memory read rules.

See paused file operations →

Shared storage already survives cluster and node replacement

Together documents shared volumes across pod rescheduling, node migration/recreation, scaling and cluster deletion. Static PersistentVolumes preserve the data and can be reattached. Local NVMe and node-local disks are explicitly ephemeral and can be lost during maintenance or other cluster operations; keep indispensable state on shared storage.

Source 1 ↗Source 2 ↗

On-demand lifecycle and reservations have different commitments

On-demand clusters can be stopped and started and run until terminated. Reservations charge the full duration upfront and are non-refundable; clusters are decommissioned when that period ends unless extended. Storage has an independent billed lifecycle and must be deleted separately. These lifecycle controls do not establish a universal checkpoint of running GPU processes.

Source 1 ↗

Use the correct GPU cluster price column

The current price table separates discounted preemptible, on-demand and term-dependent reserved GPU rates. H100 on-demand is $3.99 per GPU-hour, while $1.99 is in a separate Preemptible Compute column. Inference tokens, Code Interpreter sessions and Code Sandbox CPU/memory rates are different products and are not cluster GPU-hour prices.

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.

Together AI GPU Clusters

Pricing reviewed 2026-10-19

HGX H100 on-demand
$3.99 per GPU-hour
Do not substitute the separate $1.99 Preemptible Compute column.
Rate source ↗
HGX H200 on-demand
$5.99 per GPU-hour
Rate source ↗
HGX B200 on-demand
$8.19 per GPU-hour
Rate source ↗
Shared filesystem
$0.16 per GiB-month
Rate source ↗
H100 reserved reference
From $3.19 per GPU-hour
Term-dependent commitment; full reserved duration is charged.
Rate source ↗

Multiply per-GPU rates by the actual cluster GPU count and duration. Preemptible, on-demand and reservation terms differ, and reserved capacity is not partially refundable. Retained shared volumes continue billing independently. Confirm the selected storage service, region, capacity and account credits.

Check current Together AI GPU Clusters 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 Together AI GPU Clusters

  1. Record cluster GPU count/type, preemptible/on-demand/reserved terms, node-local paths, static PVs and shared-volume retention.
  2. Export node-local tools and files before replacement and retain required GPU training/inference capacity.
  3. Port compatible Linux agent stages; test system retention, growth and paused files separately from cluster performance and reservation/storage costs.

Common questions

Is Sandbox0 a drop-in Together AI GPU Clusters 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 ↗docs.together.ai/docs/cluster-storage.md ↗docs.together.ai/docs/gpu-clusters-billing.md ↗www.together.ai/pricing ↗www.together.ai/gpu-clusters ↗Sandbox0 RootFS capacity →

Related comparisons

Azure GPU VMs (NC_A100_v4 reference) vs Sandbox0 →Cerebrium Serverless Compute vs Sandbox0 →Crusoe vs Sandbox0 →DigitalOcean GPU Droplets vs Sandbox0 →Fluidstack vs Sandbox0 →Google Compute Engine GPU VMs vs Sandbox0 →