Inference APIs / Replicate Model APIs alternative

A Replicate alternative for persistent agent execution

Replicate serves model predictions and dedicated model deployments. Public models may charge by hardware time or by inputs and outputs; private deployments have additional lifecycle costs. Model serving is a different service from a durable general-purpose Linux agent environment.

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

When Replicate Model APIs fits

Replicate fits applications consuming supported model APIs or deploying custom models with managed prediction endpoints, hardware selection and scaling. A Linux sandbox does not replace those models or accelerator capacity automatically.

When to consider Sandbox0

Use Sandbox0 for compatible Linux agents preparing data, building tools or orchestrating model calls, with complete writable RootFS persistence, online growth and supported paused-file operations. Keep needed model-serving APIs.

Capabilities

Replicate Model APIs and Sandbox0: what persists?

Feature comparison with documentation and verification status
Environment and runtime lifetimeReplicate Model APIsNot 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 filesReplicate Model APIsPrediction artifacts have interface-specific retention, not a documented general persistent writable guest disk.View evidence ↗Sandbox0The writable RootFS survives successful checkpoints, including software, system configuration, dependencies, and user data.Read documentation →
Whole RootFSReplicate Model APIsModel deployments and Cog packaging do not establish durable arbitrary runtime OS changes.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 stateReplicate Model APIsScale-to-zero model serving does not establish full-machine pause/resume for a returning agent.View evidence ↗Sandbox0Pause compute between tasks; resume on available capacity. Default resume starts fresh processes from the saved filesystem.Read documentation →
Memory and process stateReplicate Model APIsPrediction state and output records are not documented 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.

Deployment instance scaling and model hardware selection do not establish automatic online growth of a durable per-agent 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.

See paused file operations →

Private deployment costs include more than inference

Hardware-billed public models charge while processing. Private models generally charge setup, idle and active time; fast-booting fine-tunes are an explicit active-processing-only exception. Dedicated deployments expose minimum and maximum instance counts, so scale-to-zero and always-warm configurations produce different bills.

Source 1 ↗Source 2 ↗

Save API prediction artifacts to durable application storage

By default, API prediction inputs, outputs, files and logs are removed after one hour. The web interface has different retention: its prediction data is kept indefinitely. Export necessary API artifacts rather than assuming every model output is a persistent agent filesystem.

Source 1 ↗

Prediction lifecycle is not a machine checkpoint

A prediction has execution status and results within a model-serving runtime. Packaging a model with Cog and scaling deployment instances do not establish whole writable OS retention or saved agent RAM between unrelated tasks.

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.

Replicate Model APIs

Pricing reviewed 2026-10-19

Nvidia H100 reference
$0.001525 per second ($5.49/hour)
One GPU; hardware-billed model configuration.
Rate source ↗
Nvidia A100 80 GB reference
$0.0014 per second ($5.04/hour)
Rate source ↗
CPU-small reference
$0.000025 per second ($0.09/hour)
1 CPU and 2 GB RAM.
Rate source ↗
FLUX 1.1 Pro reference
$0.04 per output image
A model-output price, separate from hardware-second pricing.
Rate source ↗

Model-specific input/output prices and hardware-time prices use different meters. Private deployment setup and idle charges, minimum instances and fast-boot exceptions must be included. These accelerator rates are not equal-performance Linux sandbox quotes.

Check current Replicate Model APIs 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 Replicate Model APIs

  1. Inventory model APIs, versions, private deployments, output retention and accelerator requirements.
  2. Export needed API prediction files before their retention deadline; keep or replace serving endpoints separately.
  3. Port compatible Linux agent stages and test durable system paths, growth, paused files and combined model plus agent costs.

Common questions

Is Sandbox0 a drop-in Replicate Model APIs 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 ↗replicate.com/pricing ↗replicate.com/docs/topics/deployments ↗replicate.com/docs/topics/predictions/data-retention ↗replicate.com/docs/topics/predictions/lifecycle ↗Sandbox0 RootFS capacity →

Related comparisons

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