YC currently lists PoplarML as Active. Confirm current hosted access, supported configurations and commercial terms directly; no shutdown date is verified. Read the source ↗

Inference APIs / PoplarML Model Deployment alternative

A PoplarML alternative for the agent around model serving

PoplarML’s YC profile describes deploying machine-learning models to GPU-backed API endpoints and currently labels the company Active. That profile establishes the model-serving product identity, but does not verify a current numeric tariff or general-purpose sandbox lifecycle. Confirm present hosted access before planning a new integration.

Start buildingCompare capabilities ↓

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

When PoplarML Model Deployment fits

PoplarML’s described scope fits teams evaluating model deployment, prediction APIs and autoscaling. Current availability, supported frameworks and service terms need direct confirmation for the intended model and traffic.

When to consider Sandbox0

Consider Sandbox0 for compatible Linux agents that build models, prepare data or orchestrate external inference, with a durable complete writable RootFS and supported files while compute pauses. The GPU model-serving layer needs separate infrastructure.

Capabilities

PoplarML Model Deployment and Sandbox0: what persists?

Feature comparison with documentation and verification status
Environment and runtime lifetimePoplarML Model DeploymentNot 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 filesPoplarML Model DeploymentThe public profile does not establish a current general runtime-disk retention contract.View evidence ↗Sandbox0The writable RootFS survives successful checkpoints, including software, system configuration, dependencies, and user data.Read documentation →
Whole RootFSPoplarML Model DeploymentDeploying a model endpoint does not establish whole writable OS filesystem persistence.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 statePoplarML Model DeploymentAutoscaling does not establish a durable user-controlled sandbox pause/resume operation.View evidence ↗Sandbox0Pause compute between tasks; resume on available capacity. Default resume starts fresh processes from the saved filesystem.Read documentation →
Memory and process statePoplarML Model DeploymentNo general process or GPU-memory checkpoint guarantee is established.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 profile describes scaling inference capacity, not automatically enlarging a complete per-user writable RootFS. Confirm model artifact and workload storage independently.

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 →

A prediction API is a specialized execution contract

The company launch describes supplying model load and prediction functions and deploying them through a CLI to an autoscaling endpoint. This is distinct from an agent issuing arbitrary Linux commands on a computer it returns to between tasks.

Source 1 ↗

Do not infer storage or RAM guarantees from deployment metadata

Before choosing a deployment service, establish where model weights, input data, output artifacts and mutable state live, and how replicas restart. The available profile does not establish whole-OS retention, paused-file mutations or durable process checkpoints.

Source 1 ↗

Confirm the complete current service quote

Request present onboarding status, supported hardware, region, model/runtime, replica minimums, scaling policy, data retention and pricing units. An Active company label is not itself a guarantee that a historical hosted plan or endpoint remains available under the same terms.

Source 1 ↗

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.

PoplarML Model Deployment

Pricing reviewed 2026-10-19

Current hosted commercial price
Confirm current access and pricing directly
No numeric current public rate card is verified.
Rate source ↗
Model and capacity configuration
Agreement-specific hardware and scaling terms
Do not infer a generic device-hour tariff from a historical model-deployment description.
Rate source ↗

YC lists Active, so a shutdown date is not asserted. Confirm present service availability, effective pricing and data guarantees before migration. A hosted prediction endpoint and a persistent Linux agent have different capacity, latency and lifecycle requirements.

Check current PoplarML Model Deployment 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 PoplarML Model Deployment

  1. Confirm current provider access and locate available model code, artifacts, schemas, inputs and outputs under the actual account agreement.
  2. Keep or replace required GPU inference and port only compatible Linux agent build, data preparation and orchestration stages.
  3. Test model behavior separately from agent system retention, growth, supported paused-file access and the complete service budget.

Common questions

Is Sandbox0 a drop-in PoplarML Model Deployment 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 ↗www.ycombinator.com/companies/poplarml ↗Sandbox0 RootFS capacity →

Related comparisons

Baseten Model Serving / Deployments vs Sandbox0 →fal Model APIs, Serverless and Compute vs Sandbox0 →OpenAGI / Lux vs Sandbox0 →Pipeshift Inference Platform vs Sandbox0 →Replicate Model APIs vs Sandbox0 →RunAnywhere Wally and Local SDKs vs Sandbox0 →