sandboxd
Self-hosted open-source dev sandboxes with AI agents and preview URLs.
A practical open-source choice for teams building AI apps or agent platforms that need many isolated environments on a single server. Not for solo devs or those wanting a managed cloud service. For cost-conscious teams, it beats per-seat SaaS sandbox pricing, but you trade away managed scaling and 24/7 support. If you need zero-ops, look at Modal or E2B instead. But if you're comfortable running a Docker host, sandboxd gives you per-sandbox memory limits, TLS preview URLs, and AI CLIs out of the box.
Verified 8d ago · liveness 25/100 · cite: rightaichoice.com/tools/sandboxd
- AI app-builder product teams (e.g., Lovable, Bolt, v0)
- Agent platform builders needing isolated workspaces per coding agent
- Teams managing per-user or per-branch preview environments
- Multi-app hosting for small internal tools on one server
- Developers wanting a single container for themselves (docker run suffices)
- Teams needing managed/cloud sandbox service with zero ops
- Users requiring Kubernetes-native orchestration
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Skip sandboxd if you prefer a managed sandbox service with zero ops, need multi-machine scaling or built-in rate limiting, or you're a solo dev who just needs one or two containers.
You must pay for and maintain your own server (VPS or on-premise), including hosting, bandwidth, and security updates.
sandboxd is free and open-source (MIT), so your only cost is the server (e.g., a $20 VPS). This is dramatically cheaper than per-seat SaaS sandbox providers like Modal or E2B, which charge for compute, but you trade away managed scaling and support.
In short
sandboxd — Self-hosted open-source dev sandboxes with AI agents and preview URLs. Best for AI app-builder product teams (e.g., Lovable, Bolt, v0), Agent platform builders needing isolated workspaces per coding agent, Teams managing per-user or per-branch preview environments. Free to use.
What people actually say about sandboxd — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
29 mentions across 3 sources (Hacker News, YouTube, GitHub) · researched Jun 30, 2026.
- +One-command setup on any Linux host with Docker, no Kubernetes needed.
- +Per-sandbox preview URLs with automatic routing and TLS encryption.
- +Sandboxes idle to zero memory and wake instantly on request.
- +Pre-installed AI coding agent CLIs (Claude Code, OpenCode) out of the box.
- +REST API for creating sandboxes, executing commands, and streaming results.
- −Code audits reveal critical and high-severity security vulnerabilities.
- −Very small community with limited real-world usage and support.
- −No disk quota per sandbox implemented yet.
- −10 open issues on GitHub indicate active but unfinished development.
- −YouTube comments are irrelevant (about a VR game), wasting signal.
- • No direct hidden costs, but requires a Linux server (VPS or on-prem) with Docker — may incur hosting fees.
Viability Score
How well maintained and how widely used is sandboxd? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Self-hosted with one-command install (./install.sh)
- Isolated Docker containers per sandbox
- Per-sandbox filesystem and memory limits
- Live preview URLs with automatic routing and TLS
- Sandbox idle sleep and instant wake
- Pre-installed OpenCode and Claude Code CLIs
- REST API for create, exec, write files, and streaming (SSE)
- SQLite-based state management
- Traefik HTTPS routing and termination
- Reconciler converges Docker state on reboot
- Host-memory pressure reaper for multi-tenant safety
- Custom API provider keys per sandbox
- MIT licensed for commercial use
About sandboxd
sandboxd turns a single Linux server into a fleet of isolated development environments, each running as a lightweight Docker container with its own filesystem and memory limits. It comes with OpenCode and Claude Code CLIs pre-installed, so you can spawn an AI coding agent per sandbox with zero setup. Live preview URLs with automatic routing and TLS are handled by Traefik, and sandboxes idle to zero memory usage, waking instantly on request. That makes it economical to run dozens of environments on one $20 VPS or an on-premise box you control. The typical user is a team or product builder shipping an AI app-builder SaaS like Lovable or Bolt, an agent platform that needs an isolated workspace per coding agent, or per-user/branch preview environments. If you're a solo dev who just wants one or two containers, plain old docker run is simpler — this tool shines at multi-tenant scale. Architecturally, it's a single Go binary orchestrating Docker, with Traefik for HTTPS and SQLite for state. No Kubernetes, no external database, no message queue. The whole codebase is MIT licensed and intentionally understandable in an afternoon. Setup is one command: ./install.sh. Compared to managed SaaS sandbox providers, sandboxd keeps your code, data, and API keys on hardware you own. It exposes a REST API for creating sandboxes, executing commands, writing files, and streaming results via SSE. For cost-conscious teams that want multi-tenant sandboxing without per-seat SaaS fees, it's a strong open-source foundation — though you trade away managed scaling and 24/7 support.
Behind the Verdict
sandboxd is a well-scoped open-source tool for teams that want to host their own multi-tenant sandboxing layer. Its strengths are the single-command install, the lightweight Docker-based isolation, and the pre-installed AI coding CLIs (OpenCode, Claude Code) that let you spin up an agent per sandbox without extra setup. The REST API with SSE streaming makes it easy to integrate into your own product backend, and the idle-sleep/wake behavior keeps costs low when you run many sandboxes on one VPS. However, there are real trade-offs. You are responsible for keeping the Docker host patched and available, and there is no built-in rate limiting or multi-machine scaling. Preview URLs only work if your host is publicly reachable, which may require extra networking configuration. If you are a solo dev needing just a couple of containers, plain docker run is simpler. And if you cannot handle server admin, you'll want a managed service like Modal or E2B. Where sandboxd really shines is in the AI app-builder space: you can give each end user a sandbox that runs their generated app and provides a live preview URL, with per-sandbox filesystem and memory limits to keep things safe. The MIT license means you can embed it in a commercial product without licensing worries. The codebase is intentionally small, so your team can adapt it to your needs. Given the 2026 news about Docker's own disposable sandboxes and enterprises scaling to a million concurrent sandboxes, the managed players are moving fast. But if you want full control and zero per-seat fees, sandboxd remains a strong DIY foundation.
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Real-world workflow fit
Concrete scenarios for the personas sandboxd actually fits — and what changes day-one when you adopt it.
Wants to give each user a sandboxed preview environment
Outcome: Integrate sandboxd's REST API: on user signup, create a sandbox with a pre-defined image, watch the SSE stream for build status, and expose the preview URL. Users get instant live demos with per-sandbox resource limits.
Needs isolated workspaces per coding agent
Outcome: Set up sandboxd on a single VPS, use the API to spawn a sandbox per agent run with its own filesystem and API keys. Agents run without interfering with each other, and idle sandboxes sleep to save memory.
Wants automatic preview URLs for every git branch
Outcome: Use sandboxd's API connected to your CI pipeline: on each PR, create a sandbox pointing to that branch, get a live URL with TLS, and have it automatically sleep when not accessed. This cuts infra costs while giving devs and stakeholders fast previews.
Use Cases
- Build an AI app-builder backend that live-previews every user's generated app on a shared server
- Give each coding agent an isolated sandbox with its own API keys and filesystem
- Spin per-branch preview environments for web apps that automatically sleep when not accessed
- Hand out disposable coding playgrounds to workshop attendees with resource limits
- Host multiple internal tools on a single VPS, each at its own URL, waking only when used
- Run a prompt-to-app pipeline where a user prompt triggers an agent to write and preview code in seconds
Models Under the Hood
as of 2026-08-18
Limitations
- As self-hosted software, sandboxd requires maintaining a Docker host and managing server resources.
- It does not offer managed scaling across multiple machines or built-in rate limiting per user.
- Preview URLs are only accessible if the host is reachable from the internet.
as of 2026-08-15
Verification history
We have re-verified sandboxd 5 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published sandboxd tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Self-Hosted (Open Source)
$0/mo
Ideal for
Teams that already have a Linux server (or are willing to run a VPS) and want full control over code, data, and infrastructure at no per-seat cost.
What this tier adds
This is the free, MIT-licensed starting tier: you get the entire codebase, one-command install, Docker orchestration, REST API, and TLS preview URLs - all without any subscription.
Where the pricing makes sense
The company stage and team size where sandboxd's pricing actually pencils out — and where peers do it cheaper.
sandboxd is free and open-source (MIT), so your only cost is the server (e.g., a $20 VPS). This is dramatically cheaper than per-seat SaaS sandbox providers like Modal or E2B, which charge for compute, but you trade away managed scaling and support.
Setup time & first value
How long it actually takes to get something useful out of sandboxd — broken out by persona, not the marketing-page minute.
For a backend engineer: you can have sandboxd installed and a test sandbox running in under 30 minutes (one command install plus a few API calls). For a devops lead: expect a few hours to plan networking, set up TLS, and connect to your CI pipeline. For a non-technical decision-maker: plan at least a day to get a demonstration running with a partner.
Switching to or from sandboxd
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a managed sandbox service (e.g., Modal, E2B): export your sandbox definitions and container images, then recreate them with sandboxd's API on your own server.
- →From plain docker run: move your current Docker-based workflows to sandboxd's REST API to get TLS preview URLs and automated sleep/wake.
- ↗To a managed sandbox service (e.g., Modal, E2B): export your container images and configuration, then recreate them using the new provider's API.
- ↗To Kubernetes-based orchestration: you would need to refactor your sandbox management to use K8s primitives; sandboxd is not a drop-in replacement.
Tutorials & Learning
Official links
Tools that pair well with sandboxd
Common stack mates teams adopt alongside sandboxd, with the specific reason each pairing earns its keep.
OpenHands
Open-source platform for autonomous cloud coding agents that fix bugs, review PRs, and automate workflows.
Warp
An open-source agentic development environment combining a modern terminal with tools to orchestrate coding agents at scale.
Crush
Open-source terminal AI coding assistant with multi-model support and LSP integration
Featured Head-to-Head Comparisons
Sandboxd vs Voyage Ai
If your need is high-accuracy retrieval over dense domain-specific documents (finance, legal, code), Voyage AI's specialized embedding models and rerankers are unmatched, but be prepared for enterprise pricing and sales engagement. If you're building AI agent apps (like Lovable, Bolt) that need isolated, self-hosted sandboxes per user with zero memory overhead, sandboxd's free, open-source model is a no-brainer. These tools solve completely different problems—choose based on whether your bottleneck is retrieval accuracy or sandbox orchestration.
Sandboxd vs Spider Cloud
Spider Cloud is your pick if you need fast, reliable web data extraction for AI agents or RAG pipelines. sandboxd fits if you run an AI app builder product and need to manage many isolated coding environments on your own server. They solve completely different problems; choose based on whether you need data (Spider) or sandboxes (sandboxd).
Sandboxd vs Temporal Ai
Temporal AI and sandboxd serve fundamentally different needs. Temporal AI is the right choice for teams requiring bulletproof durability and orchestration for complex, long-running workflows – think OpenAI or Replit. sandboxd is ideal for product teams that need to manage many isolated coding environments on their own infrastructure, like an AI app builder or agent platform. Evaluate based on whether you need workflow reliability (pick Temporal) or sandbox isolation (pick sandboxd).
Alternatives to sandboxd
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