E2B Alternatives for Self-Hosted AI Sandboxes 2026

A developer from Temps evaluated E2B alternatives for self-hosted AI sandboxes in 2026, highlighting Modal, Daytona, Morph Cloud, and Firecracker/gVisor as key options. The analysis centers on two questions: whether users can run their own Linux host and whether kernel-level isolation is needed. Managed solutions like E2B and Modal offer zero-ops convenience, while self-hosted options like Temps and Daytona provide cost savings and data residency at scale.

{/ meta: see blog-posts.ts — slug: best-e2b-alternatives-ai-sandboxes-2026 quick answer: The main E2B alternatives in 2026 are Modal, Daytona, Morph Cloud, Temps, and raw Firecracker/gVisor. Which one fits is decided by two questions: can you run your own Linux host, and do you need kernel-level isolation? Managed E2B, Modal, Morph wins on zero ops; self-hosted Temps, Daytona wins on cost at volume and data residency. Temps is our own platform. /} The main E2B alternatives for AI agent sandboxes in 2026 are Modal , Daytona , Morph Cloud , Temps , and building directly on Firecracker or gVisor . There is no single winner: the choice collapses to two questions, which are whether you can run your own Linux host and whether your code is trusted. TL;DR:E2B provides polished managed sandboxes, billed per second on vCPU plus RAM about $0.0000303/s for its default 2 vCPU / 512 MiB sandbox . If you want to stay managed, E2B is still the strongest general option, Modal is better for Python and GPU workloads, and Morph Cloud is better if you need to snapshot and fork agent state. If per-second billing or data residency is the problem, the self-hosted options are Temps and Daytona, though Daytona's open-source repo has been unmaintained since June 2026, and Temps is the one that also offers Firecracker microVM isolation. Costs diverge sharply at volume: sandbox compute that runs ~$545/mo on E2B runs on a ~$6/mo VPS self-hosted. Who wrote this:I build Temps, including the Firecracker sandbox backend described below, so the implementation details here come from having written them rather than from a vendor datasheet. That also means I have an interest in one of these tools, which is why the next paragraph exists. Disclosure:Temps publishes this blog, so treat our section as a vendor describing its own product. We have tried to describe every other tool the way its own documentation does, and to be specific about the cases where E2B, Modal, or Morph Cloud is the better choice. Pricing figures are as published at the time of writing and move often, so check each vendor's current pricing page before you commit to anything. The first question is whether you can run your own Linux host. If you cannot, or do not want to, the answer is a managed sandbox and E2B is a strong default. If you can, self-hosting removes per-compute-second billing entirely and keeps code on your infrastructure, at the cost of operating a server. The second question, whether the code you execute is trusted, decides how much isolation you need on top. | If your priority is… | Pick | Why | |---|---|---| | Zero infrastructure work | E2B | Managed Firecracker microVMs, mature SDK, integrations with every major agent framework | | Python ML and GPU workloads | Modal | On-demand A10/A100/H100, per-millisecond billing | | Snapshotting and forking agent state | Morph Cloud | Pause mid-execution and branch into parallel trajectories | | Predictable cost at high volume | Temps or Daytona | Flat VPS cost instead of per-execution billing | | Kernel-level isolation on your own hardware | Temps | Firecracker microVM backend alongside Docker | The honest summary: E2B is hard to beat if managed is acceptable to you. Its SDK is more mature than anything in the self-hosted category, and the one-time $100 credit covers a lot of prototyping. The self-hosted options become compelling at two specific thresholds, which are when your sandbox bill outgrows a VPS, and when your compliance posture will not allow code execution in someone else's cloud. | Platform | Type | Sandbox Tech | Free Tier | Starting Price | Data Residency | AI Gateway | Best For | |---|---|---|---|---|---|---|---| E2B | Managed cloud | Firecracker microVMs | $100 credit | Usage-based | E2B cloud | No | Low-latency managed sandboxes | Modal | Managed cloud | gVisor containers | $30 credit | Usage-based | Modal cloud | No | Python ML workloads | Morph Cloud | Managed cloud | Firecracker microVMs | 300 MCU credit | From $40/mo | Morph cloud | No | Snapshotting agent state | Temps | Self-hosted | Docker containers or Firecracker microVMs | Yes self-host | ~$6/mo VPS | Your server | Built-in | Self-hosted AI agents | Daytona | Self-hosted/Cloud | OCI/Docker sandboxes | Yes self-host | Free AGPL, repo unmaintained | Your server | No | Stateful agent workspaces | Firecracker/gVisor | DIY | Firecracker or gVisor | N/A | VPS cost | Your server | No | Custom isolation requirements | Since Temps is our product, here is the comparison including the places E2B wins, not just the places we do. E2B | Temps | | |---|---|---| Time to first sandbox | Minutes sign up, call the API | Longer provision a server, install, and run temps firecracker setup for microVMs | Operational burden | None, fully managed | You run and patch the host | SDK maturity | Purpose-built Code Interpreter SDK, years of iteration | Younger REST API and CLI, no Jupyter-specific SDK | Framework integrations | LangChain, LlamaIndex, OpenAI Assistants, CrewAI | Generic HTTP, no first-party agent-framework adapters | Free tier | One-time $100 in credits, 1-hour max session, 20 concurrent sandboxes | Free to self-host, but you pay for the server | Boot time | ~150–200 ms | Sub-second on Docker; Firecracker adds microVM boot plus a one-off rootfs conversion per image | Runs on macOS | Not applicable managed | Docker backend only; Firecracker needs Linux with KVM | Sandbox technology | Firecracker microVMs | Docker containers or Firecracker microVMs, per sandbox | Isolation level | Kernel-level Firecracker | Process-level Docker or kernel-level via KVM Firecracker | Self-hostable | Yes e2b-dev/infra , Terraform + Nomad on GCP/AWS | Yes | Pricing model | Per second, vCPU + RAM billed separately ~$0.0000303/s at the default 2 vCPU / 512 MiB | ~$6/mo flat VPS | Data residency | E2B cloud | Your server | AI Gateway | None | Built-in OpenAI/Anthropic/Gemini/Grok | Error tracking / analytics | None | Built-in | Deployment platform | No | Yes full PaaS | License | Apache 2.0 SDK and infra | Apache 2.0 | Read that top block honestly: if you are prototyping, E2B gets you running today and Temps does not. The self-hosted case is about what happens at volume and under compliance constraints, not about being easier to start with. E2B launched in 2023 and quickly became the default sandbox layer for AI coding agents, powering integrations with OpenAI Assistants, LangChain, and dozens of agent frameworks. Its Firecracker-based microVMs boot in roughly 150–200 ms and offer genuine kernel-level isolation. But as production AI workloads scale, three friction points drive teams toward alternatives: E2B bills per second, charging vCPU and RAM separately: $0.000028/s for the default 2 vCPU, plus $0.0000045 per GiB per second. A default sandbox therefore costs about $0.0000303/s. An agent running 10 minutes of execution per task across 1,000 daily tasks generates 18 million compute-seconds a month, or roughly $545 for sandbox compute alone. Add network egress and storage, and the bill grows. A $6/mo Hetzner VPS running Temps handles the same workload with no per-execution fees. Every code snippet, file read, and environment variable passed to an E2B sandbox transits E2B's cloud. For teams under SOC 2, HIPAA, or EU data-residency requirements, that is a blocker. Self-hosted alternatives keep all execution on the team's own servers. Temps documents exactly what stays on your machine in its data ownership and privacy guide https://dev.to/docs/data-ownership-and-privacy . E2B is a sandbox layer — not a deployment platform, not an observability stack, not an AI gateway. Teams running agents in production still need error tracking, request logging, LLM cost monitoring, and a deployment pipeline. Every additional SaaS tool adds a billing line and a data-sharing agreement. E2B uses Firecracker microVMs to provide genuine kernel-level isolation for each sandbox. Firecracker was originally built by AWS to power Lambda and Fargate, and E2B's implementation boots new VMs in roughly 150–200 ms — fast enough for interactive agent workflows. Citation Capsule:E2B's Code Interpreter SDK is used by major AI frameworks including LangChain, LlamaIndex, and OpenAI's Assistants. E2B sandboxes run on Firecracker microVMs developed by AWS, providing full kernel isolation and a fresh Linux environment per sandbox. Pricing is per compute-second. E2B's managed model removes all server operations. Call the API, get a sandbox, run code, get the output. The SDK integrates with every major AI framework: python from e2b code interpreter import Sandbox with Sandbox as sandbox: execution = sandbox.run code "import pandas as pd; df = pd.DataFrame {'a': 1, 2, 3 } ; print df " print execution.text The Hobby tier gives a one-time $100 in credits, with sessions capped at 1 hour and 20 concurrent sandboxes. Beyond that, billing is usage-based, and there is a Pro plan at $150/mo plus usage. Teams prototyping AI agents, early-stage products needing zero infrastructure, and workloads requiring strict kernel-level isolation for untrusted user-submitted code. This is our product, so weigh it accordingly. The specifics below are checkable against the source, and the limitations section is not decorative. Temps does not force a single isolation model. It ships two sandbox backends behind one API: Docker containers with isolated networking and ephemeral filesystems, and Firecracker microVMs running on KVM. For most AI agent workloads running Python scripts, executing shell commands, reading and writing files , Docker isolation is sufficient and boots in sub-second time from pre-pulled images. When the threat model calls for a hardware-virtualized boundary, the same sandbox request runs as a microVM instead. The specific claim:among the self-hosted options here, Temps is the one that offers Firecracker microVM isolation as a supported backend, chosen per sandbox, alongside an AI Gateway, a Sentry-compatible error tracker, and a deployment platform in a single Apache 2.0 binary. That combination is the argument for it. It is not the fastest way to get a sandbox running, and on a host without KVM the microVM half of that claim does not apply. When you run the AI Autofixer, Temps clones the production container into an isolated sibling container, applies the code fix inside that sandbox, runs tests, and destroys the sandbox. The pattern is reusable for any agent workload: // POST /api/v1/sandboxes/{id}/exec — runs inside the sandbox, which has // isolated networking, an ephemeral filesystem, and a clean environment const res = await fetch https://your-temps-instance.com/api/v1/sandboxes/${sandboxId}/exec , { method: "POST", headers: { Authorization: Bearer ${process.env.TEMPS API KEY} }, body: JSON.stringify { cmd: "python", "main.py" , env: { OPENAI API KEY: process.env.OPENAI API KEY }, cwd: "/workspace", } , }, ; const { stdout, stderr, exit code } = await res.json ; The same thing from the CLI, which is often what you actually reach for while debugging an agent: bunx @temps-sdk/cli sandbox exec <sandbox-id -- python main.py Secrets reach the sandbox through the platform rather than through your prompt, which the agent sandbox secrets documentation https://dev.to/docs/agent-sandbox-secrets covers in detail. The AI Gateway https://dev.to/docs/ai-gateway routes LLM calls across providers through a single OpenAI-compatible endpoint: python import OpenAI from "openai"; // Replace api.openai.com with your Temps AI Gateway const client = new OpenAI { baseURL: "https://your-temps-instance.com/api/ai-gateway/v1", apiKey: process.env.TEMPS API KEY, } ; const response = await client.chat.completions.create { model: "anthropic/claude-sonnet-4-6", // or openai/gpt-4o, google/gemini-2.0-flash messages: { role: "user", content: "Fix this bug: ..." } , } ; Yes. Temps runs Firecracker microVMs as a first-class sandbox backend alongside Docker, and you pick the backend per sandbox. A Firecracker sandbox is a real KVM virtual machine with a pinned guest kernel and its own ext4 root filesystem. Because the sandbox no longer shares the host kernel, a container-escape or kernel-level exploit inside it hits the hypervisor boundary instead of your host, which is a materially smaller attack surface than namespaces and cgroups. Docker stays the default; Firecracker is opt-in per host. Selecting the backend is one optional field on the sandbox API see the REST API reference https://dev.to/docs/api for the full request shape : // POST /api/v1/sandboxes — omit backend to use the host default Docker const sandbox = await fetch "https://your-temps-instance.com/api/v1/sandboxes", { method: "POST", headers: { Authorization: Bearer ${process.env.TEMPS API KEY} }, body: JSON.stringify { image: "python:3.12", backend: "firecracker", // kernel-level isolation via KVM disk size mb: 2048, // Firecracker only; the Docker backend ignores it } , } ; Requesting firecracker on a host that cannot run it returns a 400 with the reason, rather than silently downgrading to a container. Isolation is a property you asked for, so Temps refuses to quietly give you less of it. An operator enables the backend once per host: Provisions the pinned Firecracker binary, guest kernel, network, then boots a smoke-test VM. The backend stays disabled unless that smoke test passes. temps firecracker setup Probe the host without changing anything temps firecracker setup --check What this needs: a Linux host with KVM available at /dev/kvm . That rules out macOS dev machines and most budget VPS plans without nested virtualization, which is exactly why Docker remains the default. Bare metal and nested-virt-enabled cloud instances run it fine, and the choosing a server guide https://dev.to/docs/choosing-a-server covers the provider-by-provider specifics. The Docker daemon is still required either way, because Temps uses it as the image toolchain: your sandbox image is pulled and exported through Docker, then converted to an ext4 root filesystem and cached per image digest. Honest scope note: Firecracker support shipped in the 0.1.0 beta line and is opt-in. Two hardening items from the design are deliberately deferred: VMs currently run as the server's own user rather than under Firecracker's jailer still KVM-isolated, but one layer short of the target , and snapshot-based pause and resume is not implemented yet. If snapshot-and-fork is central to your agent architecture, Morph Cloud still leads on that specific capability. | Workload | E2B default 2 vCPU / 512 MiB | Temps ~$6/mo flat | |---|---|---| | 100 tasks/day × 10 min each | ~$54/mo | ~$6/mo | | 1,000 tasks/day × 10 min each | ~$545/mo | ~$6/mo | | 10,000 tasks/day × 10 min each | ~$5,445/mo | ~$6/mo + larger VPS | /dev/kvm , so macOS and most budget VPS plans can only run the Docker backendTeams running AI agents at scale on self-hosted infrastructure who need sandbox execution + LLM routing + observability without per-execution billing or cloud data residency, including teams that need kernel-level isolation for untrusted code but cannot ship that code to a managed cloud. Modal is a serverless cloud platform for running Python functions at scale. While not a sandbox SDK in the E2B sense, Modal is widely used as an AI execution environment because it provisions GPU compute on demand, boots in seconds, and bills per millisecond of actual CPU/GPU use. Citation Capsule:Modal provides serverless GPU and CPU compute for Python functions, with sub-second cold starts and billing per millisecond. It supports CUDA, common ML libraries, and custom container images. New accounts receive $30 in free credits. Modal shines for Python-heavy AI workloads — fine-tuning, inference, batch embedding, and agent pipelines that need GPU access. You define your function, specify the container environment, and Modal handles provisioning: python import modal app = modal.App "my-agent" @app.function image=modal.Image.debian slim .pip install "anthropic" def run agent prompt: str - str: import anthropic client = anthropic.Anthropic message = client.messages.create model="claude-sonnet-4-6", max tokens=1024, messages= {"role": "user", "content": prompt} , return message.content 0 .text AI teams running Python ML workloads — fine-tuning, batch inference, embedding pipelines — that need GPU access without managing GPU servers. Daytona is an open-source sandbox runtime for running AI-generated code. Its README describes it as "secure and elastic infrastructure for AI-generated code execution and agent workflows", built on OCI/Docker compatibility, with sandboxes it claims spin up in under 90 ms. Read this before choosing it:on 11 June 2026 Daytona moved core development to a private codebase. The public daytonaio/daytona repository now carries a notice that it "is no longer maintained" and "will receive no further updates, fixes, or releases". It stays usable under its existing licence, which isAGPL-3.0, a strong copyleft network licence with materially different obligations from Apache 2.0. Treat the open-source version as a frozen fork rather than a maintained project. Daytona suits agents that need a persistent, stateful workspace a cloned repo, installed dependencies, a running dev server rather than a one-shot execution session. Note that earlier write-ups, including an earlier version of this post, described Daytona as a Dev Containers manager. That was its pre-2025 positioning; it no longer markets Dev Container support. The Daytona SDK creates and manages workspaces programmatically: js import { Daytona } from "@daytonaio/sdk"; const daytona = new Daytona ; const workspace = await daytona.create { language: "typescript", } ; const response = await workspace.process.code run "console.log 'hello from sandbox' " ; console.log response.result ; await daytona.remove workspace ; Teams that want a self-hosted sandbox runtime and are comfortable running a frozen AGPL codebase, or who intend to use Daytona's managed offering rather than the public repo. Morph Cloud is a Firecracker-based sandbox platform built specifically for AI agent workflows. Its standout feature is snapshot and resume: you can pause a running sandbox mid-execution, save its full state memory, filesystem, network connections , and fork it into parallel branches for exploring multiple agent trajectories simultaneously. Citation Capsule:Morph Cloud provides Firecracker microVM sandboxes with snapshot, fork, and resume capabilities — allowing AI agent workflows to branch at any execution point without restarting. This enables parallel agent exploration and caching of expensive setup steps. For agent workflows that explore multiple paths from a common starting point — evaluating several code fixes, exploring different prompt strategies, running tree-of-thought reasoning — Morph Cloud's snapshot and fork model eliminates redundant setup. Instead of booting a new sandbox for each branch, you snapshot after the setup phase and fork from there. python import morphcloud client = morphcloud.MorphCloudClient Boot and set up the sandbox once sandbox = client.sandboxes.create vcpus=2, memory=512, disk size=700 sandbox.exec "pip install -r requirements.txt" Snapshot after setup snapshot = sandbox.snapshot Fork into parallel branches for different fixes branch a = snapshot.fork branch b = snapshot.fork Run different agent strategies in parallel result a = branch a.exec "python fix strategy a.py" result b = branch b.exec "python fix strategy b.py" AI agent workflows that explore multiple solution paths in parallel from a shared starting state — tree-of-thought reasoning, parallel code fix evaluation, A/B prompt testing. Some teams building multi-tenant AI platforms, where users submit arbitrary code for execution, require kernel-level isolation on infrastructure they control. Building directly on Firecracker AWS's open-source microVM technology or gVisor Google's user-space kernel is the deepest option. Worth checking before you commit to it: the main reason to build this yourself used to be that no self-hosted platform gave you Firecracker with a usable API on top. Temps now ships exactly that, so the DIY path is mainly for teams whose requirements fall outside what its backend does, such as snapshot-and-fork, custom device models, or a non-KVM hypervisor. Citation Capsule:Firecracker is AWS's open-source microVM VMM, written in Rust, originally built to power Lambda and Fargate. gVisor is Google's open-source user-space kernel that intercepts system calls. Both provide isolation beyond Linux namespaces and cgroups. Both require significant engineering investment: VM networking, image management, lifecycle management, and security hardening. This is the DIY path — you build the sandbox layer that E2B sells as a managed API. Teams building multi-tenant AI platforms where users submit arbitrary code, and where container-level isolation Docker is insufficient for the threat model. For a production AI agent running 1,000 tasks per day, each consuming 10 minutes of sandbox compute: | Platform | Monthly Cost 1K tasks/day × 10 min | Isolation Level | Data Residency | |---|---|---|---| E2B | ~$545 default sandbox size | Firecracker kernel | E2B cloud | Temps | ~$6–12 flat VPS | Docker process or Firecracker kernel | Your server | Modal | Variable per-ms CPU | gVisor user-space kernel | Modal cloud | Daytona | ~$6–12 flat VPS | Docker | Your server | Morph Cloud | Variable MCU-hours, $0.05/MCU | Firecracker kernel | Morph cloud | Firecracker DIY | VPS + engineering cost | Firecracker kernel | Your server | The self-hosted options Temps, Daytona, DIY win on cost at scale. The managed options E2B, Modal, Morph Cloud win on zero operational overhead. The isolation spectrum runs from Docker process-level through gVisor user-space kernel to Firecracker full VM , so choose based on your threat model. The spectrum used to map cleanly onto the cost table: cheap self-hosted meant process isolation, and kernel isolation meant per-second cloud billing. Temps breaks that pairing by offering both backends on hardware you already pay a flat rate for, provided that hardware exposes KVM. There is no single best one; it depends on whether you can run your own Linux host. If you want managed sandboxes with no server to operate, E2B remains the strongest general choice, Modal leads for Python ML workloads needing GPUs, and Morph Cloud is the pick if you need to snapshot and fork agent state mid-execution. If per-compute-second billing or data residency rules out a managed cloud, the self-hosted options are Temps and Daytona , where Daytona suits agents that navigate full dev environments and Temps suits teams that also want kernel-level Firecracker isolation, LLM routing, and observability from one binary. Temps is our product. The cost crossover is the practical trigger: sandbox compute billed around $545/mo on E2B runs on a ~$6/mo VPS self-hosted. Yes. Temps runs Firecracker microVMs as a first-class sandbox backend alongside Docker, selectable per sandbox with an optional backend: "firecracker" field on the sandbox API. Each Firecracker sandbox is a KVM virtual machine with a pinned guest kernel and its own root filesystem, giving the same class of kernel-level isolation E2B and Morph Cloud provide, on hardware you own. An operator enables it once per host with temps firecracker setup , which provisions the pinned Firecracker binary, guest kernel, and network, then boots a smoke-test VM before the backend is marked available. Docker remains the default backend. A Linux host with KVM exposed at /dev/kvm . Bare-metal servers and cloud instances with nested virtualization enabled qualify; macOS machines and most budget VPS plans do not, and those hosts fall back to the Docker backend. Docker itself is still required on Firecracker hosts because Temps uses it to pull and export sandbox images before converting them to the ext4 root filesystem each microVM boots from. Requesting the Firecracker backend on a host without KVM returns a descriptive error instead of silently downgrading to a container. E2B provides sandboxed code execution environments for AI agents. Its primary use case is running LLM-generated code safely — an AI agent writes Python or JavaScript, E2B executes it in an isolated Firecracker microVM, and returns stdout, stderr, and any generated files. It is used in AI coding assistants, autonomous agents, and data analysis pipelines where the AI needs to run code as part of its reasoning process. Temps' container execution model supports running Jupyter kernels inside containers, but it does not provide a Jupyter-specific SDK like E2B's Code Interpreter. Teams using Temps for agent workflows typically exec Python scripts directly rather than through a Jupyter kernel. If native Jupyter kernel execution is a hard requirement, E2B's Code Interpreter SDK is the better fit. It depends on your threat model. Docker isolation Linux namespaces + cgroups is sufficient for first-party AI agents running your code, your prompts, your execution. For multi-tenant platforms where end users submit arbitrary code, Docker isolation has a larger attack surface than Firecracker microVMs or gVisor, because every container shares the host kernel. The majority of AI coding agent deployments run first-party code and are well-served by Docker-level isolation. On Temps this is no longer an either/or decision: run the Docker backend for trusted first-party agents and switch specific sandboxes to the Firecracker backend when you need a hardware-virtualized boundary for untrusted code. The security overview https://dev.to/docs/security sets out the wider platform threat model. E2B does not include an AI Gateway. Temps' built-in AI Gateway https://dev.to/docs/ai-gateway proxies OpenAI, Anthropic, Gemini, and Grok through a single OpenAI-compatible endpoint, with unified cost tracking, request logging, and rate limiting, replacing tools like Helicone or LiteLLM. For teams running AI agents that call multiple LLM providers, Temps' bundled gateway eliminates a separate SaaS dependency. We wrote up why the gateway belongs in the platform https://dev.to/blog/ai-gateway-self-hosted-paas and how to build multi-provider LLM routing https://dev.to/blog/how-to-build-ai-gateway-multiple-llm-providers separately. Yes. E2B publishes an official self-hosting guide https://github.com/e2b-dev/infra/blob/main/self-host.md for Google Cloud and AWS, and both e2b-dev/E2B and e2b-dev/infra are Apache-2.0 licensed. An earlier version of this post said otherwise, which was wrong. The real distinction is what self-hosting costs you to operate. E2B's path provisions a cluster: Packer, Terraform and Nomad, with the SDK pointed at your own domain. That is a reasonable trade if you want E2B's SDK on your own infrastructure, but it is a different weight class from installing a single binary. Temps ships its Firecracker backend in the same binary as the rest of the platform, provisioned with temps firecracker setup , and falls back to Docker on hosts without KVM. Pick E2B self-hosted if you want its SDK and can run a Nomad cluster; pick Temps if you want one process to install and observability bundled in. If you are not sure, start with E2B. The free tier is generous enough to prove out an agent, and you will learn what your real execution volume and isolation requirements are before committing to infrastructure. Migrating to a self-hosted sandbox later is a smaller job than picking the wrong architecture now. Choose E2B if you want kernel-level isolation with zero infrastructure management and a polished SDK that integrates with every major AI framework, and you are comfortable with per-compute-second billing and code execution in E2B's cloud. Choose Modal if your AI workloads are Python-heavy and need on-demand GPU compute for fine-tuning or inference. Choose Morph Cloud if your agent architecture explores multiple solution paths in parallel and you need to snapshot and fork sandbox state at mid-execution points. No self-hosted option matches this today, ours included. Choose Daytona if your agents need persistent stateful workspaces rather than one-shot execution, and you have accepted that the open-source repository is unmaintained as of June 2026 and is AGPL-3.0. Choose Temps our platform if your sandbox bill has outgrown a VPS or compliance rules out a managed cloud, and you want Docker or Firecracker isolation per sandbox with LLM routing and observability in one Apache 2.0 binary. The quickstart https://dev.to/docs/quickstart covers setup. Skip it if you need a Jupyter-native SDK, GPU sandboxes, or snapshot-and-fork. Build on Firecracker/gVisor only if your requirements fall outside what a platform-managed microVM backend covers, such as snapshot-and-fork, custom device models, or a non-KVM hypervisor. The engineering cost is high, and it is now avoidable for the common case. The AI sandbox market is moving fast, and the old tradeoff is breaking down. Kernel-level isolation no longer implies a managed cloud and per-second billing, because self-hosted platforms now run Firecracker directly. The question is no longer just "how isolated is the sandbox?" but "what is the total cost of running agents at production scale, and where does my data live?"