How fast can Daytona create a sandbox?

Sub-90ms for cold starts, with some configurations reaching 27ms. This is among the fastest in the market. The speed matters for AI agents that need to spin up, execute code, and return results in real time.

Is Daytona secure enough for production use?

Daytona uses Docker container isolation by default, with optional Kata Containers for hardware-level isolation. The company is HIPAA and SOC 2 compliant. For security-critical workloads, enable Kata Containers for stronger isolation comparable to Firecracker microVMs.

How much does a typical sandbox cost?

A default sandbox (1 vCPU, 1 GB RAM) costs roughly $0.067/hour while running. Stopped sandboxes only pay for storage. Archived sandboxes use cheaper cold storage. New accounts get $200 in free credits to evaluate the platform.

Secure sandbox infrastructure for running AI-generated code with sub-90ms startup times and GPU support. Usage-based pricing at ~$0.05/vCPU/hour, $200 free credits at signup.

Daytona is a strong choice for AI teams that need fast, secure sandboxes for running agent-generated code. The sub-90ms startup times and GPU support are genuine competitive advantages. Default resource limits and Docker-based isolation are trade-offs worth understanding, but the open-source model, generous free credits, and startup program lower the barrier to trying it. We rate it 7.9/10.

What are the best Daytona alternatives?

The top Daytona alternatives are E2B, Modal and Fly.io Sprites. Each is compared in the alternatives section of this review.

Daytona Review 2026: 7.9/10, Pricing and Verdict

TLDR

Daytona provides secure sandbox infrastructure for running AI-generated code, with sub-90ms startup times and GPU support. Best for: AI/ML engineers building agentic applications that need safe code execution. Price: pure usage-based at ~$0.05/vCPU/hour, $200 free credits at signup. Rating: 7.9/10.

What is Daytona?

We found Daytona on Product Hunt where it won Product of the Day for solving a problem that gets bigger every day: where do you safely run code that an AI wrote?

As AI agents write more code, someone needs to run that code without risking the host system. Daytona provides isolated, ephemeral sandboxes where untrusted code executes safely. Think of it as the runtime layer underneath AI coding tools. Your AI agent writes Python, JavaScript, or shell commands. Daytona runs them in a container that cannot touch your production systems, then returns the results.

The company raised a $24M Series A in early 2026 backed by Datadog and Figma Ventures. Originally a development environment manager, Daytona pivoted to AI sandbox infrastructure in 2025 as the agentic AI market exploded. The open-source project has over 60,000 GitHub stars.

Key Features

Sandbox creation is extremely fast. Sub-90ms cold starts, with some configurations hitting 27ms. This matters for AI agents that need to spin up, execute code, and return results in real time. A 5-second container startup time makes your AI feel sluggish. A 90ms startup is invisible to the user.

The four-state lifecycle model (Running, Stopped, Archived, Deleted) with configurable automation policies gives you fine-grained control over costs. Set a sandbox to auto-stop after 10 minutes of inactivity, auto-archive after 24 hours, and auto-delete after 7 days. Stopped sandboxes only pay for storage. Archived sandboxes move to cheaper cold storage.

GPU compute support sets Daytona apart from most competitors. Running ML models, image generation, or compute-heavy AI tasks requires GPU access, and most sandbox providers only offer CPU instances. Multiple GPU configurations are available.

Persistent, stateful sandboxes survive stop/start cycles. File system data persists, and you can snapshot and restore sandboxes. This is essential for AI agents running long workflows that span hours or days, where losing state means restarting from scratch.

Pricing

Daytona uses pure usage-based pricing with no subscription tiers. Every user gets the same features.

vCPU costs $0.0000140 per second, roughly $0.05 per hour. Memory costs $0.0000045 per GiB per second, roughly $0.016 per hour. Storage costs $0.00000003 per GiB per second, with the first 5 GB free.

A default sandbox (1 vCPU, 1 GB RAM) runs about $0.067 per hour while active. Stopped sandboxes only incur storage costs. Archived sandboxes are even cheaper.

New accounts get $200 in free credits without a credit card. A startup program offers up to $50,000 in credits for qualifying companies.

The usage-based model is ideal for variable workloads but makes cost prediction harder. If your AI agents spin up thousands of sandboxes during peak usage, costs can spike. Monitor usage carefully during the initial rollout.

Plan	Price	Plan Features	Best For
Usage-Based	~$0.05/vCPU/hour	All features, per-second billing, first 5GB storage free	All users, same features for everyone
Startup Program	Up to $50,000 free credits	For qualifying early-stage companies	AI startups building agent infrastructure

Who is Daytona For?

AI/ML engineers building agentic applications that need safe, isolated code execution. Platform engineers integrating sandbox runtimes into AI products. Startups building AI coding assistants, code generation tools, or autonomous agents. Enterprise teams that need on-premise deployment with policy-based resource management.

Where Daytona Falls Short

Resource limits on default sandboxes are tight. The default cap of 4 vCPU and 8 GB RAM per sandbox means compute-heavy workloads require requesting higher limits. Competitors like E2B offer 8 vCPU per sandbox by default.

The pivot from development environments to AI sandbox infrastructure means the product is still finding its identity. Documentation mixes legacy dev environment concepts with newer sandbox terminology, which can confuse new users.

Docker-based isolation is the default, which is lighter-weight but less secure than Firecracker microVMs used by E2B. Kata Containers are available for hardware-level isolation, but they require explicit configuration. For security-critical workloads, verify that the isolation model meets your threat requirements.

The ecosystem for AI sandbox tooling is moving fast. New providers enter regularly, and pricing races to the bottom. Committing deeply to any single provider carries risk in a market this young.

Alternatives

E2B

Uses Firecracker microVMs for stronger isolation than Docker containers. Similar per-second pricing. Used by roughly half of Fortune 500 companies. Higher per-sandbox resource ceiling (8 vCPU versus Daytona’s 4). No GPU support and no self-hosting option. E2B wins on isolation and resource limits. Daytona wins on GPU support, open-source availability, and self-hosting.

Modal

AI infrastructure focused on Python and ML workloads with gVisor isolation. Scales to 50,000+ concurrent instances. Python-only, which limits it to ML use cases. Better for teams running ML training and inference workloads at scale. Daytona is better for general-purpose code execution across multiple languages.

Fly.io Sprites

Persistent Linux VM sandboxes for AI agents with 1-2 second creation times and 100GB NVMe storage. Full VMs rather than containers, which provides stronger isolation. Slower startup than Daytona but more storage and compute headroom. Better for long-running, stateful workloads. Daytona is better for short-lived, fast-spinning sandboxes.

What We Like

What Could Be Better