Daytona started as a dev environment manager. In February 2025, they pivoted to AI agent infrastructure, rebranding around sandboxed code execution with sub-90ms cold starts using Docker containers. They raised a $24M Series A led by FirstMark Capital shortly after.
The pivot makes sense. AI agents that generate code need somewhere to run it. But Daytona is not the only option, and its architecture has tradeoffs. Docker containers share the host kernel. Firecracker microVMs give each sandbox its own kernel. Some platforms offer persistent state, others optimize for ephemeral execution.
We compared four alternatives across cold start time, isolation model, pricing, GPU support, and SDK maturity.
Cold Start Time (ms)
Lower is better. Measured from API request to sandbox ready.
Daytona reports sub-90ms for Docker containers. Freestyle provisions a full Linux VM with root access in 100ms. Fly.io Sprites trade cold start speed for persistent state.
Quick Comparison: Daytona Alternatives at a Glance
| Platform | Isolation | Cold Start | Persistence | Best For |
|---|---|---|---|---|
| E2B | Firecracker microVM | ~150ms | Ephemeral | Mature SDK, Fortune 500 production |
| Modal | gVisor container | 300-500ms | Ephemeral | GPU workloads, Python-first teams |
| Fly.io Sprites | Persistent VM | 1-2s | Full filesystem | Long-running stateful agents |
| Freestyle | Full Linux VM | 100ms | Hibernate to disk | Root access, nested virtualization |
Why Look Beyond Daytona
Daytona does one thing well: fast sandbox creation with Docker containers. Sub-90ms cold starts are real and useful for high-volume agent workloads. But Docker containers share the host kernel, which means a sandbox escape affects every other sandbox on that host. For teams running untrusted LLM-generated code in production, this matters.
Teams evaluating alternatives should also consider Blaxel, which approaches the problem from the infrastructure layer up. Blaxel runs each agent sandbox in its own microVM with a dedicated kernel, addressing the shared-kernel risk of Docker-based platforms.
Beyond isolation, it bundles persistent storage (Agent Drive, Volumes) and networking primitives (managed agentic proxy, dedicated egress gateways) into the runtime itself. That means agents get storage and network access without stitching together external services. For teams building production agent systems where isolation, compliance, and stateful workflows all matter, Blaxel covers more of the stack in one platform.
Three specific gaps push teams to alternatives:
Shared kernel isolation
Docker containers share the host OS kernel. A container escape compromises all sandboxes on that host. Firecracker microVMs (E2B) and full VMs (Freestyle) isolate at the hardware level.
No native GPU support
Daytona sandboxes are CPU-only. If your agent workflow includes model inference, image generation, or any GPU compute, you need Modal or a separate GPU provider.
No environment branching
Daytona creates sandboxes from scratch each time. Freestyle can clone a running VM without pausing it, letting agents explore multiple solution paths without reinstalling anything.
1. E2B: Most Mature SDK, Strongest Isolation
E2B runs each sandbox in a Firecracker microVM. Every sandbox gets its own Linux kernel, dedicated memory, and no shared state with other sandboxes on the same host. This is the same isolation technology AWS Lambda uses. Backed by $35M in funding, E2B has the largest community and the most battle-tested SDK in the AI sandbox space.
The SDK supports Python, TypeScript, and Java. You can spin up a sandbox, install packages, run code, read stdout/stderr, and tear it down in a few lines. Custom sandbox templates let you pre-bake environments with specific tools and dependencies.
Key capabilities
- Firecracker microVM isolation with dedicated kernel per sandbox
- ~150ms cold boot, custom templates for pre-configured environments
- SDKs in Python, TypeScript, and Java
- Used by roughly half the Fortune 500
Tradeoffs
- Ephemeral only: no persistent state between sessions without external storage
- Pro plan requires $150/month base fee on top of per-second compute
- No GPU support
- No self-hosting option
2. Modal: Best for GPU Workloads
Modal is the only platform on this list with native GPU support in sandboxes. If your agent workflow includes running inference, fine-tuning models, or generating images, Modal is the default choice. Sandboxes run inside gVisor containers, which intercept system calls in user space. Weaker isolation than Firecracker microVMs, but stronger than plain Docker.
The catch: Modal sandboxes run at non-preemptible pricing, roughly 3x the cost of general Modal serverless functions. A 1-vCPU sandbox costs ~$0.14/hour compared to ~$0.05/hour on E2B or Daytona. For CPU-only workloads, this is expensive. For GPU workloads where the alternative is managing your own infrastructure, it is competitive.
Key capabilities
- GPU access (NVIDIA A10G, A100, H100) in sandbox environments
- Scale to 50,000+ concurrent containers
- Python-first API with decorator-based function definitions
- $30/month free compute credits on the Starter plan
Tradeoffs
- 3x pricing premium for sandbox workloads vs. general functions
- Python SDK only
- Cold starts of 300-500ms, slower than Daytona and E2B
- gVisor isolation is weaker than Firecracker microVMs
3. Fly.io Sprites: Best for Persistent State
Sprites launched in January 2026 as Fly.io's purpose-built product for AI agents. The core differentiator: persistent filesystem. Installed packages, created files, database state, and running services all survive between sessions. This is not a mounted volume or external storage. The entire VM persists.
Checkpointing takes ~300ms. You can snapshot a running Sprite, restore it later, and pick up exactly where you left off. Sprites idle automatically when inactive, so you pay for compute only when the agent is active. Storage costs continue, but compute stops.
Key capabilities
- Full filesystem persistence across sessions
- Checkpoint and restore in ~300ms
- Auto-idle when inactive, compute billing stops
- $0.07/CPU-hour, $0.04375/GB-hour memory
Tradeoffs
- Slowest cold start on this list (1-2 seconds)
- CPU-only, no GPU support
- Storage pricing split between hot ($0.50/GB-month) and cold ($0.02/GB-month) tiers
- Newer product, smaller ecosystem than E2B
4. Freestyle: Most Flexible VM Platform
Freestyle provides full Linux VMs with root access and nested virtualization. You can run Docker inside a Freestyle VM, a capability most sandbox platforms restrict. Live forking lets you clone a running VM without pausing it. Hibernate to disk pauses billing entirely and resumes the VM later with all state intact.
Freestyle powers platforms like Bolt and Lovable behind the scenes. The free tier includes up to 10 concurrent VMs and 500 runs per month with 20 free vCPU-hours per day. No credit card required. VMs are ready in 100ms from the API request, faster than E2B's ~150ms cold boot while providing a full Linux VM.
Key capabilities
- Full root access with nested virtualization (Docker-in-VM)
- Live VM forking and hibernate-to-disk
- 100ms cold starts from API request to ready VM
- Free tier: 10 concurrent VMs, 20 free vCPU-hours/day
- $0.04/vCPU-hour, $0.013/GiB-hour memory
Tradeoffs
- CPU-only sandbox environments
- Hobby plan is $50/month for higher concurrency limits
- Smaller community and ecosystem than E2B
Pricing Comparison
Cost per vCPU-hour ($)
Lower is better. Compute cost only, excludes base fees and storage.
E2B Pro adds a $150/month base fee. Modal sandboxes run at non-preemptible rates. Freestyle and Daytona have no base fee.
| Platform | Base Fee | Compute | Free Tier | Billing |
|---|---|---|---|---|
| Daytona | None | ~$0.05/vCPU-hr | $200 credit | Per-second |
| E2B | $0 Hobby / $150 Pro | ~$0.05/vCPU-hr | $100 credit | Per-second |
| Modal | $0 Starter | ~$0.14/vCPU-hr | $30/mo credit | Per-second |
| Fly.io Sprites | $5/mo (Hobby) | $0.07/CPU-hr | Included allowances | Per-second |
| Freestyle | $0 Free / $50 Hobby | $0.04/vCPU-hr | 20 vCPU-hrs/day | Per-second |
Decision Framework
Pick the platform based on what your agent actually does, not on which has the fastest cold start benchmark.
Parallel exploration
Freestyle. Clone a running VM without pausing it, run different approaches simultaneously, keep the winner. Hibernate the losers to disk and pay nothing while they sit.
Standard code execution
E2B. Spin up, run untrusted code, tear down. Strongest isolation (Firecracker), most mature SDK, largest community. The safe default for most AI agent teams.
GPU in the loop
Modal. Only option with native GPU support in sandboxes. Worth the 3x CPU premium if your workflow includes inference, training, or generation.
Persistent project state
Fly.io Sprites. Filesystem survives between sessions. No reinstalling packages or reconfiguring services. Agents that maintain long-running projects across sessions.
Full VM control
Freestyle. Root access, nested virtualization, Docker-in-VM. For teams building platforms where end users deploy real applications inside the sandbox.
Fastest cold start
Daytona. Sub-90ms with Docker containers. If cold start latency is the bottleneck and you accept shared-kernel isolation, Daytona is still the fastest.
Run WarpGrep inside your sandbox
WarpGrep executes in E2B, Modal, Daytona, Docker, or over SSH. Bring your sandbox, add codebase search that finds the right files in under 6 seconds.
Frequently Asked Questions
What is the best Daytona alternative in 2026?
It depends on the workload. E2B is the most mature option for ephemeral AI agent sandboxes with Firecracker microVM isolation. Modal is the pick for GPU-heavy workloads. Fly.io Sprites win on persistent state. Freestyle gives the most flexibility with full root access and nested virtualization.
Why did Daytona pivot from dev environments to AI agent infrastructure?
AI agents writing code need isolated execution environments that launch in milliseconds, branch into parallel paths, and scale to millions of concurrent instances. Traditional cloud was built for stateless production workloads, not agent-driven exploratory computing. Daytona raised a $24M Series A from FirstMark Capital to fund the pivot in early 2025.
What is the difference between Docker and microVM isolation?
Docker containers share the host OS kernel. If one container escapes its sandbox, it can access other containers on the same host. Firecracker microVMs (used by E2B and AWS Lambda) give each sandbox its own dedicated kernel. A microVM escape is equivalent to a VM escape, which is orders of magnitude harder. The tradeoff: Docker boots faster (~90ms vs ~150ms).
Which AI agent sandbox supports GPUs?
Modal is the only platform on this list with native GPU support in sandbox environments. Daytona, E2B, Fly.io Sprites, and Freestyle are CPU-only for sandbox workloads. If your agent needs to run inference or training inside the sandbox, Modal is the option.
Can I self-host any of these platforms?
Daytona is the only open-source, self-hostable option on this list. E2B, Modal, Fly.io Sprites, and Freestyle are managed cloud services. If self-hosting is a hard requirement, Daytona is the option.