Daytona
Secure infrastructure for running AI-generated code in sub-90ms sandboxes.
If you build agent tools or evals that need millisecond sandbox creation, Daytona is a top pick—SDK coverage and snapshot features are mature, and the recent releases (websocket events, TTL, pre-signed URLs) address real developer pain points. But the closed-source runtime shift in June 2026 is a trade-off. Weigh transparency needs against E2B. Start free with $200 credit; scale into usage-based pricing.
Verified 4d ago · liveness 79/100 · cite: rightaichoice.com/tools/daytona
- AI agent builders
- Platform teams running AI evaluations
- Coding agents with persistent sessions
- Developers building code interpreters
- Teams requiring fully open-source infrastructure
- Non-technical users needing no-code sandbox
- Projects with minimal AI-generated code
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Skip Daytona if you require fully open-source infrastructure (runtime closed since June 2026), need a no-code sandbox, or have minimal AI-generated code execution needs where usage costs outweigh benefits.
GPU instances are billed per hour or per second, and long-running workloads like H200 at $4.54/h can quickly rack up significant costs.
Daytona's usage-based pricing fits teams that need fast, isolated execution at variable scale; $200 free compute covers early experimentation. Compared to E2B (which has similar usage-based pricing), Daytona's sub-90ms creation is a differentiator. For heavier workloads, Modal offers serverless pricing that can be cheaper per invocation, but lacks the sandbox isolation features.
In short
Daytona — Secure infrastructure for running AI-generated code in sub-90ms sandboxes. Best for AI agent builders, Platform teams running AI evaluations, Coding agents with persistent sessions. Free to start; paid plans from $50/mo.
What's new in Daytona
Checked 4 days agoAcross the latest 5 updates: 5 changelog entries.
Warm pool management and spot GPUs
Added warm pool management APIs across SDKs, spot GPU support, and sandbox OpenTelemetry endpoint override.
Snapshot operations by name and outbound proxy
Added snapshot management by ID or name, outbound proxy config on sandbox create, and typed git transport errors.
Stable sandbox fork and snapshot creation
Graduated sandbox forking and snapshot creation to stable with cleaner client-side error signaling in TS SDK.
Pre-signed file URLs and typed SDK errors
Added pre-signed upload/download URLs, typed error codes, and lifecycle-aware sandbox listing.
Sandbox TTL support and Python 3.10 floor
Added time-to-live control over sandboxes; minimum Python version raised to 3.10 (breaking).
What people actually say about Daytona — 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.
102 mentions across 6 sources (Hacker News, Product Hunt, App Store, Bluesky, GitHub, Lemmy) · researched Jul 6, 2026.
- +Sub-90ms sandbox spin-up is fastest in class for AI code execution.
- +Stateful snapshots preserve agent sessions across runs, enabling persistent workflows.
- +Wide SDK support: Python, TypeScript, Ruby, Go, Java for programmatic control.
- +Massive parallelization handles concurrent AI agent workloads at scale.
- +Full isolation per sandbox (kernel, filesystem, network) meets security requirements.
- −Closed-source shift erodes trust and blocks community contributions.
- −Public repository abandoned – no further updates, fixes, or releases.
- −441 open issues on GitHub suggest unresolved bugs and feature requests.
- −Self-hosting impossible without maintaining an outdated fork.
- −Less flexible for Kubernetes-native teams compared to alternatives like Cordium.
- • GPU compute may incur additional charges beyond base tier
- • Parallel sandbox usage can escalate quickly for heavy AI workloads
- • Startup credits ($50k) require eligibility and application
Viability Score
How well maintained and how widely used is Daytona? 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
- Sub-90ms sandbox creation
- Isolated environments with dedicated kernel, filesystem, network
- Process execution with real-time output streaming
- Filesystem CRUD with granular permission controls
- Native Git integration with secure credential handling
- Built-in LSP support for multi-language analysis
- Stateful snapshots and sandbox forking (stable in 0.202.0)
- Sandbox TTL control and auto-pause intervals
- Pre-signed upload/download URLs (0.201.0)
- Websocket-based lifecycle event subscriptions (0.198.0)
- Outbound proxy configuration (0.204.0)
- GPU instances: Nvidia H200, H100, RTX PRO 6000, RTX 5090, RTX 4090
- SDKs for Python, TypeScript, Ruby, Go, Java
- REST API and CLI with PKCE authentication
- Web terminal, SSH, VNC, VPN access
About Daytona
Daytona is a secure and elastic infrastructure platform for running code generated by AI models. It provides composable sandboxes—isolated Linux environments with a dedicated kernel, filesystem, and network—that spin up in under 90 milliseconds. Daytona is built for AI engineers, agent developers, and platform teams who need to execute untrusted code from LLMs without risking their infrastructure. You control sandboxes programmatically through SDKs for Python, TypeScript, Ruby, Go, and Java, as well as a REST API and CLI. The platform supports process execution with real-time output streaming, filesystem CRUD with granular permissions, native Git integration with secure credential handling, and built-in LSP support for multi-language analysis. Stateful snapshots and sandbox forking are now stable, letting you create reproducible environments. TTL controls auto-pause or expire sandboxes to manage costs. Daytona excels in advanced AI use cases: running evaluations across parallel environments, powering coding agents, data analysis, computer use, and reinforcement learning. Recent releases added pre-signed file URLs for uploads/downloads, websocket-based lifecycle event subscriptions, and outbound proxy configuration. GPU instances (Nvidia H200, H100, RTX PRO 6000, RTX 5090, RTX 4090) are available for heavier workloads. Pricing is usage-based: compute at $0.0504/vCPU/h, memory at $0.0162/GiB/h, storage at $0.0000108/GiB/h after the first 5 GiB free, and GPU from $0.99/h. The first $200 in compute is free—no credit card required. Enterprise plans add SSO, audit logs, and BYOC. Compared to E2B or Modal, Daytona stands out for its sub-90ms creation and deep SDK coverage, though the runtime is closed-source as of June 2026.
Behind the Verdict
Daytona positions itself squarely as the infrastructure for AI-generated code execution—a niche that's growing fast with agentic coding. Its core strength is latency and isolation: creating a sandbox in under 90ms is genuinely fast and sets a high bar for competitors like E2B and Modal. The SDK coverage is unusually deep—Python, TypeScript, Ruby, Go, Java—which is a boon for teams building multi-language tooling. The recent maturity of snapshots and forking (now stable) is significant; it enables persistent agent sessions, which is key for complex workflows. The feature velocity in 2026 is impressive: sandbox TTL, auto-pause, websocket events, pre-signed URLs, outbound proxy, spot GPUs, warm pools—these are exactly the features a production platform needs. The shift to PKCE auth and TLS enforcement shows a security-conscious roadmap. However, a major caveat: the runtime is closed-source since June 2026. This may matter to teams that require full transparency or need to self-host without enterprise agreements. Where it fits: AI startups, agent builders, platform teams running evals or coding agents. It's less ideal for non-technical users or teams needing fully open-source infrastructure. The usage-based pricing is predictable but can escalate with heavy GPU usage, especially for long-running workloads. The free tier ($200 credit) is generous for experimentation. Alternatives: E2B offers a similar sandboxed execution platform with a focus on security; Modal is more general-purpose serverless for AI workloads; Fly.io provides infrastructure for running containers globally. For teams prioritizing open-source, E2B or self-hosted solutions like Docker-based sandboxes might be more appealing.
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Real-world workflow fit
Concrete scenarios for the personas Daytona actually fits — and what changes day-one when you adopt it.
You need to run Python code generated by an LLM safely. You install the Python SDK, create a sandbox with a single API call, execute the code, and get real-time output. You set a TTL to auto-expire the sandbox after 10 minutes.
Outcome: Secure code execution in under 90ms, with zero risk to your host, and automated cleanup saves costs.
You're building a coding agent that needs persistent state. You create a sandbox, run code, use git operations to clone a repo, and periodically take a snapshot. You subscribe to websocket events to track sandbox lifecycle, and later fork the snapshot to parallelize experiments.
Outcome: Agents maintain stateful sessions across runs, and snapshots allow reproducible environments for testing.
You need to run computer-use agents that control a virtual desktop. You enable the MCP computer-use tools, access the sandbox via VNC, and configure outbound proxy for network control. You set auto-pause intervals to manage costs.
Outcome: Your agents get full desktop control with network-level security and cost-efficient runtime.
Use Cases
- Evaluate AI-generated code from LLMs in a safe, isolated sandbox before deployment.
- Run autonomous coding agents that need persistent, stateful environments with snapshots.
- Execute large-scale parallel code execution for AI training data generation or testing.
- Integrate secure code execution into your AI agent framework via SDKs or APIs.
- Provide each AI agent with a dedicated, disposable computer for task-specific operations like computer use or data analysis.
- Run reinforcement learning for agents with long-horizon planning in dynamic, stateful environments.
- Process large datasets on clusters with optimized data locality for data analysis workflows.
- Enable your AI agent to run code and instantly render charts and visual outputs.
Models Under the Hood
as of 2026-08-18
Limitations
- Daytona charges usage-based per-hour or per-second rates for compute, memory, storage, and GPU instances (e.g., Nvidia H200 at $4.54/h), with $200 in free compute included and a startup credit program.
- Sandbox storage is free up to 5 GB, then billed per GiB.
- GPU compute can become expensive for long-running workloads, and API rate limits or per-region resource limits may apply.
- The runtime is closed-source as of June 2026, which may not suit teams needing full transparency.
as of 2026-08-19
Verification history
We have re-verified Daytona 6 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
- — 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 Daytona tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Developers evaluating Daytona or running light experiments; includes $200 in free compute and access to all SDKs.
What this tier adds
Starting tier with $200 free credit; no credit card required.
Pay-as-you-go
Usage-based
Ideal for
Teams scaling with variable workloads; per-second billing for compute and GPU.
What this tier adds
Unlimited usage with usage-based billing; includes all features, but no SSO or audit logs.
Startup Program
Up to $50k credits
Ideal for
Qualifying startups needing up to $50k in credits to scale early AI agent or eval infrastructure.
What this tier adds
Free credits and volume discounts, reducing effective cost.
Enterprise
Custom
Ideal for
Large organizations needing SSO, audit logs, larger limits, and BYOC for compliance.
What this tier adds
Adds security features, custom resource limits, and bring-your-own-cloud.
Where the pricing makes sense
The company stage and team size where Daytona's pricing actually pencils out — and where peers do it cheaper.
Daytona's usage-based pricing fits teams that need fast, isolated execution at variable scale; $200 free compute covers early experimentation. Compared to E2B (which has similar usage-based pricing), Daytona's sub-90ms creation is a differentiator. For heavier workloads, Modal offers serverless pricing that can be cheaper per invocation, but lacks the sandbox isolation features.
Setup time & first value
How long it actually takes to get something useful out of Daytona — broken out by persona, not the marketing-page minute.
For developers: sign up, get an API key, install the SDK via pip/npm (1-2 minutes), and create/run your first sandbox in under 5 minutes. For non-technical users: expect longer onboarding; you'll need to understand usage-based pricing and environment concepts, likely 1-2 hours.
Switching to or from Daytona
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From E2B: Replace E2B SDK calls with Daytona SDKs using similar create/exec/delete patterns; migrate snapshots and forking logic.
- →From Modal: Move from serverless functions to sandbox-based execution; adapt code to use Daytona's process and filesystem APIs.
- ↗To E2B: Map sandbox creation and execution, but expect differences in snapshot and event handling.
- ↗To self-hosted Docker: Reimplement sandbox orchestration manually, losing managed isolation and sub-90ms latency.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Daytona
Common stack mates teams adopt alongside Daytona, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Daytona vs Spider Cloud
Choose Spider Cloud if your AI agents need to fetch and structure web data at scale for RAG or training. Choose Daytona if your agents generate or run code and need a secure, fast sandbox. They are complementary: you may need both for a complete agent loop.
Daytona vs Temporal Ai
Choose Temporal AI if you need to orchestrate reliable multi-step AI workflows with automatic retries, human-in-the-loop, and persistence across failures. Choose Daytona if you primarily need fast, isolated code execution sandboxes for AI-generated code, especially with GPU access. The tools complement rather than compete, but for end-to-end agent reliability, Temporal is the backbone; for safe code execution, Daytona excels.
Daytona vs Voyage Ai
If you need high-accuracy retrieval on domain-specific documents (finance, legal, code) with low storage costs, Voyage AI is the clear choice. For safe execution of AI-generated code in isolated sandboxes with sub-second spin-up, Daytona excels — but be aware of its move to closed source. Pick based on your pipeline stage: retrieval vs execution.
Alternatives to Daytona
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