AI visibility report
Daytona ranks #4 in AI Code Sandboxes & Agent Runtimes AI search.
Outside the top three on 14 of the 25 prompts buyers actually ask.
Modal is cited on 11 of those losses.
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Also benchmarked
Daytona appears in another vertical
Track Daytona across these prompts daily.
Start free trial#4 among 10 vendors · still absent from 91.3% of tracked prompt responses
Top-3 citations across 150 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Google AI Mode
Gemini Search
Bing Copilot
Perplexity
GrokNarrower footprint, stronger tone. Daytona ranks #4 on presence but #1 on sentiment. That means the brand is framed well when it appears, but still needs broader prompt-response coverage.
Where Daytona is losing
Prompts where competitors are visible and Daytona is not.
These prompt-level losses are the first prompts to track and repair.
Where Daytona is winning5
Which code sandbox services have good observability built in so I can actually debug what my AI agent is running inside the environment?
Avg # 1.0 · 1 platform
Which code sandbox platforms are considered production-ready for enterprise AI applications where uptime and SLA guarantees actually matter?
Avg # 1.0 · 1 platform
Looking for an ephemeral code execution environment I can provision per user session — which services have a simple SDK or API to get started quickly?
Avg # 1.0 · 1 platform
What are the best code execution sandbox options that support pre-installing custom dependencies from a private package registry before agent runs?
Avg # 1.0 · 1 platform
What sandboxed execution environments have good support for streaming output back to the calling application in real time during an agent's code run?
Avg # 3.0 · 1 platform
Where Daytona is losing5
I need a code execution environment that supports GPU workloads for AI-generated training scripts — which sandboxed platforms handle that use case?
Competitors on 5 platforms
Track this promptI want a sandboxed runtime where my team can define reusable execution templates — which platforms make that workflow easy without deep infra knowledge?
Competitors on 4 platforms
Track this promptWhat do platform engineers typically use to manage ephemeral execution environments for AI agents — and which options have the least operational burden?
Competitors on 3 platforms
Track this promptWhat sandboxed agent runtime platforms are best suited for production workloads executing user-submitted code thousands of times per day?
Competitors on 3 platforms
Track this promptMy AI agent generates and executes code in a tight loop — which sandbox platforms sustain high-frequency execution without degrading over time?
Competitors on 3 platforms
Track this prompt
Track Daytona daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Daytona is an open-source, secure, and elastic infrastructure platform for running AI-generated code, founded in 2023 and headquartered in New York. It provides programmatic, stateful sandboxes—described as composable computers—that AI agents and developers can spin up in under 90 milliseconds via multi-language SDKs (Python, TypeScript, Ruby, Go, Java), a REST API, and CLI. Core capabilities include isolated code execution, parallel environment forking, environment snapshots, Git integration, built-in LSP support, and computer-use desktop automation across Linux, macOS, and Windows. Daytona targets teams building AI agent platforms, coding assistants, evaluation pipelines, and reinforcement learning infrastructure, offering HIPAA, SOC 2, and GDPR compliance with a customer-managed compute option for enterprises.
Daytona is an agent-native cloud infrastructure platform that provides secure, elastic, and stateful sandboxes for executing AI-generated code at scale. It offers sub-90ms environment creation, parallel execution, snapshot and restore, computer-use virtual desktops, and a comprehensive SDK and API layer, enabling AI teams to replace brittle homegrown sandbox solutions with a managed, compliance-ready runtime.
Key Facts
- Founded
- 2023
- HQ
- New York, USA
- Founders
- Ivan Burazin, Vedran Jukić, Goran Draganić
- Employees
- 11-50
- Funding
- ~$31M
- Status
- Private
Target users
Key Capabilities10
- Sub-90ms sandbox creation with per-second billing
- Stateful, persistent sandbox environments with snapshot/restore
- Parallel sandbox execution and execution-path forking
- Multi-language SDK (Python, TypeScript, Ruby, Go, Java) and REST API
- Built-in Language Server Protocol (LSP) support for multi-language code analysis
- Computer Use sandboxes for Linux (Ubuntu), macOS, and Windows desktop automation
- Native Git operations and secure credential handling
- Docker, Dockerfile, and Docker Compose support with Docker-in-Docker
- Shared volumes for cross-sandbox data access with isolation maintained
- HIPAA, SOC 2, and GDPR compliance; customer-managed compute option
Key Use Cases8
- Secure AI-generated code execution (code interpreter)
- Coding agent runtime with stateful, long-running task support
- AI evaluation and benchmarking across parallel reproducible environments
- Reinforcement learning environment management for agent training
- Data analysis and AI-driven data visualization workflows
- Computer use automation (GUI-driven agent workflows on virtual desktops)
- Replacing homegrown sandbox infrastructure at scale
- Multi-tenant SaaS products requiring isolated per-user code execution
Daytona customer outcomes
200 hours/week saved on sandbox infrastructure maintenance; 6 months of engineering time saved
Replaced custom Docker-based sandbox infrastructure with Daytona, achieving sub-100ms sandbox provisioning and eliminating ongoing infrastructure maintenance overhead for its Agentic AI platform.
Up to 6 hours/week saved on custom sandbox infrastructure maintenance
Replaced an internal sandbox provisioning system powering 800+ AI models and apps used by over 1 billion users, completing integration in 3 days and freeing engineers from weekly maintenance cycles.
Recent Trend
How AI describes Daytona3
Daytona: An open-source development environment manager that pivoted aggressively into AI sandboxes.
What do platform engineers typically use to manage ephemeral execution environments for AI agents — and which options have the least operational burden?
Daytona or Ona (Best for Complete Dev Environments) ------------------------------------------------------- If your team needs full-fledged, stateful development runtimes (e.g., matching a full repository, databases, and language servers rather than ju...
I want a sandboxed runtime where my team can define reusable execution templates — which platforms make that workflow easy without deep infra knowledge?
Daytona / OpenSumi: Originally built as development environment managers, these have evolved into strong agent infrastructure.
What are the best isolated runtime options for AI agents that need persistent filesystem state across multiple execution steps in a single session?
Most cited sources8
19Daytona - Secure Infrastructure for Running AI-Generated Code
daytona.io·Blog Post
- 8
Process and Code Execution | Daytona
daytona.io·Blog Post
- 7
Process and Code Execution | Daytona
daytona.io·Blog Post
- 7
Sandboxes | Daytona
daytona.io·Blog Post
- 5
Documentation · Daytona
daytona.io·Documentation
- 4
Log Streaming | Daytona
daytona.io·Blog Post
Alternatives in AI Code Sandboxes & Agent Runtimes6
Daytona positions itself as the agent-native infrastructure layer for AI code execution, differentiating on sub-90ms sandbox spin-up speed, stateful persistent environments, and open-source transparency (AGPL-3.0 core).
- Unlike general-purpose cloud runtimes or dev-environment tools, Daytona is purpose-built for AI agent workflows—emphasizing parallel forking, snapshot/restore, and programmatic control via multi-language SDKs.
- It targets teams that would otherwise build and maintain custom Docker-based sandbox infrastructure, offering managed compliance (HIPAA, SOC 2, GDPR) and customer-managed compute as enterprise differentiators.
Reviews
Praised
- Best-in-class sandbox provisioning speed (sub-100ms)
- Eliminates burden of maintaining homegrown sandbox infrastructure
- Easy SDK integration and fast onboarding
- Highly responsive support team
- Stateful, long-running sandbox support for complex agent workflows
- Open-source transparency and verifiable codebase
- Scalable to tens of thousands of concurrent sandboxes
Criticized
- Hardware capacity constraints reported at time of Series A (February 2026)
- Enterprise pricing requires contacting sales team
- Limited public third-party review data given early commercial stage
- Small team (~20 people) relative to enterprise support demands
No verified scores on G2, Gartner Peer Insights, or similar platforms were found, consistent with Daytona's early commercial stage. Qualitative feedback from named enterprise customers (SambaNova, Prosus, LangChain, Sentry) published on the company's own customer pages highlights exceptional sandbox provisioning speed, ease of SDK integration, responsive support, and the value of eliminating custom sandbox infrastructure maintenance overhead.
Pricing
Usage-based, pay-as-you-go pricing with no credit card required to start. vCPU: $0.0504/hour ($0.0000140/second); Memory: $0.0162/GiB-hour ($0.0000045/GiB-second); Storage: $0.000108/GiB-hour ($0.00000003/GiB-second) with 5 GiB free. New accounts receive $200 in free compute credit. Startups can apply for up to $50,000 in credits via the Startup Program. GPU sandboxes are available (12 GB GDDR6 and multi-core GPU configurations listed) with pricing on request. Enterprise/on-premises plans require contacting sales.
Limitations
- As of the February 2026 Series A, the company self-reported being hardware-constrained and actively using new funding to add capacity.
- The platform is early-stage with a ~20-person team, which may affect breadth of enterprise support.
- No publicly verifiable third-party review scores were found.
- Enterprise pricing requires contacting sales.
- Self-hosted open-source deployment (OSS version) is available but the managed cloud product is the primary go-to-market.
- GPU sandbox availability is listed but specific GPU SKU pricing is not fully published.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Google AI Mode | Bing Copilot | Gemini Search | ChatGPT | Perplexity | Grok |
|---|---|---|---|---|---|---|
Capability1/5 cited (20%) | ||||||
Which agent runtime platforms support spawning concurrent sandbox instances so multiple AI agents can run code in parallel for a multi-agent workflow? | ||||||
I need a code execution environment that supports GPU workloads for AI-generated training scripts — which sandboxed platforms handle that use case? | ||||||
Which sandboxed execution platforms let AI agents run arbitrary shell commands safely without kernel-level escape risks or shared-tenant interference? | ||||||
Looking for a sandboxed code interpreter that can handle long-running jobs — 10 to 30 minutes — without hitting timeout limits. What are my options? | ||||||
What are the best isolated runtime options for AI agents that need persistent filesystem state across multiple execution steps in a single session? | ||||||
Developer Experience3/5 cited (60%) | ||||||
Which code sandbox services have good observability built in so I can actually debug what my AI agent is running inside the environment? | ||||||
What do platform engineers typically use to manage ephemeral execution environments for AI agents — and which options have the least operational burden? | ||||||
Which agent compute platforms have the most active developer communities and solid docs for teams just getting into agentic AI workflows? | ||||||
I want a sandboxed runtime where my team can define reusable execution templates — which platforms make that workflow easy without deep infra knowledge? | ||||||
Which AI sandbox platforms offer the best developer experience for iterating on agent tools locally before deploying to production? | ||||||
Integrations & Ecosystem3/5 cited (60%) | ||||||
What sandboxed execution environments have good support for streaming output back to the calling application in real time during an agent's code run? | ||||||
What are the best code execution sandbox options that support pre-installing custom dependencies from a private package registry before agent runs? | ||||||
Which sandboxed agent runtimes integrate well with popular LLM orchestration frameworks so I don't have to build a custom execution bridge? | ||||||
Which agent compute platforms avoid heavy lock-in and work across major cloud providers so I can keep data residency in my existing infrastructure? | ||||||
I need an AI agent sandbox that allows secure outbound connections to a relational database during execution — which platforms support that? | ||||||
Performance & Reliability1/5 cited (20%) | ||||||
Which code sandbox platforms are considered production-ready for enterprise AI applications where uptime and SLA guarantees actually matter? | ||||||
What sandboxed agent runtime platforms are best suited for production workloads executing user-submitted code thousands of times per day? | ||||||
My AI agent generates and executes code in a tight loop — which sandbox platforms sustain high-frequency execution without degrading over time? | ||||||
Which microVM sandbox services have the lowest cold-start latency for AI agent code execution at scale — sub-500ms range? | ||||||
Which isolated execution environments scale elastically under bursty AI agent traffic without me having to pre-provision capacity? | ||||||
Setup & First Run3/5 cited (60%) | ||||||
I'm evaluating sandboxed agent runtimes for a small team building an AI data analyst tool — what should I look at to avoid the overhead of self-hosting? | ||||||
Looking for an ephemeral code execution environment I can provision per user session — which services have a simple SDK or API to get started quickly? | ||||||
What's the fastest sandbox runtime to spin up for an AI agent backend — which platforms let you get isolated code execution running in under 5 minutes? | ||||||
Which microVM-based sandbox platforms have the smoothest onboarding for a solo developer shipping an AI coding assistant MVP? | ||||||
I'm adding a code interpreter to my LLM app and need a sandboxed runtime — which services are easiest to integrate without managing my own infrastructure? | ||||||
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Northflank | 36.7% | 40.5% | 0.0% | 36.7% | 32.0% | #6.3 | +0.48 |
| 2 | Modal | 30.0% | 31.4% | 2.0% | 2.0% | 28.0% | #6.4 | +0.50 |
| 3 | E2B | 10.7% | 10.1% | 2.7% | 1.3% | 10.0% | #9.1 | +0.46 |
| 4 | Daytona | 8.7% | 12.1% | 4.0% | 2.0% | 8.7% | #7.4 | +0.55 |
| 5 | Cloudflare | 3.3% | 3.6% | 2.7% | 0.0% | 3.3% | #6.4 | +0.16 |
| 6 | CodeSandbox | 2.0% | 1.3% | 0.7% | 0.7% | 1.3% | #5.8 | +0.38 |
| 7 | Fly.io | 0.7% | 0.3% | 0.0% | 0.0% | 0.0% | #2.0 | +0.20 |
| 8 | Runloop | 0.7% | 0.7% | 0.0% | 0.0% | 0.7% | #3.5 | +0.00 |
| 9 | Morph | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 10 | Together AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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