vs.
Move beyond Daytona.
Daytona gives agents a sandbox. Qovery gives them a sandbox AND a path to production.
The shift
From legacy platform management
to agentic Kubernetes.
Daytona is excellent at spinning up isolated sandboxes for AI agents to execute code. But code that stays in a sandbox doesn't ship. Qovery covers the full lifecycle - from sandbox to staging to production - with the governance enterprises require.
The Daytona approach
The story ends at the sandbox
Daytona has no deployment capability. Your agent writes code in a sandbox, then... nothing. No CI/CD pipeline, no staging environments, no production hosting. You need to build the entire downstream workflow yourself.
Not true BYOC
Daytona's control plane always runs on their infrastructure. You can bring your own runner machines, but the API, scheduling, and proxy layers stay with Daytona. For data sovereignty, this is a gap.
Individual sandboxes only
A real application is not one sandbox. It's an API + frontend + database + cache + background workers. Daytona manages individual isolated sandboxes. It has no concept of multi-service environment topology.
Basic RBAC, no deployment policies
Daytona offers organization-level roles (admin/member). No project-level permissions, no environment-level policies, no deployment approval workflows. Governance stops at "who can create a sandbox."
The Qovery approach
Sandbox to production in one platform
Agents write code in isolated environments, then deploy through staging to production - same platform, same guardrails, same audit trail. No handoff, no gap.
True BYOC - everything in your cloud
Qovery's entire stack runs in your AWS, GCP, or Azure account. Control plane, data plane, workloads - all in your VPC. Full data sovereignty.
Multi-service environments
Deploy complete application topologies: API + frontend + database + cache + workers. Each environment is a full clone of your production architecture.
Enterprise governance at every layer
Project-level RBAC, environment-level policies, deployment approval workflows, cost controls per team. Every agent action is logged and attributed.
Detailed comparison
How they stack up.
A side-by-side look at what each platform delivers - including the AI capabilities that define modern infrastructure.
Qovery
Daytona
Scope
Full lifecycle: dev sandbox -> staging -> production.
Sandbox only. No path to deployment or production.
Deployment
Built-in CI/CD, preview environments, blue/green, canary.
None. Code stays in the sandbox.
BYOC model
Full BYOC. Everything runs in your cloud account.
Partial. Control plane stays with Daytona.
Multi-service environments
Full topology: apps + databases + caches + workers.
Individual isolated sandboxes only.
Managed databases
PostgreSQL, MySQL, Redis, MongoDB - managed or self-hosted.
None. Run databases inside sandboxes.
RBAC & policies
Project, environment, and role-level. Approval workflows.
Organization-level only (admin/member).
Agent governance
Per-agent policies, scoped secrets, deployment gates.
Auto-stop/archive lifecycle. No deployment policies.
Production hosting
Core purpose. Kubernetes-native, autoscaling, managed TLS.
Not supported. Temporary preview URLs only.
IaC
Terraform provider, REST API, CLI, MCP Server.
SDKs (Python, TS) for sandbox creation. Terraform for runners only.
No lock-in
Qovery adapts to your stack,
not the other way around.
While Daytona stops at the sandbox, Qovery takes your agents from code to production.
One platform, no handoffs
No more "write in Daytona, deploy somewhere else." Agents write, test, and deploy in the same platform. The deployment pipeline is built in.
Everything in your cloud
Unlike Daytona's split architecture, Qovery runs entirely in your cloud account. Control plane, sandboxes, production workloads - all behind your firewall.
Governance that scales with agents
As you go from 1 agent to 100, Qovery's RBAC, cost controls, and audit trails scale with you. Every agent is scoped, every action is logged.
“
We set up Remote Development Environments on Qovery so anyone - engineers, but also non-technical team members - can spin up a fully configured stack on demand. For Claude Code, agents work on tasks unattended for hours inside an isolated sandbox, then surface a PR when done.
Jonathan Petitcolas
Staff Engineer @Tint
Frequently asked questions
Qovery vs Daytona
What is the best alternative to Daytona for AI agent sandboxes?
Qovery is the production-grade alternative to Daytona. Where Daytona stops at the sandbox, Qovery covers the full lifecycle: sandbox, staging, and production - with built-in CI/CD, managed databases, and enterprise governance. Your agent writes code and deploys it on the same platform.
Can Daytona deploy to production?
No. Daytona creates isolated sandboxes for AI agents to execute code, but has no deployment capability. There is no CI/CD pipeline, no staging environments, and no production hosting. You need to build the entire downstream workflow yourself. Qovery includes production deployment as a core feature.
How does Qovery compare to Daytona for Claude Code?
Daytona gives Claude Code an isolated sandbox to write and test code. Qovery gives Claude Code a full environment with databases, secrets, networking, AND a deployment pipeline to staging and production. The agent writes code, tests it, and ships it - all on the same platform, fully audited.
Is Daytona truly BYOC (Bring Your Own Cloud)?
Partially. Daytona's control plane always runs on their infrastructure. You can bring your own runner machines, but the API, scheduling, and proxy layers stay with Daytona. Qovery is fully BYOC - control plane, data plane, and all workloads run in your own cloud account.
What is sandbox-to-production governance for AI agents?
Sandbox-to-production governance means your AI agents work in isolated environments with scoped secrets and network isolation, then deploy through a governed pipeline - staging, review, approval gates - to production. Every action is logged and attributed. Qovery provides this full chain; sandbox-only tools like Daytona cover only the first step.
Built for what's next
Sandboxes are necessary. But sandboxes alone are not sufficient.
Qovery is built for 2026.
Your AI agents don't just need a place to execute code. They need environments with secrets, networking, databases, and audit trails. They need a path from experiment to production. Qovery provides the full infrastructure stack - sandbox to shipped.