Daytona vs SurrealDB: Which Is Better in 2026?

Daytona is cloud infrastructure for safely running AI-generated and untrusted code in isolated sandboxes. It gives AI agents and applications a secure runtime to execute code, spinning up isolated environments in milliseconds — the "more than a sandbox" layer purpose-built for agentic workloads rather than generic code isolation.

What is Daytona?

Daytona provisions isolated sandbox environments on demand, where AI-written or otherwise untrusted code can run without endangering your systems. It streams process output in real time, supports full file-system operations, native Git and multiple languages, and — crucially — creates sandboxes in under 90 milliseconds, so agents can parallelize massively without waiting on slow environment setup.

Who it's for

Daytona is built for AI agent developers and companies building agentic workflows, teams running code evaluations or interpreters, and anyone needing secure, isolated execution at scale. Customers like SambaNova, Writer.com and Mastra AI use it for exactly this — running model-generated code safely and fast.

Key features

• Sub-90ms sandbox creation and startup
• Real-time process execution and output streaming
• Full file-system CRUD with permission controls
• Native Git integration with secure credential handling
• Environment snapshots that save, restore and resume
• Indefinitely-running sandboxes and shared volumes for stateful workflows
• Virtual desktops (Linux, macOS, Windows) with programmatic control
• SDKs for Python, TypeScript, Ruby, Go and Java, plus a REST API

Speed built for parallel AI work

Daytona's headline strength is provisioning speed. Sub-90ms sandbox creation means an agent can spin up hundreds or thousands of isolated environments almost instantly — essential when you're evaluating many code candidates or scaling an agentic pipeline. As a SambaNova executive put it, Daytona does sandbox provisioning "incredibly well," and that millisecond-level performance is what makes massive parallelization practical.

Stateful, long-running environments

Unlike throwaway sandboxes, Daytona supports persistence: you can snapshot an environment and restore or resume it later, keep sandboxes running indefinitely for long tasks, and share volumes across isolated sandboxes. That stateful design fits real agent workflows, where an agent needs to pick up where it left off or maintain context across many steps rather than starting fresh every time.

Computer use, not just code

Daytona goes beyond executing snippets. It provides full virtual desktop environments — Linux, macOS and Windows — with programmatic control, SSH access, a web terminal and VS Code Browser integration. That "computer use" capability lets agents operate a real machine, not just a code runner, opening up workflows like automated testing, browsing and multi-app tasks inside a secure boundary.

Secure and cost-transparent

Security is central: isolated, customer-managed compute in your own cloud, secret management that keeps credentials outside sandboxes, and HIPAA, SOC 2 and GDPR compliance. Pricing is pay-as-you-go and granular — per-vCPU, per-GiB memory and storage by the hour, with GPU options and a $200 free credit — so you pay for exactly the compute your agents use, and can scale from experiments to production predictably.

Fits your language and tooling

Daytona meets developers in their existing stack. It supports Python, TypeScript, Ruby, Go and Java via pip and SDKs, works with the Docker ecosystem (images, Dockerfiles and Compose) plus a declarative image builder, and offers regional deployment across US, EU and Asia. Language Server Protocol support even brings real code analysis inside the sandbox — so agents get a proper development environment, not a bare shell, wherever your users are.

Why choose Daytona

For teams building AI agents or running untrusted code, Daytona is a purpose-built, secure runtime that's fast enough to scale. Its millisecond sandboxes, stateful environments, full computer-use desktops, strong isolation and transparent pricing make it a practical foundation for agentic products that need to execute code safely — without building and maintaining that infrastructure yourself.

SurrealDB is a multi-model database that unifies documents, graphs, vectors, relational data and more in a single engine, queried with a SQL-like language called SurrealQL. Positioned as “the unified data layer for AI,” it aims to replace the stack of separate databases and services most modern applications bolt together, and it is trusted by organizations including Samsung, Verizon, Tencent and PolyAI.

What is SurrealDB?

SurrealDB collapses several kinds of database into one. Instead of running a document store, a graph database, a vector database, an auth service and a cache side by side, you model documents, relationships and vector embeddings in the same engine and query them together with SurrealQL. It is ACID-compliant with distributed write nodes, and it can run embedded inside your application or as a managed cloud service.

Who it's for

SurrealDB is built for developers and engineering teams building data-intensive and AI applications — agents, knowledge graphs, semantic search and real-time apps. It suits teams tired of gluing multiple data systems together and keeping them in sync, as well as enterprises that need one platform to reason over connected, multi-shaped data.

Key features

• Multi-model: documents, graphs, vectors and relational data in one engine
• SurrealQL, a familiar SQL-like query language
• ACID compliance with distributed write nodes
• Built-in authentication and row-level permission models
• Full-text and semantic (vector) search combined in a single query
• An Agent Memory layer for persistent AI-agent context
• Real-time capabilities with roughly 30ms vector-search latency
• Deployable embedded or as a cloud service

Built for the AI era

What makes SurrealDB especially timely is how well its multi-model design fits AI workloads. Retrieval-augmented generation and agents need vector search, structured data and relationships together — usually meaning a vector DB plus a document store plus a graph plus glue code. SurrealDB does all of it in one place, and its Agent Memory layer gives AI agents persistent, queryable context, so the database itself becomes the memory layer instead of yet another bolted-on service.

Fewer systems, less complexity

The core payoff is consolidation. By folding what typically requires five separate systems — vector database, graph database, document store, auth service and cache — into one engine, SurrealDB cuts operational complexity, reduces the latency of hopping between services, and removes whole classes of synchronization bugs. Combined built-in auth and permissions mean access control lives with the data rather than in a separate layer.

Open source and enterprise-ready

SurrealDB is available under open-source licensing, with a free tier accessible through SurrealDB Studio, so developers can start locally at no cost. For production use it brings serious credentials: SOC 2 Type 2, GDPR, ISO 27001 and Cyber Essentials Plus compliance, plus managed cloud hosting — the assurances enterprises need to trust it with real workloads.

Flexible deployment and querying

SurrealDB adapts to how a team wants to work. It can be embedded directly inside an application for local-first or edge scenarios, or run as a managed cloud service for scale, and SurrealQL gives developers a familiar, SQL-like way to express queries that would otherwise require juggling several query languages across different databases. Combining full-text and vector search in a single query — rather than stitching results from separate engines — is the kind of thing that turns complex retrieval code into a few readable lines.

Why choose SurrealDB

For a team building modern, data-intensive or AI-driven applications, SurrealDB offers a genuinely different proposition: one flexible, real-time, multi-model database instead of a fragile constellation of specialized services. Its AI-focused features, open-source availability and enterprise compliance make it a compelling foundation for developers who want power and simplicity at the same time.