Podcast Summary: SurrealDB 3.0 and Building Event-Driven AI Applications with Tobie Morgan Hitchcock

Podcast: Software Engineering Daily
Host: Kevin Ball
Guest: Toby Morgan Hitchcock (CEO & Co-Founder, SurrealDB)
Date: September 16, 2025

Episode Overview

This episode explores SurrealDB 3.0, an open-source multimodal database designed for unified, event-driven, AI-powered application development. CEO and co-founder Toby Morgan Hitchcock discusses the motivation, architecture, and powerful new features of SurrealDB—including its flexible data model, bringing AI compute close to the data, building reactive and agentic workflows directly in the database, and considerations around security and developer productivity.

Key Discussion Points & Insights

1. The Vision and Need for Multimodal Databases

• Key Insight: Modern applications require different data models (relational, document, graph, vector, time series). Historically, this led to using multiple databases—creating duplication, complexity, and security problems. SurrealDB aims to unify these modalities in one system with a SQL-like language and flexible model.
• Quote:

  "Serildb is a new multimodal database that is designed to simplify the infrastructure and the development process for developers and organizations where they might typically have been using multiple different databases to achieve what you can do with just a single database in Surrealdb."
  —Toby Morgan Hitchcock [01:56]

• SurrealDB is especially used for AI-native applications, knowledge graphs, and scenarios requiring a blend of time series, vector, document, and graph access (e.g., search + recommendation).

1.1 How SurrealDB Stores and Queries Multimodal Data

• The underlying storage is document-oriented (similar to MongoDB), while the query language (“SurrealQL”) draws from SQL but removes joins in favor of flexible graph edges/links.
• Quote:

  "You can actually use key value access or tabular access, or time series access or document access, but then also augment that with other modalities at the same time."
  —Toby Morgan Hitchcock [03:02]

• Developers define their own indexes, including for text search, vector similarity, traditional uniqueness, etc.
• Materialized views (called “predefined aggregate views”) enable real-time aggregations on writes.

2. Query Language (SurrealQL) and Data Modeling

• SurrealQL is designed to be familiar to SQL/postgres users while supporting graph traversal (arrows rather than joins), enabling both table-like and document-like queries.
• Quote:

  "The differences between ANSI, SQL and SurrealQL are that we don't have joins ... we have graph edges or record links ... you can traverse those out as many times as you want."
  —Toby Morgan Hitchcock [06:03]

• Graph edges are first-class, bidirectional, and highly flexible; even relationships can be linked (e.g., "edge to node" or "edge to edge" connections).
• Quote:

  "We're not really forcing you into doing things in a certain way. And as a result, the applications ... being built on top of Surreal are quite fascinating. We see people building in amazing things ... which we hadn't even imagined."
  —Toby Morgan Hitchcock [07:48]

2.1 GraphQL Support

• SurrealDB exposes GraphQL endpoints for broader adoption but steering developers toward SurrealQL for greater flexibility.

3. Surrealism: Bringing AI Compute and Event-Driven Logic Closer to Data

• Key Concept: Surrealism allows developers to package modular functions—in Rust now, soon in JS/Python—deploy them as compute logic "inside" the database, driven by data events.
• This supports classic AI and new agentic workflows: e.g., on ticket submission, sentiment analysis, LLM-generated replies, and storing results, all in-database.
• Quote:

  "If you can enable organizations and developers ... to understand their data better, then you unlock things that previously were unable to be accessed. So surrealism is the ability to build modular functions ... and ... bring those building blocks into the database to sit right alongside your data and ... run them or call those functions in an event driven way."
  —Toby Morgan Hitchcock [11:03]

3.1 Data Locality & Performance

• Avoids costly and complex data pipelines copying/cleaning/transforming data across multiple siloed systems.

• Quote:

  "Data also has inertia. It's hard to move around, it's expensive to move around ... baking that into the data system, if I'm understanding it right, absolutely."
  —Kevin Ball [13:04]

• Supports deployment flexibility: Compute can run centrally or at the edge, including user devices or edge servers (with security controls).

4. Event-Driven & Reactive Programming Model

• Core to SurrealDB 3.0 is a data- and event-driven (“datagentic”) programming model: workflows are constructed in response to data changes, not just user actions.
• Live queries: “live select” which updates clients/web/database layers in real time on data changes (over websockets/HTTP).
• Quote:

  "Everything is event based, everything is triggered by some event and you need to act on that data."
  —Toby Morgan Hitchcock [42:00]

4.1 Workflow Programming, Debugging, and Ops

• Functions are packaged, versioned, and can be written/tested in the developer’s language and CI environment, then embedded into the database.
• Debugging: All actions and function executions are traced/logged via OpenTelemetry, with support for granular tracing and familiar cloud observability integrations.
• Supports versioning, A/B testing, and rollback for both data (temporal queries) and compute logic.

5. Temporal Querying, Reproducibility, and Compliance

• SurrealDB offers a powerful temporal querying system: reconstruct any database state at any point in time.
• Quote:

  “One of the hardest and most research based thing that we're building ... is the ability to travel back in time of your data ... go back over your entire data set and see what it looked like at any one point in time … now you can give an exact replica of any response that was ever given at any point in time, which becomes incredibly important for organizations who need to have reproducibility and insights into what AI is doing.”
  —Toby Morgan Hitchcock [33:13–36:23]

• Essential for LLM/AI evaluation, compliance, forensic analysis.

6. Permissions, Security, and Fine-grained Access Control

• SurrealDB features a deeply programmable permissions/authentication model:
  • Authorization via OAuth, SAML, OpenID, traditional or custom logic.
  • Field-level, document-level, or table-level permissions written as SQL-like queries—e.g., "let users only see their own records".
  • Schema-less or schema-full flexibility.
• Quote:

  "The authentication data can then be used internally in the database as a variable and that can be applied to these SQL Statements which determine what you can see ... at the data level where the data is stored, who can access what and how can they access it, and what can they see."
  —Toby Morgan Hitchcock [51:48–53:59]

• Enables secure data sharing across multiple departments/teams with fine-grained control.
• Security is emphasized as central, not peripheral:

  "Security is one of those things that everyone expects you to just have to do as a platform, but no one really focuses on the real benefits of it ... having those definitions sit right alongside your data brings another layer ... you're going to be in a better position to move forward as an organization or as an enterprise."
  —Toby Morgan Hitchcock [54:04]

Notable Quotes & Memorable Moments

• The flexibility of data modeling
  "We're not really forcing you into doing things in a certain way. And as a result, the applications that we've seen being built on top of Surreal are quite fascinating."
  —Toby Morgan Hitchcock [07:48]

• On the motivation for Surrealism & data-local compute
  "The benefit of an organization or an enterprise comes from how it handles, how it stores and how it collects its data. And probably not in that order either. How it collects, how it stores, and then how it analyzes its data."
  —Toby Morgan Hitchcock [20:17]

• On security-first database design
  "I think SurrealDB is looking at the security of data in the database in a completely different way to any other provider out there."
  —Toby Morgan Hitchcock [49:24]

• Temporal querying & reproducibility
  "You can give an exact replica of any response that was ever given at any point in time ... it makes that very easy for enterprises and organizations and developers alike."
  —Toby Morgan Hitchcock [33:13–36:23]

• On developer experience & flexibility
  "You can't build things in a way that locks people into a system. ... We're never trying to replace the developer's way of working."
  —Toby Morgan Hitchcock [21:48]

• Data and AI complexity
  "Instead of having to push your data around to the different platforms ... you can actually bring the modular functionality to your data and have that single source of truth that is ever more important than the applications being built today."
  —Toby Morgan Hitchcock [13:31]

Important Timestamps

• 01:56 — Introduction to SurrealDB's multimodality, motivation.
• 03:02 — How SurrealDB fuses document, key/value, graph, and time series access.
• 06:03 — SurrealQL and why there are no joins.
• 08:42 — Graph edges flexibility, example use cases.
• 11:03 — Surrealism: moving functions/compute into the database.
• 16:00 — Separation of storage and compute, scaling strategies.
• 18:15 — Event-driven and data-driven models.
• 20:10 — What is “datagentic” programming?
• 23:23 — Programming model, packaging and running modular functions.
• 25:03 — Visibility and access to data within compute events.
• 27:08 — Permissions model, fine-grained controls.
• 33:13–36:23 — Temporal queries, data reproducibility, and versioned compute.
• 41:34 — Impact on infrastructure complexity and developer productivity.
• 43:46 — Real-time data flows, live queries, push-based model.
• 49:24 — Security, custom authentication, and access controls.
• 51:48 — Defining permissions at document/field/table level.

Conclusion

SurrealDB 3.0 embodies the direction of modern data platforms: unifying multiple data models, pushing compute and AI workflows close to the data, and empowering real-time, secure, event-driven applications—all while reducing infrastructure sprawl and developer friction. Its focus on security, flexible developer experience, and agentic, data-driven programming aims to solve the mounting complexity of AI-powered software, without sacrificing control over data.

Whether you're building knowledge graphs, integrating LLMs, or want ultra-flexible permissions for enterprise data, SurrealDB's vision is to be the one system where data, logic, and AI come together—securely, scalably, and natively event-driven.