5 Reasons Why AI Needs a Database

Generative and agentic AI systems spend most of their time moving, filtering, joining, and transforming data. Training, retrieval, evaluation, and feedback are all data problems at scale. But it’s the tools we already know, the databases, that are just as relevant here as they are essential. The same joins, filters, sorts, and aggregations that power analytics also power embedding pipelines, retrieval augmented generation (RAG) systems, and agent state management.

The impressive part is that once a workload is mapped to database primitives, it inherits decades of progress in query optimization, distributed execution, and hardware acceleration. That is why Theseus is positioned as foundational for AI systems. This distributed runtime brings the full power of accelerator-native software to generative and agentic systems.

As AI transitions from experimentation to production, the demands on infrastructure are changing fast. Modern AI systems must be as flexible as they are scalable, capable of reasoning over massive, constantly evolving datasets while adapting to new hardware and workloads. Theseus brings the proven reliability of database systems to the dynamic world of AI, uniting algebraic primitives, GPU acceleration, and distributed execution in a single extensible runtime.

In a world where everything is becoming a database, Theseus stands out as the distributed runtime built for next-gen AI. Here are 5 reasons why.

Bespoke Data Systems Will Become Databases

Modern database systems already solve the hardest challenge, scaling data processing: efficiently scanning, filtering, joining, and aggregating massive datasets across distributed hardware. The future of large-scale computation, including AI and LLM training, lies in mapping every workload, even those that do not look like SQL, onto the assembly language of relational algebra.

By doing so, we inherit mature solutions for memory management, query optimization, and parallel execution. In short, everything is becoming a database. Theseus embraces that reality, extending database principles beyond analytics to accelerate and simplify data-intensive AI workloads.

AI Pipelines Are Data Pipelines Rooted in Database Fundamentals

AI success depends on robust, auditable, and scalable data infrastructure. Most of the work behind every LLM, RAG system, or enterprise agent boils down to database fundamentals: scanning, filtering, joining, aggregating, and updating data.

Before a model sees a token, data teams are cleaning, joining, normalizing, and enriching massive datasets, merging text from multiple sources, deduplicating documents, computing token statistics, and generating embeddings. During inference, the process runs in reverse: query embeddings are matched with stored vectors, results are joined with metadata, and aggregated for ranking or retrieval.

These are table operations. Expressing them in relational primitives lets Theseus handle distribution, memory, and hardware diversity automatically through its SQL native distributed runtime.

Data Standards Deliver Portability Across Heterogeneous Hardware

Most AI pipelines today are tightly coupled to specific hardware. A pipeline written for pandas on CPUs must be retooled for GPUs, and GPU-specific implementations rarely fall back to CPUs. Scaling from a laptop to a cluster often requires costly rewrites.

Theseus expresses workloads through tabular primitives built on Apache Arrow. Joins, filters, aggregations, and UDFs become hardware-aware and hardware-agnostic. The underlying logic does not change whether it runs on a CPU, a GPU, or across a distributed cluster. The Theseus runtime automatically determines how and where to execute for optimal performance and cost.

Agentic AI Backed by a Powerful Distributed Runtime

Agentic AI thrives on continuous access to enterprise data and the ability to reason, refine, and act in real time. But a growing challenge is that agents generate far more queries than their human counterparts. An active fleet of agents can unintentionally behave like a Distributed Denial of Service (DDOS) attack on traditional databases or data warehouses, simply by doing their jobs. At the same time, LLMs are not inherently good at understanding or reasoning over tabular input data, which means they rely heavily on external computation through SQL or Python to analyze, summarize, and draw conclusions.

Theseus addresses both problems directly. It provides a unified, GPU-accelerated data fabric beneath the agentic layer, allowing agents to query PostgreSQL, Snowflake, vector databases, S3, Elasticsearch, and ETL pipelines through a single distributed interface optimized for concurrency and throughput. As agents like Claude iterate on their reasoning, they can requery Theseus dynamically, using its adaptive query planner and GPU caching to accelerate repeated access patterns and minimize redundant computation.

Theseus also fuses vector and relational operations, generating and persisting embeddings while supporting hybrid vector and SQL queries within the same runtime. In enterprise environments, multiple agents focused on security, compliance, or reliability can operate concurrently on the same Theseus cluster, sharing GPU memory and columnar caches for low-latency collaboration and context reuse. The output of each reasoning cycle is materialized directly to Arrow, ensuring fast context construction and interoperability across the entire agent ecosystem.

Unify Inference Strong Database Fundamentals

Theseus delivers a unified SQL and inference flow, combining data retrieval, prompt construction, and LLM inference inside a single distributed runtime. Python UDFs can call Together or Fireworks through their OpenAI-compatible APIs directly from within Theseus, removing the need for external orchestration. The GPU-accelerated engine executes embedding generation, vector search, and text generation as a single pipeline, minimizing latency and maximizing throughput.

In a typical RAG workflow, Theseus performs every stage:

• Generate embeddings: Extract and transform data, create embeddings, and insert them into a vector index within a single Theseus pipeline.
• Search embeddings: Compute new embeddings for queries and retrieve top matches from the index.
• Inference: Feed the most relevant embeddings and associated data into the LLM for response generation.

Theseus also supports bulk index generation for vector databases. Teams can produce embeddings and indexes for large datasets and ingest them downstream, then quickly regenerate those indexes whenever foundational models change, all within the same distributed system.

Because Together and Fireworks share the same schema, integration is frictionless, requiring only an endpoint or API key update. Operationally, Theseus centralizes monitoring and query control, allowing teams to track, pause, or cancel inference tasks while collecting token usage and latency metrics without the need for external coordination tools.

The Future of AI Infrastructure

As AI moves from experimentation to production, systems must be both flexible and scalable. Theseus brings the proven reliability of database systems to the dynamic world of AI, combining algebraic primitives, GPU acceleration, and distributed execution into a single, extensible platform. Theseus is the distributed runtime built for next-gen AI.