AI systems, from LLM pipelines to generative image models, produce staggering volumes of unstructured data. But it's not the raw objects that cause operational headaches, it's the metadata: versions, references, lifecycles, and access policies. Without metadata management tools built into the database layer, object storage at scale quickly becomes unmanageable.
“Metadata infrastructure is the foundation that determines whether enterprises can truly harness the power of AI or get buried under a mountain of unusable information,” says David Joy, Senior Manager, Sales Engineering at Cockroach Labs. “Without the ability to efficiently process, discover, and govern datasets, companies are left with data they can’t find, trust, or put to work.”
“When organizations get metadata management right, however,” Joy continues, “they create a framework where data becomes transparent, reliable, and actionable, which turns AI into a sustainable competitive advantage. If metadata is neglected, the result is wasted storage spend on assets that create drag instead of momentum, which slows innovation and delays impact.”
Here's what data architects should look for in a cloud-native database that’s ideal for scalable AI metadata management.
1. Real-time write performance for high-concurrency metadata operations
“Every file upload triggers a cascade of metadata activity: sessions, part uploads, version increments, and access updates,” states Joy. “In AI environments, these events multiply exponentially as training sets, embeddings, and intermediate artifacts flow into storage. The database needs to handle this concurrency without lag or data loss.”
Look for: a database with high-throughput ingest, low-latency writes, and distributed coordination.
The result: metadata updates that keep pace with your AI data pipeline—no bottlenecks, no dropped transactions.
2. Strong consistency to avoid metadata drift
AI pipelines are only as reliable as the data they reference. When metadata grows stale or inconsistent across replicas, training jobs may run on corrupted inputs or inference may pull outdated assets. Multi-region architectures raise the stakes further, since timing gaps can produce conflicting states.
Look for: ACID guarantees, atomic transactions, and serializable isolation across references and versions.
The payoff: Every metadata query reflects the current truth, preventing pipeline failures and data corruption while enhancing business and operational metrics.
3. Elastic scale to match AI data explosion
“AI object stores don't grow linearly – they explode exponentially!” Joy explains. “Billions of objects and trillions of metadata relationships can accumulate within months. Legacy architectures that rely on manual sharding or rigid schema rewrites collapse under this pressure.”
Look for: cloud-native horizontal scaling, dynamic repartitioning, and scale-out capacity that doesn't require a re-architecture.
Developer impact: Your metadata layer scales automatically as your AI workloads grow with minimal need for manual operations.
4. Advanced query support for metadata exploration
Metadata delivers the most value when it can be queried flexibly. Teams must trace lineage, identify duplicates, or audit lifecycle states across petabyte-scale stores. These queries often span multiple joins and relationships that NoSQL systems struggle to express efficiently.
Look for: full SQL support, advanced query syntax, and PostgreSQL compatibility for developer familiarity.
Business result: Data teams can explore and analyze metadata using familiar tools and queries.
5. Global distribution with regulatory compliance
“AI workloads span the globe – however, identity, access, and data management must respect regional compliance requirements,” says Joy. “Metadata often includes sensitive fields like ownership, access logs, retention policies. These must be stored and queried within specific legal boundaries.”
Look for: a simple native vernacular for geo-partitioning, data domiciling, and locality-aware routing that minimize latency while meeting GDPR, HIPAA, DORA, or data sovereignty obligations.
The benefit: global AI operations that stay compliant without sacrificing performance.
6. Developer efficiency with unified metadata and operational workloads
“Fragmented architectures create friction,” Joy notes. “When metadata lives in one system and operational logic in another, application developers write ‘glue code’ that's brittle and hard to maintain. These teams are also limited in what they can do to manipulate data when it resides in multiple places. AI teams move fast, so they need a unified system that supports structured, semi-structured, and AI-specific schemas in one place.”
Look for: a complete database platform that combines operational data and metadata under one API while simplifying data access for application developers.
Developer impact: less time managing multiple systems, less application layer code specific to these integrations, and more time building AI features.
7. How do you compare database approaches for metadata management?
Different categories of cloud-native databases bring distinct strengths. However, not all are equally suited to the demands of metadata in AI object storage:
The takeaway: Metadata is inherently relational and transactional. Systems optimized for vectors, unstructured data, or analytics may excel elsewhere, but distributed SQL databases like CockroachDB provides the balance of scale, consistency, and queryability that AI metadata management demands.
AI metadata management: The X-factor
“In modern AI pipelines, metadata is everything,” David Joy concludes. “It’s the X-factor that determines whether object storage becomes a messy archive or a usable, compliant, AI-ready resource. Choose a cloud-native distributed SQL database that can handle massive object relationships without silos or scaling bottlenecks.”
“CockroachDB powers the metadata backbone for AI platforms managing petabytes of data,” he continues. “It delivers the consistency, scale, and query flexibility that keeps AI pipelines running smoothly.”
See how hyperscaler CoreWeave scaled their AI metadata plane with CockroachDB.
