An AI application rarely needs one database to do everything. It needs durable product records, conversation and job state, retrieved documents, embeddings, caches and audit evidence—with different consistency and latency requirements.

The useful question is not simply SQL versus NoSQL. It is which system owns each type of state. This guide is the decision layer below the AI data infrastructure hub.

Default to a relational system of record

For most AI products, PostgreSQL is the safest starting point. Users, subscriptions, permissions, conversations, jobs and audit events benefit from transactions, constraints and mature operational tooling.

Use SQL for:

• tenant and user identity;
• billing and entitlements;
• agent runs and job status;
• tool approvals and audit logs;
• conversation metadata;
• document ownership and access policy;
• evaluation results that must remain comparable.

Flexible JSON columns can store provider-specific metadata without abandoning relational guarantees. Compare managed options in Neon versus Supabase and the broader Postgres, SQLite and MySQL guide.

Document databases

A document database can be useful when records vary significantly and are normally retrieved as a whole. Examples include imported source objects, evolving integration payloads or tool-specific execution traces.

Do not choose a document database merely because model output is JSON. JSON syntax does not remove the need for identity, ownership, validation or relationships. If the application frequently joins users, permissions, jobs and documents, relational storage is usually clearer.

See MongoDB versus PostgreSQL for a focused comparison.

Embeddings and vector search

Embeddings are derived search indexes, not the authoritative copy of a document. Store enough metadata to enforce tenant and document permissions before returning retrieved text to a model.

A vector record typically needs:

• embedding and model/version identifier;
• source document and chunk identity;
• tenant or access scope;
• content version or checksum;
• timestamps and deletion state.

Small and medium RAG systems can often use a PostgreSQL vector extension. Dedicated vector infrastructure becomes attractive when scale, filtering, latency or indexing behaviour requires it. Keep the source documents and access policy outside the vector index so it can be rebuilt safely.

Conversation and agent state

Separate durable state from transient working memory:

• durable: user messages, approvals, tool results and final status;
• transient: partial tokens, locks, short-lived plans and presence;
• derived: summaries, embeddings and caches that can be regenerated.

An agent’s current state should have an explicit version. Use idempotency keys or optimistic locking so retries do not execute the same irreversible tool twice.

SQLite can work well for a single-user local agent, but concurrent workers need careful write coordination. Use the SQLite concurrency guide before treating a local database like a shared queue.

Redis and caches

Redis is useful for short-lived coordination:

• response and embedding caches;
• rate-limit counters;
• job leases and distributed locks;
• streaming session state;
• short queues where loss semantics are understood.

Do not make an unpersisted cache the only copy of approvals, billing records or audit evidence. Cache keys must include tenant, model and prompt/configuration versions where those affect the result. See Redis versus Memcached for trade-offs.

Queues are not ordinary tables

Background inference, document ingestion and agent runs need delivery semantics, retries and dead-letter handling. A database-backed queue can be enough at moderate scale if jobs are claimed atomically and leases expire safely.

Use dedicated queue infrastructure when throughput or operational separation warrants it. Regardless of technology, record durable job status in the system of record so users can see what happened after queue messages expire.

Audit logs

AI audit records should answer:

• which user or service initiated the action;
• which model, prompt and tools were used;
• which approval allowed a side effect;
• what data sources were accessed;
• whether a fallback or retry occurred;
• the final outcome and relevant cost.

Store references and redacted metadata rather than copying secrets or sensitive prompt content into every log. Audit storage should be harder to alter than ordinary application state.

A practical starting architecture

For many AI SaaS products:

1. PostgreSQL owns users, jobs, permissions, conversations and audits.
2. Object storage owns original documents and large artefacts.
3. PostgreSQL vector search or a vector service indexes approved chunks.
4. Redis accelerates caches, counters and short-lived coordination.
5. A queue moves slow inference and ingestion outside requests.

Add systems only when a measured workload demands them. Every extra datastore creates another consistency model, security boundary, backup process and failure mode.

The right AI database architecture makes authoritative state obvious, derived indexes rebuildable and every model or agent action attributable.