Monitoring vs. guardrails
Datapace vs. pganalyze
pganalyze is a Postgres monitoring and tuning product: it surfaces slow queries, index suggestions, and EXPLAIN analysis so a human can decide what to do. Datapace sits in a different layer. It is the guardrail and audit control plane for AI agents that act on production databases, where every action is policy-checked, can require human approval, and is recorded in a complete audit log.
| Capability | Datapace | pganalyze |
|---|---|---|
| Primary job | Gate and audit what agents do to production | Monitor and recommend query and index improvements |
| Acts on the database | Yes, inside policy and approval | No, advisory only |
| AI-agent guardrails | Policy checks on every agent action | Not in scope |
| Human approval gate | Yes, required for risky actions | Not applicable |
| Audit ledger | A record of every action | Monitoring history, not an action ledger |
| Databases | Database-agnostic (Postgres, MySQL, MongoDB, and more) | PostgreSQL focused |
| Data location | Deployment and data handling designed with each partner | SaaS that collects database statistics |
Choose Datapace when
- You are letting AI agents or coding assistants touch production data and need to contain what they can do.
- You need an approval gate and a complete audit trail of every action taken on production.
- You run more than just Postgres.
pganalyze fits when
- You want deep, human-driven Postgres performance monitoring and tuning advice.
- Your need is observability and recommendations, not controlling autonomous actions.
Sources
See what agents can do on your data, safely
Bring a use case. We will show you how Datapace gates and audits agent actions on your live database.