SIXTA provides an autonomous Database Reliability Engineer that continuously monitors PostgreSQL and MySQL instances, detects performance regressions and plan changes, and automatically generates structured incident reports with root‑cause analysis and remediation steps. The reports are delivered to existing incident‑management tools such as Slack, Teams, ServiceNow, PagerDuty, and Jira, helping database operations teams resolve issues faster and reduce operational risk.
Funding
Funding not disclosed
Founders
Product
Problem
Database teams often spend hours manually investigating performance incidents, tracing root causes, and coordinating remediation across multiple tools, which delays resolution and increases operational risk. Traditional monitoring provides metrics but lacks actionable, context‑rich analysis of query behavior and schema changes.
Solution
SIXTA offers an autonomous Database Reliability Engineer (DBRE) that connects directly to PostgreSQL and MySQL instances, both cloud‑managed and self‑hosted. The platform continuously monitors query performance, statistics, and execution plans, and when an anomaly is detected it generates a structured incident report within minutes. The report includes the root‑cause diagnosis, a list of affected queries, and recommended remediation steps, which are delivered to existing incident and change‑management workflows such as Slack, Microsoft Teams, ServiceNow, PagerDuty, Jira, or custom webhooks. By learning from each investigation, SIXTA improves its analysis over time, helping teams resolve issues faster and prevent recurrence.
Target Audience
Primary customers are database operations and reliability teams managing PostgreSQL or MySQL workloads in cloud, on‑prem, or internal DBaaS environments, as well as DevOps engineers who need automated incident analysis integrated into their existing tooling.
Features
- Direct integration with PostgreSQL and MySQL databases, supporting AWS RDS, Aurora, Azure Database, Cloud SQL, on‑prem, and private‑cloud deployments
- Real‑time detection of performance regressions, plan changes, and statistics drift with automated root‑cause analysis
- Structured output containing affected queries, impact assessment, and step‑by‑step remediation recommendations
- Native delivery of incident reports to collaboration and ticketing platforms (Slack, MS Teams, ServiceNow, PagerDuty, Jira, webhooks)
- Compatibility with common observability stacks (Datadog, Prometheus, Percona PMM, AWS CloudWatch, Azure Monitor) for enriched context
- Continuous learning loop that refines detection accuracy and recommendation quality as it processes more incidents