Your lakehouse spends money while you sleep.
Omnitrace watches 24/7, reasons in dollars, and ships the fix — before you open your laptop.
Idle cluster auto-terminated
0428-194212-violet42
Applied 14:23 → Verified 14:27
Warehouse auto-stop enabled
Northwind-ETL · serverless
Applied 14:21 · verifying...
OPTIMIZE proposed
main.analytics.events
Awaiting blast-radius approval
Omnitrace is an agentic observability platform for Databricks that helps enterprises reduce cloud costs, improve reliability, and automate operational remediation.
The FinOps Agent identifies hidden spending and optimization opportunities, while the Reliability Agent detects performance bottlenecks and operational risks across clusters and workloads. With autonomous remediation and human-in-the-loop escalation, Omnitrace helps engineering teams spend less time firefighting and more time building.
Enterprise proof
A closed loop your platform team can audit.
Omnitrace is built for teams that need more than alerts. Every recommendation is tied to evidence, every action is governed, and every fix is verified after it runs.
No customer table contents
No query result sets
No business records
Cloud-bound model option
Scoped credentials
Human approval controls
Step 1
Connect
Read-only operational metadata
Databricks, cloud cost, system-table, configuration, owner, and workflow signals enter the agent loop without customer rows, files, or query results.
Step 2
Reason
Cost and reliability evidence graph
The agent correlates spend, workload behavior, policy drift, failures, and ownership to decide what matters and why.
Step 3
Act
Guarded MCP tool calls
Approved actions move through scoped tools, Jira workflow, autonomy policies, blast-radius checks, and savings thresholds.
Step 4
Verify
Read-back proof
Omnitrace checks the target state after every action and keeps verification evidence with the finding and action record.
Explore by problem
Start where the pain is loudest.
Omnitrace connects FinOps, reliability, and remediation workflows so teams can move from evidence to verified action.
Databricks cost optimization
Find idle clusters, warehouse waste, table hygiene issues, and hidden cloud charges that DBU-only views miss.
Read moreLakehouse health
Detect reliability drift across tables, workloads, warehouses, and operational signals before they become incidents.
Read moreAutonomous remediation
Move from alert to Jira context, approved fix, and verified outcome without losing human guardrails.
Read moreSpark reliability
Explain failed jobs, OOMs, spill-heavy stages, skew, and cluster configuration drift with action-ready evidence.
Read moreArchitecture
From lakehouse telemetry to verified action.
Omnitrace connects Databricks and cloud signals to detectors, Atlas Playbook agents, governed remediation, and verified outcomes.
View architecture01
Lakehouse signals
Databricks, cloud cost, jobs, SQL, Spark, ownership, and workflow metadata.
02
Omnitrace agents
Detectors and Atlas Playbook agents reason over metadata and operational telemetry.
03
Governed action
Policy gates, approvals, scoped tools, and autonomy levels control every change.
04
Verified outcomes
Read-back checks prove what changed and keep evidence with the action record.
The problem
Your lakehouse leaks ~$100K/year.
Dashboards see it. Tickets pile up. Nobody fixes it.
How it works
Not a dashboard. A teammate.
Every minute, on every workspace. Continuously.
Continuously senses your lakehouse
Agent ingests rich telemetry across compute, queries, tables, billing, and infrastructure — building a live model of your environment.
Reasons over evidence
AI weighs signals, quantifies dollar impact, and writes natural-language narration explaining why action is justified.
Acts through MCP-orchestrated tools
Within your autonomy budget, the agent calls the right write-tool — adjusting cluster, warehouse, or table state. Auditable, reversible, sandboxed.
Verifies its own work
Two-tier verification reads back the post-action state. If the change didn't stick, the agent flags REGRESSED and notifies your team.
Why Omnitrace
Outcomes, not dashboards.
| Native tools | Generic obs | Manual | Omnitrace | |
|---|---|---|---|---|
| Lakehouse-aware | ✓ | ✗ | — | ✓ |
| Quantifies in dollars | ✗ | ✗ | ✗ | ✓ |
| Autonomous remediation | ✗ | ✗ | ✗ | ✓ |
| Verifies own work | ✗ | ✗ | ✗ | ✓ |
| Multi-tier cost attribution | ✗ | ✗ | ✗ | ✓ |
| Bring-your-own MCP tools | ✗ | ✗ | ✗ | ✓ |
Capability matrix
Built for FinOps, SRE, and data platform operators.
The product surface connects cost evidence, reliability symptoms, workflow ownership, governed action, and verification in one operating loop.
Cost attribution
DBU, cloud cost, workspace, owner, and workload evidence
Detector coverage
50+ implemented detector types across FinOps, SQL, Spark, storage, and governance
Remediation coverage
19 auto-fix paths with manual, approval-required, and low-risk autonomy modes
Workflow integration
Jira-ready context, owner routing, approval state, and audit trail
Enterprise boundary
Metadata-only design with cloud-provider-hosted model options
Verification
Scheduled polling, targeted read-back checks, and outcome evidence
What the agent fixes
One agent. Fifty-plus ways to find waste.
56 detector implementations across FinOps, SQL, Spark reliability, table hygiene, and storage. 19 auto-fix paths are already wired into the apply-and-verify loop.
Roadmap
Where the agent goes next.
Real-time Spark telemetry. Multi-platform compute. Spark config auto-tuning. Tool marketplace.
See the full roadmapQ3 · Now
Real-time Spark telemetry
Q4 · Next
Multi-platform: EMR, Starburst, Snowflake
2026
Spark config auto-tuning + tool marketplace
Ready to put the agent to work?
Connect operational metadata, prioritize verified savings, and move approved Databricks fixes through the agent loop.