Data Cost and Observability for Financial Services and Banking

Revefi optimizes financial services data workloads with
continuous visibility and control over cost, data quality, and performance.

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"With Revefi, an organization slashed costs across 700+ Snowflake warehouses by 50% in under 48 hours with just a single click."

Fortune 100 Company

Read Case Studies

50%

Cost reduction

700+

Warehouses covered

<48 hours

Time to results

1 click

Setup effort

Critical Challenges Confronting the Global Banking and Financial Services Industry

Integration Issues

across disparate IT ecosystems.

Complex Data Platforms

that result in repeated data pipeline failures.

Data Lineage Challenges

as a result of data and tool
sprawl.

LLM Model Risks

like hallucinations, and cost overruns.

Siloed Data Systems

across disparate IT ecosystems.

What do financial service providers
need in today's scenario?

Financial services face rising costs across platforms like Snowflake, Databricks, Google BigQuery, and AWS RedShift, where multi-cluster environments drive compute spend faster than business value, limiting cost visibility and accountability.

At the same time, poor data quality impacts:
- Risk models
- Regulatory reporting
- Trading systems

The growing use of AI models such as OpenAI, Gemini, and Claude adds further complexity, with limited insight into token usage, output quality, and data model drift.

Together, these challenges hinder cost optimization, data reliability, and AI performance across modern financial data platforms.

Maximizing Cloud Data ROI

Governing AI and Agentic Deployment

Restoring Confidence in Fragmented Data

Reducing Engineering "Firefighting"

Meeting Rising Regulatory and Audit Standards

Why Revefi For Financial Services?

Four capabilities. One autonomous Platform.

Scalable Query-Level Cost Attribution

Revefi leverages a read-only API to seamlessly connect and instrument every warehouse, cluster, and compute job. By tracking consumption at the granular level, it attributes credit and dollar usage directly to the specific queries, pipelines, users, and teams responsible. Your team can choose to manually review and implement recommendations or delegate tasks to Revefi for autonomous execution. This level of automation is fully configurable to align with your operational needs.

Data Quality & Observability

Revefi automatically deploys data quality monitors across your entire data ecosystem, eliminating the need for manual configuration. The platform tracks critical metrics at both the table and column levels, including freshness of SLAs, schema integrity, detecting unexpected structural changes, and spotting distribution anomalies. When issues arise, Revefi’s root cause analytics (RCA) identifies the specific upstream transformation, job, or source responsible.
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AI Observability

As AI moves into production, Revefi provides the essential operational audit trail required by risk and compliance teams. It instruments API calls to major providers (including OpenAI, Google Gemini, and Anthropic Claude) at the request level. Key captured signals include consumption metrics (such as token usage, latency, and cost per call), and output quality (which is advanced detection for LLM hallucinations and semantic drift).

DataOps & Performance

For autonomous and real-time remediation of performance bottlenecks, Revefi continuously profiles query execution, cluster utilization, and pipeline runtimes to identify inefficient or slow workloads. When enabled, Revefi can autonomously apply optimizations, allowing your engineering team to stop "firefighting" performance issues and shift their focus toward building core features.

Works Seamlessly with Your Existing Stack

Zero-touch, read-only integration. No agents, no pipeline changes.

Cloud Data Platforms

LLMs & AI Agents

Compliance

Up to 60%

in Cloud data costs

10x

in operational efficiency

>5 Mins

time taken to witness results

FAQs

How does Revefi demonstrate ROI from cloud data investments?

Revefi automatically attributes every dollar of data spend to specific teams, pipelines, and business units at the query level replacing vague billing reports with clear workload accountability that FinOps and engineering leaders can act on.

How does Revefi detect data quality issues in financial data pipelines?

Revefi deploys automated monitors tracking freshness SLAs, schema changes, null rates, row count anomalies, and distribution drift. Alerts surface in real time with root cause analysis pointing to the upstream pipeline or transformation responsible.

How does Revefi integrate with existing infrastructure?

Revefi uses metadata only from Snowflake, Databricks, BigQuery, and Redshift. No agent to install, no pipeline changes required. SOC 2 Type II and ISO 27001 certified. The read-only access model writes to your production environment only if needed after your approval.

Is an AI Agent required to use Revefi?

Automated quality monitors deploy across all connected assets in minutes, delivering a single observability layer without any data migration or consolidation.

How quickly does Revefi detect issues in actuarial and claims pipelines?

Real-time monitoring at table and column level surfaces freshness, schema, null rate, and drift issues instantly, with root-cause analysis pointing directly to the responsible upstream job.

How does integration work?

Read-only API connections to your existing platforms. No code changes, no downtime, and full security compliance.