Best Data Observability Tools in 2025: Complete Comparison Guide

Choosing the right data observability tool can feel overwhelming. The market has exploded with options, from enterprise platforms costing $100K+ annually to open-source solutions requiring significant engineering investment.

This guide compares the leading data observability tools to help you find the right fit. We'll cover features, pricing, ideal use cases, and honest pros and cons for each option.

What to Look for in a Data Observability Tool

Before diving into specific tools, let's establish the key criteria for evaluation:

Core Capabilities

• Anomaly detection: Automatically identifies unusual patterns in data
• Freshness monitoring: Alerts when data goes stale
• Schema monitoring: Catches structural changes to tables
• Data profiling: Analyzes data to understand its characteristics
• Data validation: Custom rules to check data quality
• Data lineage: Maps where data comes from and goes

Practical Considerations

• Setup complexity: How long to get value?
• Database support: Does it work with your stack?
• Pricing model: Per-table, per-user, or flat?
• Team size fit: Built for your scale?
• Alert quality: Signal-to-noise ratio

Data Observability Tools Comparison

1. Monte Carlo

Monte Carlo is the market leader in enterprise data observability. Founded in 2019, they pioneered the "data observability" category and have raised over $200M in funding.

Key Features

• Comprehensive automated monitoring across all five pillars
• Extensive data lineage with column-level tracking
• ML-powered anomaly detection
• Broad integration ecosystem
• Incident management and collaboration tools

Pros

• Most feature-complete solution on the market
• Strong data lineage capabilities
• Proven at enterprise scale
• Excellent support and professional services

Cons

• Enterprise pricing (typically $100K+/year)
• Can be complex to configure properly
• May be overkill for smaller teams
• Long sales cycles

Best For

Large data teams (30+ people) at companies with significant data infrastructure budgets. If you have complex multi-cloud environments and need enterprise-grade support, Monte Carlo is a strong choice.

See our detailed Monte Carlo comparison →

2. Bigeye

Bigeye focuses on automated data quality monitoring with a strong emphasis on ML-driven anomaly detection. They target mid-market to enterprise customers.

Key Features

• Automated metric monitoring with ML
• Custom SLA tracking
• Catalog integration
• Schema change detection
• dbt integration

Pros

• Strong automated monitoring
• Good dbt integration
• Clean, modern UI
• Solid anomaly detection

Cons

• Enterprise pricing
• Less comprehensive lineage than Monte Carlo
• Smaller integration ecosystem

Best For

Mid-size to enterprise data teams looking for automated data quality monitoring with less complexity than Monte Carlo.

See our detailed Bigeye comparison →

3. Metaplane

Metaplane positions itself as data observability for modern data teams, with a focus on usability and quick time-to-value.

Key Features

• Automated anomaly detection
• Schema change monitoring
• Freshness tracking
• Slack-first alerts
• dbt integration

Pros

• Quick setup (often same-day)
• Clean, intuitive interface
• Good Slack integration
• More accessible pricing than enterprise tools

Cons

• Less feature-complete than Monte Carlo/Bigeye
• Lineage capabilities still maturing
• May lack some enterprise features

Best For

Small to mid-size data teams who want automated monitoring without enterprise complexity.

See our detailed Metaplane comparison →

4. Great Expectations

Great Expectations is the leading open-source data validation framework. It provides a Python-based approach to defining and testing data quality "expectations."

Key Features

• Extensive library of pre-built expectations
• Custom expectation support
• Data documentation generation
• Integration with most data platforms
• Active open-source community

Pros

• Free and open-source
• Highly customizable
• Strong community support
• Works with any Python environment
• No vendor lock-in

Cons

• Requires engineering investment to implement
• No built-in anomaly detection (rule-based only)
• No native UI—need to build your own
• Must manage infrastructure yourself
• Steeper learning curve

Best For

Engineering-heavy teams who want full control and have capacity to build and maintain their own data quality infrastructure.

See our detailed Great Expectations comparison →

5. Soda

Soda offers both an open-source option (Soda Core) and a commercial platform (Soda Cloud). It uses a YAML-based approach to defining data checks.

Key Features

• SodaCL domain-specific language for checks
• Anomaly detection (Soda Cloud)
• Schema monitoring
• dbt integration
• Incident management (Soda Cloud)

Pros

• Open-source core available
• Easy-to-learn YAML syntax
• Good dbt integration
• Flexible deployment options

Cons

• Advanced features require Soda Cloud
• Less automated than some competitors
• Pricing can add up with scale

Best For

Teams who want a balance of open-source flexibility and commercial features, especially those already using dbt.

See our detailed Soda comparison →

6. Elementary

Elementary is a dbt-native data observability tool that runs entirely within your dbt project. It's built specifically for teams already using dbt and wants observability without adding another external tool.

Key Features

• Runs as dbt package (no separate infrastructure)
• Automated data quality tests
• Schema change detection
• Lineage visualization
• Slack/email alerting
• Open-source core with cloud option

Pros

• Zero infrastructure to manage
• Native dbt integration (runs in your existing workflow)
• Open-source core is free
• Quick setup for dbt users (under an hour)
• Lineage derived from dbt metadata

Cons

• Requires dbt (not standalone)
• Less suitable for non-dbt data sources
• Monitoring happens at dbt run time (not continuous)
• Cloud version needed for advanced features

Best For

Teams with dbt at the center of their data stack who want observability without managing additional infrastructure. Especially good for teams already comfortable with dbt's testing paradigm.

7. Sparvi

Sparvi is built specifically for small data teams of 3-15 people. It provides enterprise-grade data observability capabilities with pricing and complexity suited for growing companies.

Key Features

• Automated data profiling
• ML-powered anomaly detection
• Schema change monitoring
• Custom SQL validation rules
• Team collaboration (comments, @mentions)
• Slack integration

Pros

• Purpose-built for small teams
• Quick setup (hours, not weeks)
• Affordable pricing
• No dedicated admin required
• Collaborative features out of the box

Cons

• Newer to market
• Currently focused on Snowflake (more coming)
• Less extensive lineage than enterprise tools

Best For

Small data teams (3-15 people) at startups and growth-stage companies who need real data observability without enterprise complexity or pricing.

Tool Comparison Table

How to Choose the Right Tool

Consider Your Team Size

Team size is often the most important factor:

• 1-3 people: Consider open-source (Great Expectations, Soda Core) or affordable tools like Sparvi
• 3-15 people: Look at Sparvi, Metaplane, or Soda Cloud
• 15-30 people: Metaplane, Bigeye, or Soda Cloud
• 30+ people: Monte Carlo, Bigeye

Consider Your Budget

Be realistic about total cost of ownership:

• $0 (self-hosted): Great Expectations, Soda Core—but factor in engineering time
• $299-1,999/month: Sparvi (starts at $299), Soda Cloud
• $5,000-15,000/month: Metaplane, Bigeye, Soda Cloud (larger scale)
• $10,000+/month: Monte Carlo, Bigeye enterprise

Consider Your Tech Stack

Make sure the tool works with your databases:

• Snowflake: All tools support it well
• BigQuery: Most tools, check Sparvi roadmap
• Redshift: Most tools support it
• Databricks: Monte Carlo, Bigeye have best support
• PostgreSQL: All tools support it

Consider Your Engineering Capacity

How much can you build and maintain?

• High capacity: Great Expectations gives you full control
• Medium capacity: Soda offers a middle ground
• Low capacity: Choose a managed solution like Monte Carlo, Bigeye, Metaplane, or Sparvi

Conclusion

There's no single "best" data observability tool—the right choice depends on your team size, budget, tech stack, and engineering capacity.

For enterprise teams with large budgets and complex environments, Monte Carlo remains the most comprehensive option. For teams wanting automated monitoring without the enterprise price tag, Bigeye and Metaplane are strong choices.

For small data teams of 3-15 people, Sparvi provides the right balance of capability and simplicity. And for engineering-heavy teams who want full control, Great Expectations and Soda Core offer powerful open-source foundations.

Whatever you choose, the most important thing is to start monitoring your data. Even basic observability is far better than discovering problems when stakeholders report wrong numbers.

Frequently Asked Questions

What is the best data observability tool for small teams?

For small data teams of 3-15 people, Sparvi and Metaplane are the best options. They offer quick setup (hours not weeks), affordable pricing, and don't require dedicated admin resources. Open-source options like Great Expectations work for engineering-heavy teams willing to invest in setup.

How much do data observability tools cost?

Data observability tool pricing varies widely: open-source tools like Great Expectations and Soda Core are free but require engineering investment. Commercial tools range from $500-2,000/month for small teams (Sparvi, Soda Cloud) to $5,000-15,000/month for mid-market (Metaplane, Bigeye) to $100,000+/year for enterprise (Monte Carlo).

What's the difference between data observability and data quality tools?

Data observability tools provide continuous, automated monitoring of data health including freshness, volume, schema changes, and anomaly detection. Data quality tools typically focus on validation and testing at specific points. Modern platforms like Monte Carlo, Bigeye, and Sparvi combine both capabilities.

Which data observability tool works best with dbt?

Elementary, Soda, and Great Expectations have the tightest dbt integrations, running tests within dbt workflows. Metaplane and Sparvi also integrate well with dbt through their platforms. For pure dbt-native testing, Elementary is purpose-built for the dbt ecosystem.

Can I use open-source data observability tools in production?

Yes, tools like Great Expectations and Soda Core are production-ready and used by many companies. However, they require engineering investment to implement, maintain, and build dashboards/alerting. Teams should factor in 2-4 weeks of setup time and ongoing maintenance costs.

What are the five pillars of data observability?

The five pillars are: Freshness (is data up-to-date?), Volume (did the expected amount arrive?), Schema (did the structure change?), Distribution (are values within expected ranges?), and Lineage (where does data come from and go?). Different tools emphasize different pillars.