About

Kaelio vs Julius for Translating Natural Language into Governed SQL

December 30, 2025

Kaelio vs Julius for Translating Natural Language into Governed SQL

By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku · Dec 30th, 2025

Kaelio excels over Julius for translating natural language into governed SQL by inheriting existing database security controls, semantic definitions, and audit requirements while generating queries that respect row-level and column-level policies. Julius offers accessible chat-based analysis but operates independently of enterprise governance infrastructure, making Kaelio the stronger choice for organizations requiring compliant, auditable SQL that integrates with existing semantic layers.

Key Takeaways

• Governance approach: Kaelio automatically inherits warehouse-level RBAC, row access policies, and semantic definitions while Julius provides SOC 2 compliance without deep security integration

• Architecture difference: Kaelio leverages dbt's Semantic Layer and MetricFlow for grounded metric definitions; Julius uses Python-first processing for file and database analysis

• Accuracy benchmarks: Text-to-SQL systems achieve at most 50% accuracy on enterprise schemas, making governed semantic layers critical for reducing hallucinations

• Scale capabilities: Kaelio supports enterprise deployments with complex schemas and multiple data sources; Julius optimizes for individual analysts and smaller teams

• Security enforcement: Enterprise security requires fine-grained policies across SQL, BI, and APIs with RBAC, row-level security, and data masking—capabilities Kaelio inherits from existing infrastructure

• Pricing models: Julius offers transparent pricing from free to $70/month per user; Kaelio uses enterprise pricing aligned with organization-wide deployments

Kaelio vs Julius is the question every data team asks when they need natural language answers that still respect governed SQL. This post sets the record straight.

Why Compare Kaelio vs Julius for Natural-Language SQL?

Natural language to SQL (NL2SQL) has become foundational for making structured data accessible to non-technical users. As one academic paper puts it, NL2SQL "translates natural language questions into SQL queries, thereby making structured data accessible to non-technical users, serving as the foundation for intelligent data applications."

But accessibility alone is not enough. A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures, according to the UNITE benchmark research. That generalization must happen without sacrificing accuracy or security.

This is where governed SQL enters the picture. Text-to-SQL experiences can be prone to hallucinations when applied directly to enterprise-style schemas that are both opaque and hard to retrofit. Governed SQL means the queries your AI generates respect existing permissions, row-level security policies, semantic definitions, and audit requirements.

When comparing Kaelio vs Julius, the question is not just "can it write SQL?" but "can it write SQL that your compliance officer, CFO, and data team will trust?"

Architectural Approaches: Kaelio's Governed Copilot vs Julius's Notebook-Style Chat

The two platforms take fundamentally different approaches to turning plain English into SQL.

Kaelio uses dbt Cloud's Semantic Layer alongside data warehouse connectors to power a natural language interface. When you ask "What is total revenue by month in 2024?" the underlying MetricFlow technology translates that request to SQL based on semantics defined in your dbt project. This approach grounds every query in your organization's existing metric definitions.

Julius, by contrast, positions itself as a tool where you can connect your data, ask questions in plain English, and get insights in seconds without coding. It is trusted by over 2 million users and integrates with databases and spreadsheets.

The architectural difference matters. Kaelio inherits your governance layer. Julius provides speed and accessibility but operates somewhat independently of your existing semantic infrastructure.

Kaelio: SQL that honors existing semantics

Kaelio generates queries that respect the security controls already in place in your data warehouse. In Snowflake, for example, Row Access Policies are schema-level objects that determine whether a given row in a table or view can be accessed by a specific role. Kaelio inherits these policies automatically.

In BigQuery, row-level security lets you filter data and enables access to specific rows in a table based on qualifying user conditions. This extends the principle of least privilege by enabling fine-grained access control.

Oracle Analytics documentation is explicit about why this matters: "If you implement security only in workbooks, dashboards, or analyses, then the deployed semantic model and database are exposed to SQL injection hacker attacks and other security vulnerabilities."

Kaelio addresses this by generating SQL that respects these database-level controls rather than bypassing them.

Julius: Python-first Chat over Files & Databases

Julius enables conversational data analysis with support for file uploads, SQL databases, and cloud integrations. The platform allows users to ask questions in natural language, perform statistical calculations, and visualize data without needing code or complex software tools.

The platform translates user commands into Python code and applies them to the data. You can perform actions such as filtering, sorting, and aggregating.

Julius is particularly well-suited for smaller teams or individuals. It features a chat-based interface for easy data analysis without technical expertise, with pricing starting at $45/month and a free tier.

The tradeoff is clear: Julius optimizes for speed and ease of use, while Kaelio optimizes for governed, auditable SQL that respects your existing data infrastructure.

Security & Governance: How Do Both Tools Enforce Row-Level Controls?

Governance is not optional for enterprise analytics. The question is how each platform enforces access controls.

Timbr, a semantic layer platform, describes the standard well: enterprise-grade security means enforcing fine-grained policies across SQL, BI, and APIs to ensure compliant, role-based access aligned with identity management. This includes RBAC, row-level security, column-level security, and data masking.

Object-level security (OLS) in Power BI enables model authors to secure specific tables or columns from report viewers. These controls only apply to Viewers in a workspace, not to users with edit permissions.

TextQL's security whitepaper states that the platform provides enterprise-grade data analysis capabilities while maintaining compliance with SOC2 and HIPAA frameworks. This multi-layered security architecture integrates with existing enterprise authentication infrastructure.

The stakes are real. IBM research found that 97% of organizations that reported an AI-related security incident lacked proper AI access controls. Additionally, breaches involving third parties doubled compared to the previous year.

Kaelio is built for environments where security is non-negotiable. It inherits permissions from your existing warehouse RBAC, generates queries that respect row-level and column-level policies, and maintains audit trails. Julius offers SOC 2 Type II compliance but does not integrate as deeply with existing warehouse-level security controls.

Key takeaway: If your organization operates under HIPAA, SOC 2, or similar regulatory requirements, the depth of governance integration should be a primary selection criterion.

How Accurate Are They on Public Text-to-SQL Benchmarks?

Accuracy in text-to-SQL is harder than it looks. The UNITE benchmark contains natural language questions from more than 12 domains, SQL queries from more than 3.9K patterns, and 29K databases. Compared to the widely used Spider benchmark, UNITE introduces approximately 120K additional examples and a threefold increase in SQL patterns.

The NLQB benchmark on GitHub provides an open-source evaluation framework that enables objective comparison of text-to-SQL solutions. It uses full table equality of query results to ensure robustness.

The Falcon benchmark, designed for enterprise-grade evaluation, found that all current state-of-the-art large language models (including Deepseek) achieve accuracies of at most 50%. Major errors originate from schema linking in large enterprise landscapes and mapping colloquial language into exact operators and predicates.

These benchmarks reveal why governed approaches matter for accuracy, not just compliance.

Why Does Governance Reduce Hallucinations?

Semantic layers serve as the bridge between raw data and meaningful insights, helping ensure that both AI and BI systems interpret information consistently and accurately. When the LLM knows the approved definitions for "revenue" or "active user," it does not have to guess.

Galileo's guardrail metrics include Context Adherence, which measures whether a model's response was grounded on the context provided. This metric is intended for RAG or context-based use cases and is a good measure for hallucinations.

Google Cloud's research on agent evaluation introduces the concept of "silent failure," where an agent can produce a correct output through an incorrect process. As they note, "A core challenge is that an agent can produce a correct output through an inefficient or incorrect process, what we call a silent failure."

Kaelio's integration with semantic layers and governance tools means it constrains the SQL the model can emit. This reduces both hallucinations and silent failures by grounding queries in approved definitions and relationships.

Can Both Platforms Scale to Enterprise-Size Schemas?

Enterprise analytics often involves massive schemas. DuckDB documentation notes that 1% of respondents used database files of 2 TB or more, corresponding to roughly 10 TB of CSV files.

The best semantic layer tools in 2025 address scale directly. dbt Labs rewrote the semantic layer playbook in April 2025 by merging MetricFlow into dbt Cloud. This enables governed metric definitions to work across enterprise-scale deployments.

Julius is designed with accessibility in mind. It supports high-volume querying without lag and connects to databases and spreadsheets. However, its architecture is optimized for individual analysts and smaller teams rather than enterprise-wide deployments with strict SLAs.

Kaelio is built for organizations with complex data stacks. It connects to warehouses like Snowflake, BigQuery, and Databricks, works with transformation tools like dbt, and integrates with semantic layers and governance tools. This architecture supports the scale and complexity that enterprise environments require.

Key takeaway: For organizations with hundreds of tables, multiple data sources, and strict latency requirements, integration depth matters as much as raw query speed.

How Should Data Teams Decide Between Kaelio and Julius?

McKinsey's 2025 State of AI report found that 80% of respondents set efficiency as an objective of their AI initiatives, but companies seeing the most value often set growth or innovation as additional objectives. The choice between Kaelio and Julius should align with your strategic objectives.

AI governance tools are designed to ensure responsible and ethical use of AI within organizations. The primary goal is to help organizations achieve transparency, accountability, and fairness in their AI systems.

When evaluating options, consider these criteria:

Integration capabilities: Does the solution work with your existing AI infrastructure and tools?
Governance requirements: Do you need SOC 2, HIPAA, or other compliance certifications?
Team size and technical expertise: Is your team primarily analysts or includes dedicated data engineers?
Schema complexity: How many tables, databases, and semantic definitions do you need to support?
Budget constraints: Julius starts at $45/month; enterprise solutions have different pricing models

Julius offers pricing from free to $70 per member per month, with annual billing saving 15% and educational users receiving 50% off. This makes it accessible for smaller teams and individual users.

Kaelio is designed for organizations where governance, auditability, and integration with existing data infrastructure are primary requirements.

Key Takeaways

Row Access Policies in Snowflake are applied to tables or views to enforce security policies at the row level. This is the standard that enterprise analytics must meet.

Customers want AI-powered conversational analytics but worry about ungoverned data access and inconsistent or inaccurate answers. Both concerns are valid, and the choice between Kaelio and Julius depends on how you prioritize them.

Julius excels at:

Rapid onboarding for individual analysts
Chat-based exploration of uploaded files
Statistical analysis and visualization without coding
Accessibility for non-technical users

Kaelio excels at:

Integration with existing semantic layers and governance tools
Row-level and column-level security inheritance from your data warehouse
Auditability and compliance for regulated industries
Enterprise-scale deployments with complex schemas
Continuous improvement of metric definitions through usage feedback

For data teams at high-growth and enterprise companies who need governed, auditable SQL that respects existing infrastructure, Kaelio is the stronger choice. If you are looking for a natural language interface that integrates with your existing data stack while maintaining the governance your CFO and compliance team require, Kaelio is worth evaluating.

About the Author

Former AI CTO with 15+ years of experience in data engineering and analytics.

Frequently Asked Questions

What is the main difference between Kaelio and Julius for NL2SQL?

Kaelio focuses on generating governed SQL that respects existing data governance and security policies, while Julius offers a more accessible, chat-based interface for quick data analysis without deep integration into existing semantic layers.

How does Kaelio ensure SQL queries are secure and compliant?

Kaelio generates SQL queries that inherit existing security controls from data warehouses, such as row-level and column-level security, ensuring compliance with enterprise governance standards like SOC 2 and HIPAA.

Can Julius handle enterprise-scale data schemas?

Julius is optimized for smaller teams and individual analysts, offering ease of use and accessibility, but it may not scale as effectively as Kaelio for complex, enterprise-wide deployments with strict SLAs.

Why is governance important in text-to-SQL systems?

Governance ensures that SQL queries respect existing data definitions and security policies, reducing the risk of inaccurate or unauthorized data access, which is crucial for compliance in regulated industries.

How does Kaelio integrate with existing data infrastructure?

Kaelio connects to existing data stacks, including data warehouses, transformation tools, and semantic layers, to provide governed, auditable SQL that aligns with an organization's data governance framework.