Astrato vs Omni: Warehouse-Native BI vs Collaborative Intelligence Platform

Most BI platforms are built around the same assumption: analytics is something you observe. You look at a dashboard, you draw a conclusion, you act somewhere else. The data flows one way — into a chart, into a report, into a meeting.

That assumption held up fine for a decade. But teams have started to ask a harder question: what if the action is the data? What if the update to the sales forecast, the budget approval, the operational decision — what if those belong inside the same platform where the analysis happens?

That's the fault line running through this comparison. Omni Analytics and Astrato are both modern warehouse-native BI tools built by people who spent years inside the platforms they set out to improve. Both have semantic layers, AI capabilities, and strong data team adoption. The difference is what each platform decided to optimize for — and whether read-only analytics still fits what your team actually needs.

TL;DR

Astrato is the right fit if:

• Your team needs users to write data back to the warehouse — budget inputs, pipeline updates, operational decisions — directly inside the analytics layer
• You're building customer-facing or embedded analytics and need a purpose-built embedding experience that goes beyond iframes
• Business users in Marketing, Finance, or Ops need to build their own dashboards without waiting on a data engineer to configure topics or write SQL
• You want every dashboard query to hit the warehouse live, with no cache layer introducing data staleness
• You need automated branded report delivery — PDFs, Excel, PowerPoint — on a schedule
• Transparent, predictable pricing matters to how you plan your data stack budget

Omni Analytics may still be the right call if:

• Your team runs dbt and wants bidirectional sync between your BI tool and your dbt repo — Omni's integration here is genuinely the best in the market
• Your primary use case is internal analytics for data-engineer-led teams who live in SQL and want workbook branching and shared semantic models
• You're coming from Looker and want the governance strengths of LookML without the rigidity — Omni's workbook model is a credible upgrade
• You want an AI analytics suite (Blobby AI, Dashboard AI, MCP server) that's grounded in a shared semantic layer and shipping fast

Quick comparison: Astrato vs Omni Analytics

What is Astrato?

Astrato is a warehouse-native BI platform built on a single architectural premise: if your data lives in the cloud warehouse, your analytics layer should work directly from that foundation. Every query runs live against Snowflake, BigQuery, Databricks, Redshift, ClickHouse, or PostgreSQL — no extracts, no scheduled refreshes, no copies of data sitting in a separate engine.

The semantic layer inherits governance from the warehouse itself. Security rules defined in Snowflake or Databricks enforce automatically across every dashboard, every user, every query — without needing to recreate them in the BI layer.

Astrato's three core use cases are guided self-service BI, customer-facing embedded analytics, and data apps with native writeback — where users don't just read dashboards but take action directly inside them.

What is Omni Analytics?

Omni Analytics launched in 2022, founded by Colin Zima — former Chief Analytics Officer at Looker — alongside a founding team with deep Looker and data infrastructure experience. The product is the answer to: what would Looker look like if we kept everything that worked and rebuilt the parts that frustrated data teams?

The answer Omni landed on is the workbook model. Analytics engineers get a shared semantic model with governed metrics and SQL-based exploration. Business users get a point-and-click interface layered on top, including spreadsheet-style formula support. The bidirectional dbt integration — where you can prototype metrics in Omni's UI and push changes directly back to your dbt repo via pull request — is the most technically differentiated feature in the product.

By March 2025, Omni had raised $116M in total funding. In October 2025, Omni acquired Explo, an embedded analytics company, accelerating its embedded analytics motion.

Architecture: what happens when you query

Both Astrato and Omni describe themselves as warehouse-native. Both run queries against cloud data warehouses. But the mechanics are different.

Omni uses an intelligent multi-layer cache. Queries go to the warehouse, results are stored, and subsequent queries hit the cache. The default cache retention is six hours. A dashboard opened at 9 AM may show data from 3 AM. For operational teams watching real-time metrics, the difference is architectural.

Astrato queries the warehouse on every interaction. No cache layer, no staleness window to configure. Whatever is in the warehouse at the moment a user loads a dashboard is what they see.

Writeback: the gap that no Explo acquisition changes

Omni is a read-only platform by explicit architectural choice. Capturing user inputs, planning scenarios, or triggering actions requires exporting data or connecting a separate workflow tool.

Astrato's native writeback lets users enter data directly inside dashboards — budget targets, forecast adjustments, pipeline values, operational decisions — and write those inputs back to the warehouse in real time. No export, no separate planning tool, no round-trip through a spreadsheet.

Embedded analytics: built first versus built later

Omni's embedding works via iframes and JWT-signed URLs. CSS customization is available. But there is no native SDK, no web component architecture, no programmatic embedding interface for teams who need deep product-level integration.

Astrato was built for customer-facing analytics from the start. Pixel-perfect white-labeling, multi-tenant isolation, and the embedding architecture are not add-ons — they're the product.

Final verdict

Omni Analytics is a well-built product. The workbook model delivers on its vision. The bidirectional dbt integration is the best in the BI market. For data-engineer-led teams doing internal analytics on Snowflake or Databricks who live in SQL and want governed self-service without LookML rigidity, Omni earns its 4.8 G2 rating.

The structural constraint is read-only. And in 2026, the teams getting the most from their data aren't the ones who observe it better — they're the ones who act on it directly.

Astrato's clearest wins: native writeback, always-live data with no cache layer, embedded analytics built from day one, no-code self-service for non-technical business users, and transparent pricing.

Book a demo and see how Astrato runs analytics directly on your warehouse →