Logo
Login
Start Trial
Request Demo

Knowi vs Looker: Which BI Tool Works Without a Data Warehouse?

Last updated:

Choose Knowi if You have NoSQL or API data and need analytics without a warehouse or LookML.
Choose Looker if Your data is already in a SQL warehouse and you want LookML governance.
Start Free Trial Request Demo

AT A GLANCE

Things to Know Before You Decide

Direct NoSQL + API connectivity. Knowi queries MongoDB, Elasticsearch, Cassandra, and REST APIs. Looker requires all data in a SQL warehouse.

No LookML bottleneck. Knowi lets business users query data directly. Looker requires LookML modeling for every source.

Cross-source joins. Join MongoDB + PostgreSQL + REST API data in one query. Looker cannot join across connections.

Private AI stays on your infrastructure. No data sent to external LLMs. Looker's Gemini processes data through Google's cloud.

SIDE BY SIDE

Knowi vs Looker at a Glance

CapabilityKnowiLooker (Google Cloud)
Data Sources 30+ sources: MongoDB, Elasticsearch, REST APIs, SQL. No warehouse needed SQL-only. Requires a data warehouse. No MongoDB or Elasticsearch
Modeling No mandatory modeling. Query raw data directly LookML required for every source. Developer bottleneck
Cross-Source Joins Join any sources in one query. No data movement Cannot join across connections
AI / NLP Private AI on your infrastructure. No external data transmission Gemini AI. Free until Sep 2026, then paid
Embedding White-label, multi-tenant, no cookie dependency iFrame-only. Third-party cookie dependency
Setup Time Days to weeks Weeks to months (warehouse + LookML setup)

HOW CLIENTS SUCCEED WITH KNOWI

Who's Using Knowi

MacroFab

Electronics Manufacturing

Replaced legacy BI tooling with Knowi. Engineering, operations, and finance teams now share a unified analytics layer across production data — no ETL pipelines required.

Ampush

Digital Marketing

Consolidated marketing analytics across channels and clients, replacing fragmented tools with unified dashboards, a single source of truth without data prep.

Frequently Asked Questions

Can Looker connect to MongoDB at all?
No. Looker does not support MongoDB as a data source. Looker generates SQL and requires all data to reside in a SQL-compatible warehouse. To use MongoDB data in Looker, you must build an ETL pipeline to extract data from MongoDB, transform it (including flattening nested structures), and load it into a SQL warehouse like BigQuery or Snowflake. Knowi connects to MongoDB natively using MongoDB's own query language.
Is LookML really mandatory?
Yes. LookML is required for every data source in Looker. You cannot run a query or build a dashboard without a LookML model that defines the tables, joins, dimensions, and measures. This provides strong governance but creates a developer dependency. Business users cannot modify models, and every new data source or schema change requires LookML development. Knowi has no mandatory modeling language.
How does Gemini in Looker compare to Knowi's AI?
Gemini in Looker offers conversational analytics, a LookML coding assistant, code interpretation, and slide generation. It benefits from Looker's semantic layer for accuracy. It is free through September 2026, after which quota-based pricing begins. Knowi's Private AI runs entirely inside your deployment with no external data transmission, works on unmodeled data without a semantic layer prerequisite, and includes Document AI for querying unstructured files. The core trade-off is governance-enhanced cloud AI (Looker) versus privacy-first AI on heterogeneous data (Knowi).
Why is Looker so expensive for small teams?
Looker's negotiated pricing model and minimum annual contracts ($36,000-$48,000/year) create a high floor. For a team of 10 users, that translates to $300-$400 per user per month before warehouse and ETL costs. Looker is priced for enterprise deployments where the per-user cost decreases with scale. Smaller teams often find better value in platforms with predictable, lower-commitment pricing.
Can Looker join data from different databases?
No. All LookML joins must reference tables within the same database connection. You cannot join a BigQuery table with a Snowflake table in Looker. The only workaround is to consolidate all data into a single warehouse first. Looker Studio (the separate, lighter Google BI product) supports data blending across up to five sources, but with significant limitations. Knowi can join data from any combination of its 30+ supported sources in a single query.
Is Knowi a replacement for Looker if we already have a mature LookML model?
Not necessarily. If your organization has invested significantly in LookML models, your data is centralized in a SQL warehouse, and your governance requirements align with Looker's semantic layer approach, that investment has ongoing value. Knowi is most compelling as a replacement or complement when you have NoSQL or API data sources that Looker cannot reach, when you need cross-database joins, when data privacy prevents using cloud AI, or when LookML development has become a bottleneck for business user self-service.

Skip the LookML Bottleneck.
Query Your Data Directly.

Native NoSQL connectivity, cross-source joins without a warehouse, and Private AI that never sends your data to a third party. Find out why teams choose Knowi over Looker.

Book a Demo Start Free Trial