QuantumLayers runs as a hosted service by default. An on-premise deployment moves the same application inside your own network so the data never leaves your environment. Here is what an on-premise install includes, what stays identical to the cloud, the two decisions unique to running it yourself, and a side-by-side table of the tradeoffs against the online version.
QuantumLayers connects to seven external databases and warehouses: MySQL, PostgreSQL, SQL Server, Amazon Redshift, Azure Synapse, Google BigQuery, and ClickHouse. Here is what each connection needs, the two ways to define the data with SQL or a plain-English description, and worked examples for loading one as a dataset.
Dataset Monitors can now send change alerts to a Slack channel and to a signed webhook on your own systems, alongside email. The trigger stays the same: a genuine statistical change. Only the destination is new.
Reading Charts, or Reading Findings: QuantumLayers Against Traditional BI Traditional BI was designed when computing was expensive and analyst time was the cheap, abundant resource, so it put its effort into a governed surface for a person to read. That…
QuantumLayers Against Claude and ChatGPT: Where Each Fits in a Data Workflow Two of these tools can produce a correlation matrix from a CSV. They look interchangeable on a small task and stop looking interchangeable the moment the task repeats,…
Building With the QuantumLayers JDK: Load Order, Sign-In, and Your First Live Chart The last post named the two ways into the API and left the JDK described but not yet used. This one takes that path in code. It…
Introducing the QuantumLayers MCP Server: Bring Your Own Agent to Your Data How QuantumLayers exposes the same tools that power QL-Agent over the Model Context Protocol, so any MCP-compatible client, from Claude Desktop and Claude Code to Cursor or an…
Introducing Monitoring: Let QuantumLayers Watch Your Data and Tell You Only When Something Changes How a Monitor re-runs the same validated statistical pipeline behind AI Insights on a schedule you set, emails you only when a genuinely new pattern appears…
Building Embedded Analytics on QuantumLayers A first look at using QuantumLayers as a backend for charts, statistical analysis, and AI insights inside your own product. This post covers how the API fits together as a single endpoint pattern, how the…
AI Hallucinations in Analytics: How to Make Sure Your AI-Generated Insights Are Actually True Why AI-powered analytics platforms sometimes fabricate patterns, invent statistics, and confidently present fiction as fact, and what you can do to catch it before it reaches…
Data Quality in the Age of AI Agents: Why Garbage In Now Means Bad Decisions at Machine Speed How poor data quality transforms from a reporting nuisance into a strategic liability when AI agents act autonomously on your behalf The…
Agentic Data Analytics and QL-Agent: How Conversational AI Is Automating Analytical Workflows on QuantumLayers How an AI-powered conversational agent transforms the way teams interact with data, automate analysis, and deliver insights, without writing code or navigating complex interfaces The Analytics…
SaaS in the Age of AI Agents: Why Specialized Platforms Still Win, and How They’ll Evolve The case for domain-specific SaaS in an agentic world, and what the next generation of analytical platforms looks like The Narrative: Agents Will Replace…