What is Plotly?
Plotly is a data visualization platform built around an open-source graphing library and a set of tools for turning raw data into interactive charts, dashboards, and full analytics apps. It covers three main capability groups: charting libraries for Python and JavaScript, a framework called Dash for building analytical web apps, and Plotly Studio, an AI-native desktop application that generates charts and apps from natural-language prompts. Beyond data visualization, Plotly functions as a business intelligence tool, a dashboarding platform, and an agentic data analysis environment for teams that need to explore data and ship results without building a front end from scratch.
The core library renders over 70 chart types in the browser, with export to SVG and PNG for reports and presentations. On top of that, Plotly Studio uses an AI agent to connect to a data source, write and run Python, and produce graphs, tables, and Dash apps while showing the code behind each step. That combination of manual charting and AI-assisted data visualization lets both engineers who want full control and analysts who want faster output work from the same underlying graphs.
Plotly Video
Features & Benefits
- Interactive Charting Library: render over 70 chart types in Python and JavaScript, from time series with dual y-axes to statistical and geospatial plots, with browser-based interactivity and publication-quality export to SVG or PNG.
- AI Agent for Data Analysis: connect a data source and describe what you need, then let Plotly Studio’s agent explore the data, write Python, run it, and iterate the way an analyst would, producing data visualization output backed by verifiable code at each step.
- Natural-Language App Building: generate dashboards and analytics apps from a written request, with Plotly Studio inferring column types, computing metrics like rolling drawdown or return distributions, and building the resulting charts automatically.
- Ambiguity Detection: have the AI agent raise questions when the data or request is unclear instead of guessing, so results stay traceable to a specific interpretation.
- Code-Backed Reproducibility: keep the exact Python behind every chart, table, and app the agent generates, so any session can be re-run and verified rather than treated as a black box.
- Dash App Framework: build interactive analytical web apps and dashboards in Python, using Dash to put data visualization on a web page without a separate front-end build.
- Coding-Agent Compatibility: build and modify Dash apps alongside coding agents such as Claude or Codex, which work directly with Plotly’s open-source libraries.
- One-Click Publishing: push apps built in Plotly Studio or Dash straight to Plotly Cloud or Dash Enterprise, or export them as a local Python file.
- Built-In Authentication: manage access to published apps with login screens and authentication included in Plotly Cloud, so sharing with a team doesn’t require separate infrastructure.
- Broad Data Connections: connect to data warehouses, databases, and file formats, including Snowflake, Databricks, BigQuery, Postgres, MySQL, SQL Server, MotherDuck, Oracle, and CSV files, plus anything else reachable from Python such as pandas, Polars, SQLAlchemy, or Arrow.
- Local Compute: run all code locally on your own machine in Plotly Studio, with only prompts, metadata, and an output preview passing through the LLM.
- Credential Security: store credentials in the operating system’s keychain and redact secrets from LLM transcripts, keeping data connections separate from what the AI model sees.
- Enterprise Deployment: run Dash Enterprise self-hosted and integrated into your own infrastructure, supporting SOC 2 and SSO/SAML for organizations that need data to stay behind their firewall.
What can Plotly do?
- create interactive charts from data
- build a dashboard from a CSV file
- generate a data app with AI
- connect Snowflake to a dashboard
- visualize time series data
- build a Python dashboard without coding a UI
- create a chart with dual y-axis
- turn a spreadsheet into a chart
- build an analytics app with natural language
- export a chart as SVG or PNG
- deploy a Dash app to the cloud
- connect BigQuery to a data visualization tool
- generate a rolling drawdown chart
- build a self-hosted analytics dashboard
Real-World Applications
Financial analysts may use Plotly to turn portfolio holdings into live dashboards, tracking allocation across stocks, bonds, and cash alongside rolling drawdown and return histograms. A quant team could prompt Plotly Studio to build this kind of view directly from a CSV or a warehouse connection, without writing the charting code by hand.
Data scientists and engineers who already work in Python can use Plotly’s open-source graphing library and Dash framework for data visualization inside existing pipelines, since the libraries connect to pandas, SQLAlchemy, and similar tools already in use. Teams without dedicated front-end developers can still ship a working dashboard, since Dash handles the web layer.
Healthcare and life sciences groups may apply Plotly to clinical trial visualization, drug discovery workflows, or regulatory reporting, where dashboards need to draw from structured data sources and hold up under audit. Energy and utilities teams can build grid monitoring dashboards and sensor data visualization for operational forecasting in a similar way.
Organizations with strict data residency requirements might deploy Dash Enterprise behind their own firewall, so dashboards and analytics apps run on internal infrastructure rather than a public cloud. This suits banks, national labs, and other regulated environments that need SOC 2 and SSO support alongside their data visualization tools.
Analysts newer to coding can rely on Plotly Studio’s AI agent to go from a raw data file to a published app, since the agent writes and shows its own Python rather than requiring the user to build queries manually. This extends data visualization work to people who understand the data but don’t write code daily.
