API-driven analyst chats
Create conversations with the AI analyst through POST /chats and send questions about connected data sources. The API returns answers in a form your product can present inside its own UI.
Basedash developer platform
Basedash developer platform brings Basedash’s AI analyst, insights, automations, dashboards, and charts to API and MCP server for customer-facing analytics and internal workflows.
Basedash developer platform
Basedash developer platform is the programmable version of Basedash’s AI business intelligence product. The launch post says the company is making the same analyst, insights, automations, dashboards, and charting capabilities available through the API, so teams can use them in their own products and internal systems.
The platform is aimed at two main scenarios: customer-facing analytics inside a product, and internal BI workflows that need to be triggered from other systems. Basedash describes the API as infrastructure for asking questions about data, streaming verified answers, rendering charts, managing dashboards, and connecting those outputs to custom interfaces or automation flows.
Core capabilities
Streaming response flow
The analyst streams back its work as server-sent events, including schema exploration, SQL generation and verification, chart creation, and the final answer. That lets applications show progress instead of waiting for a single response.
Chart and dashboard rendering
Responses include structured chart objects with image endpoints, so charts can be rendered natively or exported into web apps, emails, and PDF reports. The launch post also notes that dashboards and their tabs are available through the API.
Insights and automations control
Insights and automations are part of the API surface, including their runs. Basedash positions this as a way to wire scheduled reporting and data workflows into external systems.
Platform management endpoints
The platform exposes operational objects such as data sources, metric definitions, skills, members, groups, audit logs, and AI usage. This supports building admin tooling around the analytics layer.
MCP server for agents
The MCP server provides the same analyst as a tool for agent workflows, with support for assistants such as Claude Code, Cursor, and ChatGPT. The post describes it as complementary to the API for conversation-based agents.
Practical use cases
Customer-facing product analytics
Use the API to let your product users ask questions about their own data inside your interface, while Basedash handles query generation, verification, and chart output behind the scenes.
Internal reporting workflows
Trigger analyses from events in your own systems, then render charts or post verified answers into internal tools, wikis, or status pages.
Recurring insight delivery
Schedule or deliver AI-generated briefings and recurring reports through automations, instead of relying on manual analyst work every morning.
Agent-assisted data analysis
Connect an agent tool such as Claude Code, Cursor, or ChatGPT to Basedash so it can query data and return verified answers during agent-led workflows.
Analytics operations as code
Manage dashboards, data sources, audit logs, and AI usage programmatically when analytics infrastructure needs to be treated like code.
Pros and Cons
Pros
- Exposes the AI analyst through an API instead of limiting it to the app UI.
- Supports streaming responses, which can make custom interfaces feel more interactive.
- Includes structured chart outputs and image endpoints for reuse across web, email, and PDF workflows.
- Covers more than chat, including dashboards, insights, automations, data sources, and admin objects.
- Offers both a customer-facing embedding path and a fully custom UI approach.
Cons
- The source material is a launch announcement rather than a full technical reference, so some implementation details are still unclear from the page alone.
- The coverage here does not fully confirm every integration, security, or deployment option, so buyers will still need the docs or product pages for those specifics.
FAQ
What can you access through the Basedash developer platform?
The developer platform exposes Basedash capabilities through the API, including chats with the AI analyst, charts, dashboards, insights, automations, data sources, metric definitions, members, groups, audit logs, and AI usage. The blog post also mentions an MCP server for agent-based workflows.
How do you get started with the API?
According to the launch post, the API is available to all Basedash workspaces. Getting started requires signing up or logging in, connecting a data source, creating an API key in Settings → API keys, and making a POST /chats request.
Can Basedash be embedded or fully customized in a product?
The launch post says customer-facing analytics can be built with a fully custom UI while Basedash handles the analyst and data layer behind it. It also says embedding is still available as a faster option through an iframe scoped per customer.
Does Basedash support self-hosting?
Yes. The pricing page says self-hosting is available for teams that need tighter network control or stricter compliance requirements, and it also mentions VPC-based deployments for some enterprise workflows.
How is Basedash priced?
The pricing page says Startup is $1,000/month plus AI usage for up to 25 users, with a 14-day free trial and no credit card required. Enterprise plans are custom and include options such as SSO, SCIM, audit logs, self-hosting, and custom AI models.
Quick Facts
- Category
- AI BI platform / developer platform
- Primary format
- API plus MCP server
- Main use cases
- Customer-facing analytics, internal BI workflows, scheduled insights, automations
- Pricing signal
- Startup plan starts at $1,000/month plus AI usage; 14-day free trial available
- Deployment options
- Self-hosting is available; pricing page also mentions VPC-based environments
- Source domain
- basedash.com
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