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PromptQL Review

PromptQL is a multiplayer AI agent designed to maintain shared context within teams, learning from corrections and disseminating knowledge effectively.

shipped Jul 25, 2026writingfreemium
Domain rating49Monthly visits468/mo
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Why it matters

1PromptQL launched in mid-2025 and repositioned as a 'multiplayer AI agent' in 2026.
2It raised $136 million in July 2026 to enhance its AI-powered virtual analyst platform.
3PromptQL utilizes a 'plan-then-execute' architecture, generating Python and SQL queries outside the LLM for reliability.
4The platform is ISO 27001:2022 and SOC 2 Type II compliant, with HIPAA alignment.

About PromptQL

Business Model
Subscription SaaS
Team Size
51-200
Funding
Bootstrapped
Platforms
Web, iOS, Android, Desktop
Target Audience
Teams in various sectors looking for AI solutions to maintain context and improve efficiency

Pricing Plans

Free Tier
Free
  • • Access to core features
  • • Limited usage
Pro Tier
Contact for pricing / monthly or annual
  • • Full access to all features
  • • Higher usage limits

Leadership

Lili RiahiDemand Generation Manager
Anushrut GuptaApplied AI Lead

Specs

API Docs

View Documentation →

API Available

Yes, public API

overview

What is PromptQL?

PromptQL is an enterprise AI platform and data agent developed by Hasura that enables organizations to perform reliable natural language analysis and automate complex business processes on their data and systems. It functions as a multiplayer AI agent with shared context, providing trusted answers to business questions for internal teams and powering customer-facing products. PromptQL connects to various data sources, including databases, data warehouses, SaaS applications, and APIs, without requiring data movement. Its 'plan-then-execute' architecture employs a large language model (LLM) to write a query plan, which is then executed as real code (Python and SQL) outside the model, aiming for repeatability and higher accuracy by mitigating LLM hallucinations. This approach positions PromptQL as a solution for data and analytics teams dealing with complex, cross-system data.

features

Key Features of PromptQL

PromptQL offers a suite of features designed to facilitate AI-driven data interaction and team collaboration, emphasizing reliability and contextual learning. Its core functionality revolves around enabling natural language querying and automation on enterprise data.

  • Multiplayer AI collaboration with shared threads and a living knowledge base.
  • Integration with various data sources including databases, data warehouses, SaaS applications, and APIs.
  • Contextual learning and improvement from user corrections to disseminate knowledge effectively.
  • User-friendly interface for natural language interaction with business data.
  • Real-time knowledge sharing within teams, reducing friction in workflows.
  • API available for programmatic access and integration into other systems (API Docs URL: https://promptql.hasura.io/docs/api/query-api).
  • Plan-then-execute architecture for generating Python and SQL queries, ensuring deterministic and repeatable results.
  • Compliance with ISO 27001:2022, SOC 2 Type II, and HIPAA alignment.
  • Data processing addendum available at https://hasura.io/legal/dpa/ and privacy policy at https://hasura.io/legal/privacy-policy/.

use cases

Who Should Use PromptQL?

PromptQL is primarily designed for enterprise environments requiring reliable natural language interaction with complex data, targeting a range of technical and business personas.

  • Data and Analytics Teams: For natural language analysis and automation of complex business processes on enterprise data.
  • Technical Teams, Business Teams, Operations Teams, and Executives: To obtain trusted answers to business questions, reducing reliance on specialized experts.
  • Developers Building Customer-Facing Products: For powering products and autonomous agents with data exploration and reasoning capabilities.
  • Organizations in Financial Services, Healthcare, and Retail: To build specialized AI agents grounded in unique data models, logic, and workflows.
  • Teams Seeking Workflow Automation: For automating repetitive workflows and generating dashboards/reports from business data.

how to use

How to Use PromptQL

PromptQL enables users to interact with their business data using natural language queries, facilitating data analysis and automation. Getting started involves connecting data sources and leveraging the AI agent for insights and workflow management.

  • 1Access the PromptQL platform via its web interface or integrated applications.
  • 2Connect PromptQL to relevant data sources such as databases, data warehouses, SaaS applications, and APIs.
  • 3Input natural language queries to analyze data or automate specific business processes.
  • 4Review the AI-generated query plans (Python/SQL) and executed results for accuracy and reliability.
  • 5Utilize shared threads and the AI-native workspace for collaborative data discussions and knowledge dissemination.
  • 6Turn recurring analysis and workflows into reusable artifacts for automation and reporting.

pricing

PromptQL Pricing & Plans

PromptQL operates on a freemium model, offering a free tier for initial access and a professional tier for advanced enterprise needs. Specific pricing for the Pro Tier requires direct contact with PromptQL sales.

  • Free Tier: Free (monthly). Includes core multiplayer AI collaboration features and basic data integrations.
  • Pro Tier: Contact for pricing (monthly or annual). Offers enhanced capabilities for enterprise-grade natural language analysis, automation, and advanced compliance features.

Pros

  • +Utilizes a 'plan-then-execute' architecture for high accuracy and reliability in data analysis, mitigating LLM hallucinations.
  • +Functions as a multiplayer AI agent, fostering shared context and knowledge dissemination within teams.
  • +Offers extensive data source integration, connecting to databases, data warehouses, SaaS applications, and APIs without data movement.
  • +Provides an API for programmatic access, enabling integration into custom applications and workflows.
  • +Compliant with ISO 27001:2022, SOC 2 Type II, and HIPAA, addressing enterprise security and privacy requirements.
  • +Offers a freemium model, allowing initial access to core features without upfront cost.

Cons

  • −Public user reviews and reception are currently limited, making broad sentiment assessment challenging.
  • −Requires a technical rollout and integration for complex enterprise data environments, not a simple plug-and-play solution.
  • −Specific pricing for the Pro Tier is not publicly disclosed, requiring direct contact for details.
  • −The '100% AI data accuracy' claim in some marketing materials is acknowledged by Hasura as an aspirational goal, not a literal guarantee.
  • −May require teams to adapt to a new collaborative workspace paradigm, potentially replacing existing tools like Slack for data-centric conversations.

Policies

Pricing Page

View Pricing→

Similar Tools

PromptQL vs Competitors

PromptQL differentiates itself in the AI agent landscape through its 'plan-then-execute' architecture and focus on a multiplayer AI agent for shared team context, aiming for high reliability and accuracy in enterprise data interactions.

1
PromptLayerOn Stork

It provides a visual workspace for managing prompt versions and deployments, connecting applications to LLM providers and logging all requests.

PromptLayer focuses heavily on prompt versioning, logging, and a visual workspace for non-technical users. While it helps manage prompts and their performance, it is more about managing prompts for AI agents or LLMs rather than a single, continuously learning AI agent for team context, which is a core aspect of PromptQL.

2
PromptHubOn Stork

It offers Git-style version control for prompts, including branching, commits, and merge workflows, and a community library for reusable templates.

PromptHub excels in version control and collaborative prompt development, similar to code repositories. It provides a structured way to manage prompt changes but might require a more explicit workflow for 'learning from corrections' and 'disseminating knowledge' compared to PromptQL's agentic approach.

3
AgentaOn Stork

It is an open-source, self-hostable LLMOps platform that combines a prompt playground, management, evaluation, and observability.

Agenta offers a comprehensive open-source solution for prompt management and evaluation, allowing for deep customization and self-hosting. The trade-off is that it requires more technical setup and maintenance compared to a hosted freemium service like PromptQL, and its 'agent' aspect is more about managing LLM applications than a single, continuously learning AI agent for team context.

4
InstantContext↗

It provides a collaborative workspace where humans and AI agents dynamically share artifacts, assign tasks, and build on each other's work, scaling collaboration from individuals to organizations.

InstantContext is very similar to PromptQL in its focus on collaborative AI workspaces and shared context for human-agent and multi-agent teams. It directly addresses the 'multiplayer AI agent' and 'shared context' aspects, potentially offering a very close workflow, but the specifics of its 'learning from corrections' might differ.

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