PromptQL is live · free plan, paid from $40/user/mo · last verified 6 Nov 2026 · viability healthy
PromptQL
Multiplayer AI coworker bots that run shared dashboards, docs, sheets, decks and boards for your team.
PromptQL targets the real bottleneck in team AI: context spread across people who never write it down. Bot-per-responsibility plus a shared wiki is the most direct answer we've seen to that, and the OLU table is genuinely useful for budgeting model choice. Watch the math — Team is a $40/user floor billed weekly in $40 increments with consumption OLUs stacked on top, so model choice moves your bill a lot.
Verdict generatedVerified 8d ago · liveness 76/100 · cite: rightaichoice.com/tools/promptql
- Data and analytics teams where the meaning behind a metric lives with three different people
- Engineering teams wanting on-call triage and incident hypotheses assembled from code, logs and tickets
- Finance teams reconciling ARR across billing, CRM and the warehouse and documenting which number wins
- Security and compliance teams running access reviews and splitting up security questionnaires
- Teams under five people — multiplayer shared context is underused at that size and you still pay the per-user floor
- Anyone who needs fully self-hosted or BYOC deployment on day one; that sits on the contract-priced Custom tier
- Buyers who want one flat fee — Team stacks consumption-based OLU charges on a $40/user/month floor billed weekly
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Skip PromptQL if you want a single flat per-seat bill and no usage metering — Team's $40/user/month floor billed weekly in $40 increments sits on top of consumption-based OLU charges that scale with which models you pick.
Consumption-based OLU charges stack on top of the per-paid-user floor, so your bill tracks which models you run, not just seat count
Team's $40/user/month floor billed weekly in $40 increments plus OLU consumption fits mid-size data, engineering and finance teams of roughly 5–50 where shared context pays for itself. Under five people you're better off on a per-seat ChatGPT or Claude plan, which is cheaper when the person who knows the answer is already in the thread. Above a few hundred seats, Enterprise at $1,000/month with a minimum commit and unlimited users usually beats stacking Team seats, and Databricks Genie or
In short
PromptQL — Multiplayer AI coworker bots that run shared dashboards, docs, sheets, decks and boards for your team. Best for Data and analytics teams where the meaning behind a metric lives with three different people, Engineering teams wanting on-call triage and incident hypotheses assembled from code, logs and tickets, Finance teams reconciling ARR across billing, CRM and the warehouse and documenting which number wins. Free to start; paid plans from $40/user/mo.
What is PromptQL?
PromptQL — Multiplayer AI coworker bots that run shared dashboards, docs, sheets, decks and boards for your team. Best for Data and analytics teams where the meaning behind a metric lives with three different people, Engineering teams wanting on-call triage and incident hypotheses assembled from code, logs and tickets, Finance teams reconciling ARR across billing, CRM and the warehouse and documenting which number wins. Free to start; paid plans from $40/user/mo.
PromptQL is a shared workspace where AI bots, not private chat threads, do the work. Give a bot a standing responsibility — the reliability dashboard, a data-quality sheet, a spec doc, a deck, a sprint board — and it runs 24×7 on its own isolated cloud computer with a browser, reaching into databases and SaaS tools where the data already lives. When it hits a wall, you pull in the teammate who has the missing context and the bot keeps moving; output lands in the shared artifact, not a DM. The product ships five named bot surfaces: bot dash, bot sheet, bot doc, bot deck and bot board. Each bot connects to Postgres, Snowflake, BigQuery, Databricks, GitHub, Slack, Salesforce and Google Workspace, and can build its own tools to reach any API or database it doesn't cover. Multiplayer is enforced in the architecture: users cannot ask a bot to reach another user's data, permissions are applied at the data layer, and a private bot can do sensitive work and report back only what it should. A shared context layer — knowledge, skills and a semantic layer documented in a shared wiki — means business definitions and tribal context accumulate over time. You can switch models per bot or mid-thread, from open-weight options served via Fireworks (DeepSeek V4 Flash 0731 at 0.025x, GPT-6 Luna at 0.02x) to frontier models like Claude Fable 5 and GPT-6 Astra, with published OLU multipliers versus Opus 4.6. Shipping across web, Mac (Apple Silicon and Intel), Windows, iOS and Android (both beta), plus Slack and Teams. PromptQL is best when several people each hold a fraction of the context and none of it is written down. If one person owns the answer and works alone, a per-seat assistant will be cheaper and simpler.
Key features
- Multiplayer AI bots shared by multiple users in one workspace
- Five bot surfaces: bot dash, bot sheet, bot doc, bot deck and bot board
- Bots run 24×7 rather than only during an active session
- Each bot works on its own isolated cloud computer with a browser
- Bots build tools to connect to any API or database
- Query Postgres, Snowflake, BigQuery and Databricks in place with no ETL
- SaaS connectors including GitHub, Slack, Salesforce and Google Workspace
- Bots create artifacts and apps — tables, charts, reports, interactive dashboards
- Shared context layer: knowledge, skills and semantic layer in a shared wiki
- Per-user permissions enforced at the data layer
- Private bots do sensitive work and report back only what they should
- Switch models per bot or mid-thread, including open-weight models via Fireworks
- Published OLU multipliers for budgeting model choice vs Opus 4.6
- Multi-user threads with @-mentions of teammates and the agent
- Web, Mac, Windows, iOS and Android apps (iOS/Android beta), plus Slack and Teams
Behind the verdict
Where PromptQL earns its keep: teams where the answer to a metric question lives in three heads. The bot owns the dashboard, flags the cell, and asks the person who knows why the pipeline double-counted. In practice that pattern covers incident triage, ARR reconciliation and access reviews surprisingly well — we'd reach for it whenever the work product is a shared artifact rather than a chat reply. The onboarding blueprint PromptQL published in September, claiming it halved new-hire ramp time internally, is a decent signal of how they think about uses: repeatable, documented, routed to a bot or the cheaper Jev decision model where a frontier LLM call would be overkill. That routing discipline is the part most teams miss. Pass if you're a team of fewer than five, if your workflows are genuinely one-off and private, or if you need self-hosted deployment immediately — BYOC and self-hosted sit on the contract-priced Custom tier, not below it. Small teams get little from shared context and pay the flat $40/user minimum anyway. The comparison that matters is a per-seat ChatGPT or Claude rollout. If one person owns the answer and never needs to hand off, seats win on price and effort. PromptQL pays off only when handoff is the actual job. Model choice is a budgeting lever, not a footnote. Fireworks-served open-weight models — DeepSeek V4 Flash 0731 at 0.025x, GLM-5.3 Flash at 0.03x, Kimi K3 at 0.6x — do meaningfully cheaper work than frontier models such as Claude Fable 5, GPT-6 Astra and Gemini 3.8 Flash, and you can switch per bot or mid-thread. Budget an owner for that decision or OLU consumption will drift. What to watch: the free Playground gets you building, but multiplayer value only shows once a real team is in the workspace. Set expectations that the first month
Models under the hood
as of 2026-11-07
Limitations
- Pricing is per-user plus consumption: Playground is free, Team starts at $40/user/month billed weekly in $40 increments with usage charged on top, Enterprise starts at $1,000/month with a monthly minimum commit, and BYOC/self-hosted plus bring-your-own-models sits on the contract-priced Custom tier.
- Total spend depends on which models you run — frontier models like Claude Fable 5 and GPT-6 Astra consume the most OLUs, while open-weight models work for a fraction.
- Integration breadth depends on your stack: Postgres, Snowflake, BigQuery, Databricks, GitHub, Slack, Salesforce and Google Workspace are documented, while niche apps rely on a bot building its own connector.
as of 2026-10-13
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Is PromptQL still active?
PromptQL is live · free plan, paid from $40/user/mo · last verified 6 Nov 2026 · viability healthy.
Viability score
How well maintained and how widely used is PromptQL? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What's new in PromptQL
Checked 13 days agoAcross the latest 5 updates: 5 community discussions.
Can Jev and an LLM Work Together in One Workflow?
PromptQL describes mixing Jev decision steps with LLM steps inside a single agent workflow.
Jev vs LLMs: What's the Difference and When to Use Each
PromptQL argues small repetitive AI tasks should route to Jev rather than a frontier LLM on every call.
Jev vs ChatGPT for Classification, Routing and AI Agents
PromptQL compares its Jev decision model against ChatGPT for classification, routing and agent control flow.
Your Next Move: How to Use AI for Your Job Search
PromptQL and Jev walkthrough of applying their AI to job-search research and outreach workflows.
We cut new-hire ramp time in half. Here is the exact 7-step blueprint.
PromptQL publishes a seven-step onboarding blueprint it says halved new-hire ramp time internally.
What do users say about PromptQL?
What people actually say about PromptQL — is it worth it?
Our community sentiment research written Jul 24, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about PromptQL — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 6 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified PromptQL 12 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing tiers, description, who should skip it, our verdict, summary, features
- — changed platforms, limitations, pricing page
- — changed summary, pricing tiers, who should skip it, features, models, hidden costs, switching out, switching in, limitations, description, use cases, setup time, pricing fit, our verdict
- — changed features, description, our verdict, who it suits, integrations, who should skip it, pricing tiers, summary, pricing page, models, limitations
- — changed SSL certificate
- — changed SSL certificate
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does PromptQL cost in 2026?
PromptQL: Free to start; paid plans from $40/user/mo. Pricing last verified 6 Nov 2026.
Plans compared
Every published PromptQL tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Playground | FreeVendor: “$0/user/month” | — | A single team lead or small group evaluating whether multiplayer bots fit their workflow before committing any budgetAdds: Free entry point: a workspace to build multiplayer bots with no usage budget — shared context (knowledge, skills and semantic layer) is not included |
| Team | From $40/user/moVendor: “Starts at $40/user/month” | monthly, per seat | Mid-size data, analytics, engineering or finance teams around 5–50 people who need shared bots and the wiki but can run on shared infrastructureAdds: Adds shared context — knowledge, skills and semantic layer — on a $40/user/month floor billed weekly in $40 increments, with consumption-based OLU charges on top |
| Enterprise | From $1,000/moVendor: “Starts at $1,000/month” | monthly | Larger organizations with a security review, many seats and a need for dedicated infrastructure rather than multi-tenantAdds: Adds unlimited users and single-tenant deployment on consumption-based pricing with a $1,000/month minimum commit |
| Custom | Custom | quote | Regulated or air-gapped buyers, or teams that must run their own models and want a vendor engineering team embeddedAdds: Contract pricing adds BYOC or self-hosted deployment, bring-your-own-models and a dedicated forward-deployed team |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where PromptQL's pricing actually pencils out — and where peers do it cheaper.
Team's $40/user/month floor billed weekly in $40 increments plus OLU consumption fits mid-size data, engineering and finance teams of roughly 5–50 where shared context pays for itself. Under five people you're better off on a per-seat ChatGPT or Claude plan, which is cheaper when the person who knows the answer is already in the thread. Above a few hundred seats, Enterprise at $1,000/month with a minimum commit and unlimited users usually beats stacking Team seats, and Databricks Genie or
Has PromptQL's pricing changed?
No plan price for PromptQL has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick PromptQL?
Pick PromptQL if you are Data and analytics teams where the meaning behind a metric lives with three different people, Engineering teams wanting on-call triage and incident hypotheses assembled from code, logs and tickets, Finance teams reconciling ARR across billing, CRM and the warehouse and documenting which number wins, Security and compliance teams running access reviews and splitting up security questionnaires.
Look elsewhere if you are Teams under five people — multiplayer shared context is underused at that size and you still pay the per-user floor, Anyone who needs fully self-hosted or BYOC deployment on day one; that sits on the contract-priced Custom tier, Buyers who want one flat fee — Team stacks consumption-based OLU charges on a $40/user/month floor billed weekly, Workflows that are genuinely one-off and private, where a per-seat assistant is simpler and cheaper.
Skip it if Skip PromptQL if you want a single flat per-seat bill and no usage metering — Team's $40/user/month floor billed weekly in $40 increments sits on top of consumption-based OLU charges that scale with which models you pick.
GeneratedReal-world workflow fit
Concrete scenarios for the personas PromptQL actually fits — and what changes day-one when you adopt it.
Someone circles a Tuesday 4pm spike on the reliability dashboard. Three people each know a different cause — a deploy, a late pipeline, a recurring alert — and each points the bot at where that data lives.
Outcome: The bot connects to all three sources, tests each theory against the spike and adds a panel with the answer directly to the shared dashboard instead of replying in a DM.
An alert fires in a service the on-call engineer doesn't know. They ask the bot to pull recent changes and similar incidents and identify the right expert.
Outcome: The bot assembles the change history and similar incidents, posts a hypothesis with what to confirm, and sends it to the expert rather than paging them cold.
They need to know who can reach the customer tables and which services use them, then split security questionnaire questions by who'd actually know.
Outcome: The bot flags anomalies and drafts the access fix without changing anything, and routes questionnaire items to infra, controls and DPA owners, reusing past answers for the rest.
Use cases
- Point at a reliability dashboard spike and let the bot test every colleague's theory against the data, then add a panel with the answer
- Let a bot own a data-quality sheet: fix a bad cell at the source, rebuild the sheet, and log every fix
- Maintain a spec doc where the bot checks each comment against code and tickets and writes the decision into the doc
- Draft the monthly metrics review from the warehouse, dashboards and last month's deck, then rewrite slides around what people know
- Paste standup notes into a bot-run board and have it rework the plan and tell the people affected
- Trace a stale orders pipeline, find the broken dependency, pull in the owner and post the fix with evidence
- On-call triage: pull recent changes and similar incidents, then send the right expert a hypothesis to confirm
- Run an access review of who can reach customer tables and draft the fix without changing access
Setup time & first value
How long it actually takes to get something useful out of PromptQL — broken out by persona, not the marketing-page minute.
Sign-in is once: you join your team's project and your threads, data connections and shared wiki follow you across web, Mac, Windows, iOS, Android, Slack and Teams — so first value on a single dashboard or sheet bot is typically a same-day task. Wiring the shared context wiki to be genuinely useful takes longer, because accuracy depends on your team correcting the bot and documenting definitions;
Switching to or from PromptQL
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From ChatGPT or Claude per-seat rollout: keep the seats for individuals, stand up a PromptQL Team workspace for the team-owned dashboards, docs and sheets, then move shared metric definitions into the wiki
- →From a warehouse-native assistant like Snowflake Cortex or Databricks Genie: point PromptQL at the same Postgres, Snowflake, BigQuery or Databricks sources in place — no ETL — and reuse existing dashboards as the
- →From ad-hoc Slack bots and scheduled scripts: replace the script with a bot that runs 24×7 on its own cloud computer and can build its own connector for APIs the script hardcoded
- →From per-team Notion or Confluence runbooks: have a bot doc own the spec and check comments against code and tickets so decisions land in the doc next to the comment
- ↗To a per-seat ChatGPT or Claude plan: export the artifacts and wiki content you care about, then keep individual work in the assistant and drop shared bot responsibilities if the team is too small to use multiplayer
- ↗To a warehouse-native assistant (Snowflake Cortex, Databricks Genie): move SQL-first questions back into the warehouse layer and keep PromptQL only where multi-source context and artifact upkeep matter
- ↗To a self-hosted or air-gapped agent stack: use the Custom tier's BYOC/self-hosted deployment and bring-your-own-models rather than leaving the platform
- ↗To a plain BI tool: hand off the dashboards and metric definitions by documenting which number wins in the semantic layer notes before you switch
Integrations
Tutorials & Learning
YouTube returned 6 videos for “PromptQL”, and we withheld 6: 6 could not be judged, because “PromptQL” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about PromptQL.
Official links
What are the best PromptQL alternatives?
The closest alternatives to PromptQL we list are Ambient, Granicus and Rowboat. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Ambient | Paid | Pricing not published | — |
| Granicus | Contact Sales | Custom pricing | — |
| Rowboat | Freemium | Pricing not published | — |
Alternatives to PromptQL
View all
Tools that pair well with PromptQL
Common stack mates teams adopt alongside PromptQL, with the specific reason each pairing earns its keep.
Ambient
Ambient pools your team's meetings, chats, and emails into one shared context layer your AI assistants can read.
Granicus
Granicus powers AI-driven citizen experience software for government service delivery, resident communications, and public meetings.
Rowboat
Rowboat is a self-hosted AI coworker that keeps your work context local and shares it through team Spaces.
PromptQL vs its closest rival
PromptQL's closest head-to-head is with Gem — If your job is hiring at scale, Gem’s AI agents (sourcing, screening, fraud detection) and all-in-one ATS/CRM are unmatched. If your team drowns in Slack threads and context switching, PromptQL’s multiplayer AI with shared context replaces the chat tool entirely. They solve different problems—choose based on whether you need to fill seats or replace Slack. Verdict generated
Head-to-head comparisons
Promptql vs Gem
If your job is hiring at scale, Gem’s AI agents (sourcing, screening, fraud detection) and all-in-one ATS/CRM are unmatched. If your team drowns in Slack threads and context switching, PromptQL’s multiplayer AI with shared context replaces the chat tool entirely. They solve different problems—choose based on whether you need to fill seats or replace Slack.
GeneratedPromptql vs Poke Interaction Co
Choose PromptQL if you need a multiplayer AI agent that replaces Slack for your team, with shared context, auto-skills, and integrations for engineering/product workflows. Choose Poke if you want an AI personal assistant inside your existing messaging apps (Apple Messages, WhatsApp, Telegram) to manage email, calendar, health data, and personal automations. They serve different needs: team collaboration vs. personal productivity.
GeneratedPromptql vs Guesty
PromptQL and Guesty serve completely different domains: PromptQL replaces team chat with a multiplayer AI that learns from your team, while Guesty automates vacation rental operations. Choose PromptQL if you're a fast-moving engineering or product team wanting to reduce context switching and centralize knowledge. Choose Guesty if you manage 4+ rental properties and need AI-driven messaging, reconciliation, and multi-channel distribution. There's no overlap in use case.
GeneratedFrequently asked questions
Best-of guides
For makers of PromptQL
- Get a Verified Listing for PromptQL ($39): a written decision within 1 working day.
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