# Bi · BI Copilot — element 46 of 58 > Ask your data in English. Turns dashboards into conversations. - **Group:** 10 · Data & Analytics - **Necessity:** Optional - **Price band:** $$ · $30–150/mo - **Maturity:** Experimental - **Edition:** v2026.Q3 · verified 2026-09-13 ## Leading tools (v2026.Q3) - **Omni** — modeled bi, then chat - **Hex** — analyst copilot plus threads - **Lightdash** — dbt-native, no per-seat - **Metabase** — free tier, real ai - **ThoughtSpot Spotter** — search box for everyone ## Our take The dashboard graveyard problem is real. Conversational BI works when your data model is clean — that's the actual work. ## Combines with Wh, An ## The top 5 — deep dossier (verified 2026-09-13) Omni, if you want one tool that makes "ask your data in English" actually work — it is a modeled BI platform first and a chat box second, which is the only architecture that survives contact with real questions (Series C $120M at $1.5B, 4x YoY revenue, Apr 23, 2026). Hex is the pick when you have analysts and want the copilot to help them, not replace them — it scored highest of any real BI product on the only public head-to-head accuracy test. Lightdash when your metrics already live in dbt and you refuse per-seat pricing. Metabase when the budget is zero and the questions are simple. ThoughtSpot when a search box for 500 non-technical users is the actual requirement. And if you already pay for Databricks, Fabric, or Looker, use the copilot you own before you buy a fifth one — the differentiator in this category is never the LLM, it is whether someone did the semantic modeling. 1. **Omni** (Omni Analytics (Colin Zima, ex-Looker)) — No public price list — sales-quoted annual contracts (verified Aug 2026). AI chat, modeling agent and MCP server included; LLM tokens not separately metered.. Best for: Startups that want Looker-grade governed metrics without Looker's cost or rebuild tax, with conversational access bolted to the same model. Why: Built by the people who built Looker, on the correct premise for this category: the chat is only as good as the semantic model, so Omni ships a modeling agent to build the model and an AI chat that queries through it, plus an MCP server so Claude, ChatGPT and Cursor hit the same governed logic. ICONIQ led a $120M Series C at $1.5B on Apr 23, 2026 against 4x YoY revenue — up from a $650M mark in March 2025 — with BambooHR (100,000+ end users), Checkr and Cribl as reference accounts. Watch: Zero published pricing means you cannot budget without a sales call, and the entry point is not seed-stage cheap. The modeling agent shortens the semantic-layer build but does not remove it — you are still doing the actual work, just faster. No independent accuracy benchmark for Omni's AI exists as of Aug 2026. [https://omni.co](https://omni.co) 2. **Hex** (Hex Technologies) — Community free (5 projects) · Professional $36/editor/mo · Team $75/editor/mo · Enterprise custom. Per-seat AI credit grants on paid plans; compute add-ons $0.32–$4.06/hr. BYO API key Enterprise only.. Best for: Teams with at least one analyst — the Notebook Agent accelerates them, Threads gives everyone else a governed chat surface on the same models. Why: Hex is the rare vendor shipping both halves: a Notebook Agent that writes and debugs code with dependency awareness, and Threads (Oct 1, 2025) — a step-by-step reasoning chat for non-technical users, reachable from Slack, Claude and Cursor — plus a Modeling Agent for the semantic layer underneath. It scored 80.6% on BI Bench, third overall and the highest of any purpose-built BI product tested, behind only two general coding agents. Pricing is published, which in this category is itself a differentiator. Watch: Threads and the semantic-model agent require the $75/editor Team plan; the free and Professional tiers get the notebook agent only. Hex's centre of gravity is still the analyst notebook — element Wh territory — so buying it purely as a business-user chat box overpays. Last confirmed raise is the $70M Series C (May 2025); 2026 round listings on aggregator sites are unverified. [https://hex.tech](https://hex.tech) 3. **Lightdash** (Lightdash (Apache-2.0)) — Open source free (self-hosted) · Cloud Pro $3,000/mo flat, unlimited users, AI agents included · Enterprise custom · embedding $0.05/load after 1,000 free or $790/mo for 100k loads. Best for: Teams whose metrics already live in dbt YAML and who refuse to pay per seat to let the company read its own numbers. Why: The dbt-native path: metrics and dimensions are defined once in your dbt project and the AI agents query that, with Slack delivery, memory that learns from user corrections, and — uniquely among the top five — built-in evaluations to regression-test agent accuracy as your model changes. Apache-2.0 and self-hostable (5.9k GitHub stars, Aug 2026), so the escape hatch is real. Flat pricing means adding 40 viewers costs nothing. Watch: $3,000/mo flat is the wrong shape for a five-person startup — you self-host or you pay Looker money. AI agents are Cloud Pro or an Enterprise add-on, not in the open-source build. It scored 23.8% on BI Bench at default settings, second-lowest of eleven — a result that says more about ungroomed dbt models than about Lightdash, but the dependency is the whole point of the category. [https://www.lightdash.com](https://www.lightdash.com) 4. **Metabase** (Metabase) — OSS free (self-host) · Cloud Free $0 · Starter $100/mo (5 users, +$6/user) · Pro $575/mo (10 users, +$12/user) · Enterprise from ~$20k/yr. AI on every tier; $3.75 per 1M tokens if you don't BYO key (1M free).. Best for: Pre-Series-A teams who need the answer today and cannot justify a four-figure monthly BI line item. Why: The only tool here where natural-language questions and SQL generation are on the free tier, and the 2026 releases are serious: Metabot, an MCP server, Slack integration, Data Studio for semantic-layer curation, and a dependency graph so you can verify model integrity before the AI leans on it. There is also a master kill switch for all AI — a governance feature the enterprise vendors mostly lack. Watch: It finished last on BI Bench at 12.4%, and Metabase's own docs are candid about why: accuracy depends on field descriptions, correct semantic types and a glossary you have to write. Out of the box, pointed at raw warehouse tables, this is the tool most likely to confidently answer the wrong question. Verified-content-only mode for Metabot is Pro/Enterprise. [https://www.metabase.com](https://www.metabase.com) 5. **ThoughtSpot Spotter** (ThoughtSpot) — Essentials from $25/user/mo annual (5–50 users, 25M rows) · Pro from $50/user/mo (to 1,000 users, 250M rows) · Enterprise & Embedded custom · Developer free 1 yr (10 users). LLM tokens explicitly not metered.. Best for: Rolling a search box out to hundreds of non-technical staff who will never open a BI tool, with governance and unmetered token cost. Why: ThoughtSpot invented search-first BI and is the only pure-play NLQ vendor named a Leader in the Jun 29, 2026 Gartner Analytics & BI Magic Quadrant, alongside Microsoft, Salesforce Tableau, Google Looker and Qlik. The 2026 agent line went beyond chat: SpotterViz builds dashboards from language, SpotterModel builds semantic models without code, SpotterCode generates embedding code — plus industry-tuned Spotter agents. Not metering LLM tokens matters when 400 people start asking questions. Watch: The $25 list price is the smallest part of the bill — this is an enterprise sales motion with modeling services attached, and analysts flag semantic modeling as the bottleneck the agents only partially remove. No independent accuracy data exists, no ARR is disclosed, and the persistent IPO-or-sale speculation means the strategic picture in 12 months is genuinely unknown. [https://www.thoughtspot.com](https://www.thoughtspot.com) ### How to choose - If You are already paying for Databricks, Microsoft Fabric, Looker or Snowflake → Use the copilot you own before buying a fifth tool. 1.5M Genie Spaces were created in 2026 alone; Fabric Copilot dropped to F2 capacity (~$262/mo) with no per-user AI fee. Buy a standalone BI copilot only after the built-in one demonstrably fails. - If You have no semantic layer and no one to build one → Do not buy conversational BI yet — buy the modeling. Standalone semantic-layer adoption tripled from ~8% to ~28% among data teams in six months (Hex, Dec 2025) precisely because chat without a model produces confident wrong answers. Omni's or ThoughtSpot's modeling agent shortens that job; nothing skips it. - If Your metrics already live in dbt and you have more viewers than analysts → Lightdash — flat pricing, agents that query your existing dbt YAML, and built-in evals to catch regressions. Self-host free if $3,000/mo is out of range. - If You have analysts and the bottleneck is their throughput, not executive self-service → Hex at $36–75/editor. It out-scored every other BI product on the one public head-to-head accuracy test, and the notebook agent compounds with the people you already employ. - If An executive will make a real decision on the AI's answer → Require citation-to-source and verified-content-only modes before rollout. Only 51% of 114 data leaders trust AI-generated insights and 26% have already been burned by an inaccurate one (insightsoftware, Jun 2026); audit trails linking output to source data were the single most-requested fix (53%). ### The field (30 more) Sigma, Databricks AI/BI Genie, Databricks One (renamed), Snowflake Cortex Analyst / Cortex Agents, Power BI Copilot, Looker Conversational Analytics, Tableau Next / Tableau Agent, Amazon Q in QuickSight, QuickSight Q (renamed), Qlik (Answers / Insight Advisor), Zenlytic (Zoë), Basedash, TextQL (Ana), Julius AI, Fabi.ai, Dot, Querio, Veezoo, Tellius, AnswerRocket, GoodData.AI, Pyramid Analytics, Steep, Preset / Apache Superset, Rill Data, Evidence, Holistics, Domo AI, Sisense, Narrative Science (acquired) Full dossier data: https://elems.ai/e/bi.json --- Source: [elems.ai](https://elems.ai/e/bi.html) — the periodic table of the AI-led startup. Data: https://elems.ai/elements.json (CC BY 4.0, cite elems.ai).