Akkio vs Metabase

A detailed comparison to help you choose between Akkio and Metabase.

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Verdict: Metabase wins

Metabase scores 4.3/5, edging out Akkio at 3.2/5. Small and mid-size teams wanting self-hostable, easy-to-use BI where AI answers are inspectable and governed by their own metric definitions.

Akkio

Akkio

No-code predictive AI for business teams

Metabase

Metabase

Open-source BI with AI query assistance

Rating3.2 (1 review)4.3 (1 review)
Pricing Modelpaidfreemium
Starting PriceFrom $49/moFree tier available
Best ForMedia and advertising agencies — especially holding-company scale — that want audience building, planning and measurement consolidated with white-label client delivery, and can buy enterprise.Small and mid-size teams wanting self-hostable, easy-to-use BI where AI answers are inspectable and governed by their own metric definitions.
Free Tier
API Access
Team Features
Open Source
Tags
no codeteam features
open sourcefree tierteam features
Visit Akkio →Visit Metabase →

Akkio

Pros

  • + Agency workflow chain (strategy → audiences → planning → activation → measurement) matches how media teams actually operate
  • + White-label Build-On package lets agencies resell analytics under their own brand — the most distinctive capability in the category
  • + No-code model building really was accessible: churn-propensity training took about ten minutes in my earlier self-serve testing
  • + Serious enterprise validation: Havas holding-company deal across 100+ markets (Jan 2026) plus Horizon Media and LG Ad Solutions
  • + Predictive modelling folded into planning and activation rather than sold as an isolated toy

Cons

  • - Self-serve pricing is gone — no free tier, no trial, no way to evaluate without a sales process
  • - The $49/$99 tiers that still flood review sites are stale and misleading to buyers
  • - Small team (~32 people) serving holding-company clients leaves smaller accounts at service risk
  • - No-code modelling has a hard ceiling: limited model customization versus a real data science stack
  • - The standalone prediction tool that built the reputation is no longer sold on its own
View full Akkioreview →

Metabase

Pros

  • + Semantic-layer-grounded AI: in my testing, questions resolved correctly almost every time where metrics were defined, and the docs openly state the failure mode elsewhere
  • + Generated SQL is inspectable before execution and every chart drills through to its query — fixing one wrong definition corrected the answer for everyone
  • + Open Source edition is free with unlimited users, questions and dashboards, self-hostable
  • + Metabot answered a data question and posted the chart in Slack without a context switch in my test
  • + Same price for cloud and self-hosted Pro ($575/month including 10 users), with an MCP server for governed external AI access

Cons

  • - Not a machine-learning product — no forecasting, anomaly detection or predictive modelling
  • - AI governance controls (token limits, usage analytics, system prompts) sit on Pro at $575/month, a large jump from free
  • - Embedded viewer accounts count as billable users on paid tiers
  • - Metabot works best in English and only inspects the first 100 tables when no table is specified
  • - Complex chart layouts still require SQL or the API
View full Metabasereview →

Frequently Asked Questions

Which is better, Akkio or Metabase?▼
Metabase scores higher at 4.3/5 vs 3.2/5 for Akkio, making it the better-rated option in our comparison.
What is the difference between Akkio and Metabase?▼
Akkio: Akkio is an AI analytics platform for media and advertising agencies, used to build audiences, forecast campaign results and automate client reporting. Sold by custom quote. Metabase: Metabase is open-source business intelligence software with a semantic layer, so questions asked in natural language resolve against defined metrics and the underlying query stays inspectable.
Is Akkio or Metabase more affordable?▼
Akkio starts at €49/mo, Metabase starts at €0/mo. Check the comparison table above for current pricing.

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