Propel alternatives

2 tools to consider instead of Propel, shown against it.

Propel MetricFlow Honeydew
Vendor Propel dbt Labs Honeydew
Pricing model Quote only Open source + paid options Subscription
Free tier — Yes No
Deployment Cloud Cloud, Self-hosted Cloud
Open source No Yes (Apache-2.0) No
Best for Product engineering teams embedding fast, customer-facing metrics and reports into their own application. Analytics engineers already on dbt who want one governed metric definition queried consistently everywhere. Snowflake-centric teams wanting one semantic layer shared by BI dashboards and AI analyst agents.
Pricing

Propel does not publish self-serve pricing; plans are scoped through a sales conversation.

Pricing has not been verified yet — see the vendor's site.

MetricFlow itself is free, open-source Python; serving its metrics in production through the hosted dbt Semantic Layer requires a paid dbt platform plan.

Pricing has not been verified yet — see the vendor's site.

Per-user monthly pricing plus a platform fee that scales with the number of active semantic objects; a 14-day trial is available.

Lite $20/user/month
Standard $30/user/month
Enterprise Custom pricing

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features
  • Single GraphQL endpoint for counters, time series and leaderboard queries
  • Managed ClickHouse-backed Data Pools for storage
  • Built-in filtering, pagination and sorting on queries
  • Metric Report API for building tabular reports quickly
  • Pre-defined and inline metric definitions
  • Designed for embedding analytics inside a product UI
  • Metrics, dimensions and entities defined as YAML alongside dbt models
  • Compiles metric requests into warehouse SQL automatically
  • Handles joins, aggregation and time-granularity conversion
  • Command-line interface for local querying and validation
  • Python API for embedding in other tools
  • Powers the dbt Semantic Layer's API and BI integrations
  • Consistent metric definitions across dashboards, notebooks and agents
  • Semantic modeling layer native to Snowflake
  • No-code and code-based metric and dimension definitions
  • Governed SQL compilation for BI tools
  • MCP-compatible access for AI agents and chatbots
  • Lineage across metrics, models and consumers
  • Query consistency across BI and AI channels
  • Role-based access control on semantic objects

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