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Smithery vs Kanlet

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Smithery compared with Kanlet
DetailSmithery logo SmitheryKanlet logo Kanlet
What it doesConnect agents to thousands of tools and services with managed authentication and credentials Semantic context layer that helps AI agents understand your business data accurately
StageNot given MVP
FoundedNot given Not given
Based inNot given Not given
Pricing modelFreemium Not given
PricingHobby: Free (50K RPCs/month, 3 Namespaces, Managed OAuth, Persistent connections); Pay as you Go: $10/mo (100K RPCs/month included, then $0.10 per 1K RPCs, 100 Namespaces, Managed OAuth, Persistent connections); Custom: Contact for pricing (Everything in Pay as you Go plus custom rate limits, uptime Not given
Key features
  • Managed OAuth and credential storage
  • Persistent connections
  • Public MCP registry with hundreds of pre-built services
  • Compatible with Claude, Cursor, and MCP-compatible AI assistants
  • Multiple namespace support
  • RPC-based usage model
  • Connects to 15+ data systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI and Postgres
  • Extracts structural metadata including databases, schemas, tables, views, columns and relationship lineage
  • Provides Web-UI, MCP server and REST APIs for accessing semantic context
  • Automatic incremental updates with configurable sync schedules from on-demand to daily or weekly
  • Role-level access controls and data classifications including PII, PHI and SPII
  • Only reads metadata without accessing or moving actual data from systems
  • Integrates with quality monitoring tools including Monte Carlo, Anomalo and Bigeye
What makes it different
  • Handles authentication and credential complexity for agents
  • Unified interface to thousands of tools and services
  • Public registry of MCP servers for easy discovery
  • Free tier available for getting started
  • Eliminates confident AI mistakes by grounding agents in semantic context instead of having them guess from table names
  • Reduces AI token costs and speeds up answers by resolving schema and joins once upstream instead of in each agent query
  • Ensures consistent answers across all agents and teams using the same definitions and business logic
  • Provides complete traceability and auditability of AI decisions with documented context and ownership
Free planNot given Not given
Open sourceNot given Not given
PlatformsNot given Not given
Public APINot given Not given
Community votes00
DR (Domain Rating by Ahrefs)79 no change since last check8 no change since last check
Trust Flow (Majestic)241
Citation Flow (Majestic)5524
Referring domains (Majestic)377529

3 details filled in for both companies.

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