Supabase vs TrackMCP: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Supabase and TrackMCP — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Supabase
Supabase
Postgres-first development platform providing hosted Postgres, Auth, instant APIs, Realtime, Functions, Storage and vector embeddings.
Key features
- Hosted Postgres Database: Provides a managed, dedicated PostgreSQL instance with tools and integrations designed for production web, mobile, and AI applications.
- Auto-generated APIs: Instantly exposes REST and GraphQL endpoints from the Postgres schema (PostgREST/Graphile-style auto-APIs) so developers can query and mutate data without writing backend code.
- Authentication and Authorization: Built-in user management, sign-up/sign-in flows, JWT-based auth, and role/row-level security integrations to secure database access.
- Realtime Subscriptions: Change-data-capture based realtime updates and WebSocket subscriptions to push database changes to clients for live features like chat or presence.
- Functions (Edge & Database): Support for serverless Edge Functions alongside Postgres database functions (RPC) to run custom business logic close to data.
- File Storage: Object storage for uploading, serving, and managing files/media with access controls integrated with Supabase Auth.
- Vector Embeddings Toolkit: Integration with pgvector and embedding tooling for semantic search, similarity queries, and AI-driven features.
- Client Libraries & Local Dev: Official SDKs (e.g., supabase-js), dashboard, CLI and local stack tooling (npx supabase start) for rapid development and self-hosting.
- Hosted Postgres database with full SQL/Postgres feature set
- Authentication and Authorization (OAuth, RLS integration with Postgres)
- Auto-generated REST APIs (PostgREST) and GraphQL endpoints
- Realtime subscriptions and broadcasting for database changes
- Database Functions (rpc) and Edge Functions (serverless)
- File Storage for uploading/downloading files
- AI & Vector/Embeddings toolkit (pgvector support and related tooling)
- Official client libraries (supabase-js is an isomorphic JS/TS client)
- Dashboard and management console for projects
- Self-hosting support via supabase CLI (npx supabase start) and Docker
- Open-source codebase (Apache-2.0 for core, MIT for many clients)
Best for
- Full-stack applications: Quickly build web and mobile backends with a managed Postgres database, auto-generated APIs, auth, and storage without building a custom backend.
- Realtime apps: Implement live chat, collaborative editing, dashboards, or presence systems using realtime subscriptions based on Postgres change data capture.
- Authenticated user platforms: Manage sign-ups, logins, roles, and row-level security for multi-tenant or user-specific data access directly integrated with the database.
- File and media handling: Store, serve, and manage user uploads and assets with built-in object storage linked to project auth and permissions.
- Semantic search and AI features: Store vector embeddings in pgvector and run similarity queries for recommendations, semantic search, and retrieval-augmented generation workflows.
- Self-hosted development & testing: Run a full local Supabase stack (CLI npx supabase start) for local development, testing, or self-hosted production deployments.
- Web and mobile backends with Postgres as primary datastore
- Realtime applications (chat, live dashboards) using subscriptions
- Auto-generated APIs for rapid prototyping and production services
- Serverless business logic via Edge Functions and Postgres functions
- File upload, CDN-backed storage and media management
- Vector search and embeddings-powered semantic search or recommendation systems
- Self-hosted local development and CI via supabase CLI and monorepo tooling
TrackMCP
TrackMCP
Analytics for MCP servers — see which AI clients connect, which tools they call, whether the work completes and what to fix.
Key features
- One-line install: Drop the @trackmcp/sdk into an existing TypeScript or Python MCP server with no manual event tagging
- Client breakdown: See the share of traffic coming from Claude, Cursor, ChatGPT and custom agents
- Tool analytics: Per-tool call volume, adoption, latency percentiles and health status ranked in one table
- Workflow paths: Follow sessions from first request to result and see exactly where they stop
- Outcome tracking: Completion rates, sessions that reached a tool and returning clients within seven days
- Hidden-error detection: Flags calls that report 200 OK while returning isError, with retry counts and a suggested fix
- Real-time dashboard: Events appear as they happen across production and staging environments
- Alerts: Slack and webhook notifications when a tool starts failing or a workflow degrades
Best for
- An MCP server author finds out which of their tools agents actually call and which have never been used
- A team diagnoses why a checkout workflow stops at 38% instead of completing, by replaying the session path
- A maintainer catches a tool failing 94% of calls behind a 200 OK response that logs never surfaced
- A product team measures whether new clients keep coming back within seven days of first connecting
- An engineer compares latency and error rates across production and staging before shipping a schema change
- A company decides which MCP tools to invest in by ranking them on adoption rather than guesswork