pgflow vs Inngest

Features

Technical Criteria	pgflow	Inngest
Architecture Model	Database-centric orchestration	Durable execution engine
Infrastructure Requirements	Zero additional (just Supabase)	Hosted service or self-hosted
Workflow Definition	TypeScript DSL with explicit dependencies	Step-based functions with checkpoints
Supabase Integration	Built-in	Requires manual configuration
Type System	End-to-end type safety	Standard TypeScript typing
Failure Handling	Automatic per-step retries	Automatic per-step retries
Execution Structure	Database-driven DAG	Linear step-by-step with state persistence
State Management	Input/output data stored in database	Input/output memoized in execution service
Time-based Operations	Via Supabase pg_cron	Built-in sleep and waitForEvent capabilities
Maturity Level	Active development	Production-ready
Monitoring Tools	SQL queries for visibility	Comprehensive web dashboard
Concurrency Management	Simple per-flow limits	Advanced throttling and concurrency controls
Event System	No built-in event system	Rich event-driven architecture

Both systems provide reliable task execution with proper retries and error handling. The key difference is who controls the workflow orchestration: the database (pgflow) or a durable execution engine (Inngest).

When to Choose

pgflow

• Supabase-native solution - You need workflow orchestration directly within your Supabase stack
• Transparent state storage - You want workflow state directly visible and queryable in your database
• Parallel data processing - Your workflows involve processing data with multiple parallel paths
• Database-centric ecosystem - Your system already centers around PostgreSQL operations
• Explicit data dependencies - Your processes have clear input/output relationships between steps
• Migration-based deployments - You prefer deploying workflow changes via database migrations
• Minimal infrastructure - You want to avoid adding services beyond your Supabase project

Inngest

• Event-driven systems - You’re building applications centered around events and reactions
• Temporal workflows - You need sequences that wait for time or external events before continuing
• Sequential steps - Your processes follow linear paths with checkpointing
• UI visibility need - You need a dedicated dashboard for monitoring workflow executions
• Multi-environment deployment - You need to run the same workflows across different platforms
• Sleep/wait mechanics - Your workflows pause for specific time periods or external triggers
• Throttling requirements - You need sophisticated rate limiting for external API calls

Code Examples

pgflow

pgflow puts PostgreSQL at the center of your workflow orchestration. Workflows are defined in TypeScript but compiled to SQL migrations with all orchestration logic running directly in the database. The database decides when tasks are ready to execute based on explicit dependencies.

// In pgflow, the database orchestrates the workflow
new Flow<{ url: string }>({
  slug: 'analyze_website',
})
  .step(
    { slug: 'extract' },
    async (input) => /* extract data */
  )
  .step(
    { slug: 'transform', dependsOn: ['extract'] },
    async (input) => /* transform using extract results */
  );

Inngest

Inngest uses a durable execution engine to orchestrate workflows. Functions are defined as a series of steps, where each step is a checkpoint that can be retried independently. The execution engine tracks state and resumes execution from the last successful step.

// In Inngest, steps define checkpoints within a function
import { Inngest } from "inngest";

const inngest = new Inngest({ id: "my-app" });

export default inngest.createFunction(
  { id: "analyze-website" },
  { event: "app/website.analyze" },
  async ({ event, step }) => {
    // Each step is retried independently
    const extracted = await step.run("extract", async () => {
      return await extractData(event.data.url);
    });

    // State is automatically passed between steps
    const transformed = await step.run("transform", async () => {
      return await transformData(extracted);
    });

    // Return the final result
    return { data: transformed };
  }
);

Similarities

Both systems provide reliable execution of workflow tasks:

• Both handle retries and error recovery
• Both track execution state and progress
• Both provide type safety
• Both can process steps asynchronously
• Both store the state of workflow executions

The difference is architectural:

• pgflow: PostgreSQL orchestrates when steps run based on dependencies
• Inngest: Execution engine orchestrates steps sequentially with state memoization

Integration with Supabase

pgflow

• Native integration - Built specifically for Supabase ecosystem
• Zero infrastructure - Uses Supabase Edge Functions and PostgreSQL
• Simple setup - Single command (npx pgflow install) sets up all required components
• Direct database access - All workflow state directly accessible in your Supabase database

Inngest

• Possible integration - Can work with Supabase but not specifically designed for it
• Additional infrastructure - Requires either hosted service or self-hosted instance
• Event bridging - Requires connecting Supabase events to Inngest’s event system
• Separate state storage - Workflow state stored outside your Supabase database