Hasura vs PostGraphile: Quick Verdict
Hasura vs PostGraphile is a recurring decision for teams building GraphQL API layers over PostgreSQL — and it comes up often in AI product architectures where you need fast, flexible data access alongside LLM inference calls.
Choose Hasura if you need a managed, multi-source data platform with built-in auth, real-time subscriptions, and a GUI-driven workflow. It's the faster path to a production API with less custom code.
Choose PostGraphile if you need maximum PostgreSQL leverage, prefer code-first customization, want zero vendor lock-in, and are comfortable owning the infrastructure.
Both are excellent. The decision depends on how much you value autonomy over convenience.
What Each Tool Does
Hasura is an open-core GraphQL engine that introspects your database and generates a full CRUD API automatically. It adds an authorization layer (row-level security through its own permission system), event triggers, remote schemas, and subscriptions. Hasura Cloud adds managed hosting, observability, and a collaborative console.
PostGraphile is a Node.js library (and CLI) that also introspects PostgreSQL and generates a GraphQL API — but leans much harder into PostgreSQL's native capabilities. It exposes stored procedures, row-level security policies, and custom SQL functions directly as GraphQL operations. It's code-first, plugin-driven, and self-hosted by design.
Feature Comparison
| Feature | Hasura | PostGraphile |
|---------|--------|--------------|
| Auto-generated CRUD | ✅ Yes | ✅ Yes |
| Real-time subscriptions | ✅ Live queries + events | ✅ Via @graphile/pg-subscriptions |
| Auth integration | ✅ JWT + session vars | ✅ Delegates to Postgres RLS |
| Multi-source joins | ✅ Yes (REST, GraphQL, DB) | ❌ Postgres only |
| Managed cloud | ✅ Hasura Cloud | ❌ Self-hosted only |
| Custom business logic | Action handlers (HTTP) | Postgres functions / plugins |
| Plugin ecosystem | Remote schemas | makeExtendSchemaPlugin, community plugins |
| Postgres RLS support | Partial (own permission model) | ✅ Full native RLS |
| N+1 query protection | ✅ DataLoader built-in | ✅ Look-ahead optimization |
| License | Apache 2.0 + commercial | MIT (v4) / Grafserv (v5) |
Performance Considerations for AI Workloads
AI applications have unusual query patterns compared to typical SaaS:
- Bulk vector reads — Fetching embedding neighbors for RAG pipelines
- High write throughput — Logging inference requests, tool calls, agent traces
- Mixed latency requirements — User-facing queries need <100ms; background batch jobs tolerate seconds
- Schema fluidity — Prompt templates, tool definitions, and eval datasets evolve rapidly
PostGraphile has a measurable edge in raw PostgreSQL throughput. Because it generates highly optimized SQL that leverages look-ahead and PostgreSQL's query planner directly, complex nested queries often outperform Hasura's equivalent. For RAG pipelines that combine pgvector similarity search with relational joins, PostGraphile's native SQL generation is a genuine advantage.
Hasura trades some raw performance for operational simplicity. Its DataLoader implementation handles N+1 queries well, and for most AI applications the difference is imperceptible. Where Hasura shines is in heterogeneous data access — if your AI agent needs to join Postgres data with a REST API response or a remote GraphQL service in a single query, Hasura's Remote Schemas make that straightforward.
Authorization Models
This is where the tools diverge most meaningfully.
Hasura ships its own permission layer. You define row-level and column-level rules in the Hasura console using session variables (typically from a JWT). This is fast to configure and works well for standard multi-tenant patterns (user can only see their own data).
The limitation: Hasura's permission model runs in the application layer, not the database. If you access Postgres directly (for migrations, analytics, or batch jobs), those permissions don't apply.
PostGraphile delegates authorization entirely to PostgreSQL's Row Level Security. Your security rules live in the database, enforced for every connection regardless of how data is accessed. This is the correct architecture for regulated industries and any AI application that touches sensitive user data — medical records, financial data, PII.
For AI products where you're storing user conversations, agent memory, and LLM traces, database-enforced RLS is worth the extra setup cost.
Customization and Extensibility
AI backends need custom logic that doesn't fit neatly into CRUD: webhook triggers when an agent completes a task, computed fields that derive values from LLM outputs, batch mutations for importing eval datasets.
Hasura handles custom logic through:
- Actions — HTTP handlers that extend the schema with custom mutations/queries
- Event triggers — Async webhooks fired on database changes
- Remote schemas — Stitching in external GraphQL APIs
This is flexible but means your custom logic lives outside the database, in separate services you have to deploy and maintain.
PostGraphile handles custom logic through:
- Postgres functions — Exposed directly as GraphQL mutations or queries; business logic runs inside the database
makeExtendSchemaPlugin— Add custom types and resolvers in Node.js- Custom plugins — Deep hooks into schema generation
Postgres functions are particularly powerful for AI workloads. You can write a function that retrieves the nearest embedding vectors, applies a business rule filter, and returns formatted results — all in a single SQL call, exposed as a clean GraphQL mutation.
Developer Experience
Hasura wins on onboarding speed. The console is genuinely good — you can have a working GraphQL API over an existing Postgres database in under 10 minutes. For teams prototyping an AI MVP, that speed matters.
PostGraphile has a steeper initial learning curve, especially v5 (Grafserv-based). But teams with strong PostgreSQL backgrounds often find it more natural — it feels like an extension of the database rather than a layer on top of it.
Hosting and Operations
| Aspect | Hasura | PostGraphile | |--------|--------|--------------| | Self-hosted | Docker / Helm chart | Node.js process | | Managed | Hasura Cloud (paid) | None (DIY) | | Observability | Built-in in Cloud; manual OSS | Manual (OpenTelemetry plugins) | | Multi-region | Hasura Cloud feature | Custom infrastructure | | Cold start | Minimal | Minimal |
For early-stage AI startups, Hasura Cloud reduces operational overhead significantly. For teams already running Kubernetes with solid Postgres infrastructure, PostGraphile's self-hosted model is a natural fit.
When to Use Hasura for AI Applications
- You need to join Postgres data with external REST APIs or GraphQL services in the same query
- Your team prefers a GUI and wants to move fast without writing boilerplate
- You're building a multi-tenant AI SaaS with standard auth patterns
- You want managed infrastructure with observability out of the box
When to Use PostGraphile for AI Applications
- You need maximum PostgreSQL performance for vector search and complex joins
- You're in a regulated industry and need database-enforced RLS
- You want full ownership of your infrastructure with no vendor dependencies
- Your team is PostgreSQL-native and comfortable with stored procedures
The Verdict for AI API Layers
For most early-stage AI products, Hasura provides the faster path to a working API. Its multi-source capabilities are underrated — being able to federate Postgres, your LLM provider's API, and a vector store into one GraphQL schema is powerful.
For production AI systems handling sensitive data or requiring maximum PostgreSQL throughput, PostGraphile is the stronger architectural choice. Its database-centric model ages well as complexity grows.
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