The task queue and workflow engine market has options at every complexity level, and Hatchet versus Temporal represents a clean comparison between two different architectural philosophies. Hatchet says PostgreSQL is enough for durable task execution — no Cassandra, no Elasticsearch, no complex cluster topology. Temporal says distributed workflows at scale require purpose-built infrastructure. Both deliver durable execution guarantees, but through fundamentally different architectures.
Hatchet's PostgreSQL foundation is its defining architectural choice. By using PostgreSQL for both task state and queue management, Hatchet eliminates the operational complexity of running Redis, Cassandra, or other specialized databases alongside the workflow engine. If your application already uses PostgreSQL (as most web applications do), adding Hatchet means no new database infrastructure. ACID guarantees from PostgreSQL provide natural durability for task state.
Temporal's distributed architecture provides capabilities that a single-database approach cannot match. Sharded task queues across a cluster enable millions of concurrent workflows with sub-second scheduling. Multi-datacenter replication ensures availability even during region-level failures. The separation of workflow history, visibility storage, and matching services enables independent scaling of each component based on workload characteristics.
Developer experience shows Hatchet's modern approach. Hatchet workflows are defined as code using TypeScript or Python SDKs with step-based composition — each step has its own retry policy, timeout, and concurrency limit. The web dashboard provides real-time queue depths, worker health, step-level traces, and error rates. The getting-started path goes from npm install to running workflows in under 10 minutes. Temporal's learning curve is steeper due to its replay-safe programming model requirements.
AI workload suitability is where Hatchet positions itself. The platform is popular for RAG pipeline orchestration, multi-step LLM workflows, and GPU task scheduling. Fan-out patterns, rate limiting per API provider, and durable state for long-running agent loops are common use cases. Temporal handles these patterns through generic abstractions but without AI-specific tooling or optimizations. Hatchet's design decisions reflect the specific needs of AI application backends.
Self-hosting complexity differs. Hatchet self-hosts with Docker Compose (PostgreSQL + Hatchet server + dashboard) — a single docker-compose up starts everything. Temporal self-hosts require the Temporal Server, Cassandra or MySQL, optionally Elasticsearch, and typically Kubernetes for production. Hatchet's deployment simplicity is a direct consequence of its PostgreSQL-only architecture.
Scale characteristics create a natural boundary. Hatchet handles the workloads typical of web applications, SaaS backends, and AI pipelines — thousands to hundreds of thousands of concurrent tasks. Temporal handles workloads from the same range up to millions of concurrent workflows with multi-region redundancy. For most applications, Hatchet's scale ceiling is well above what they need. For massive-scale systems, Temporal's distributed architecture becomes necessary.