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Blazing Flow
Build resilient async pipelines with automatic recovery and dynamic optimization
Serverless, Persistent async pipelines with the power that Lambda can't provide.
Full Python. Full Reusability. Full Security. Full Scalability. Full Sovereignty.
Write real Python with if statements and loops. Pool connections across thousands of tasks. Run untrusted code safely. Scale infinitely. Control your infrastructure completely.
Get Started
- Quick Start 🚀 - Build your first async pipeline in 5 minutes
- Guide 📚 - Complete guide with examples
Serverless, Persistent: The Full Stack
Lambda gives you serverless. Blazing Flow gives you serverless and persistence. The workflow/step/service architecture unlocks capabilities that traditional serverless cannot provide:
⚡ Full Python
Lambda/Cloud Run: Rigid DAGs, Step Functions, JSON state machines. No if statements, no loops, no real programming.
Blazing Flow: Write real Python. If statements, loops, dynamic branching. Unlimited complexity.
🔄 Full Reusability
Lambda/Cloud Run: Cold starts every time. Connections rebuilt. State lost. 50-200ms overhead per invocation.
Blazing Flow: Connections pooled. State cached. Warmup once, reuse forever. 5ms per task.
🔒 Full Security
Lambda/Cloud Run: All code has full access. User code can steal credentials, access databases, leak secrets.
Blazing Flow: Sandboxed steps run isolated. Service calls automatically execute in trusted environment. Credentials never leak - architecture prevents it.
Architecture: Sandboxed code → Service call → Trusted execution → Safe result
📈 Full Scalability
Lambda/Cloud Run: Limited concurrency, timeout limits, regional restrictions.
Blazing Flow: Unlimited workers, no timeouts, scale to millions of tasks.
🏛️ Full Sovereignty
Lambda/Cloud Run: Locked to AWS/GCP. No control over where code runs.
Blazing Flow: Deploy on GCP, Akash Network, or hybrid. Your infrastructure, your control, your data residency.
⚡ The Comparison
| AWS Lambda | Cloud Run | Blazing Flow | |
|---|---|---|---|
| Python | ❌ DAGs only | ❌ Step Functions | ✅ Full Python |
| Reusability | ❌ Cold starts | ❌ Per request | ✅ Pool & Cache |
| Security | ❌ Full access | ❌ Full access | ✅ Sandboxed |
| Scalability | ⚠️ Limited | ⚠️ Limited | ✅ Unlimited |
| Sovereignty | ❌ AWS only | ❌ GCP only | ✅ Multi-cloud |
When to Use Blazing Flow
✅ Complex multi-stage workflows - Pipelines with branching logic and multiple steps
✅ Stateful computations - Automatic checkpointing and partial recovery
✅ Mixed workloads - Combination of I/O-bound and CPU-bound tasks
✅ Long-running pipelines - Workflows that run for hours or days with automatic recovery
✅ Dynamic routing - Pipeline structure that changes based on data
Quick Example
Key Features
📝 Declarative Pipelines
Define workflows using simple decorators - @workflow for pipelines, @step for steps.
🔄 Automatic Recovery
Pipelines resume automatically after interruptions. No lost work, no manual intervention.
⚡ Smart Workers
System automatically balances async and blocking workers based on your workload patterns.
🔌 Persistent Connections
Keep database and SSH connections alive across tasks for better performance.
📊 Built-in Monitoring
Real-time dashboard shows queue depths, worker states, and throughput metrics.
Learn More
- Quick Start Guide - Build your first pipeline in 5 minutes
- Complete Guide - In-depth examples and patterns