Compute Resources
Understanding compute requirements for AI workflows and agents.
Compute Requirements
Most workflows on Neura Market run on standard hardware — a modern laptop or a small cloud VM is sufficient. The main compute costs come from API calls to LLM providers, not local processing.
Typical requirements:
- Simple workflows: Any machine with Node.js or Python. API-bound, not compute-bound.
- Data processing workflows: 4GB+ RAM for large datasets. SSD storage recommended.
- Multi-agent systems: 8GB+ RAM. Multiple concurrent API connections.
- Local LLM workflows: GPU required (NVIDIA recommended). 16GB+ VRAM for 7B+ parameter models.
Cloud Compute Options
For production deployments:
- Serverless (Vercel, AWS Lambda): Best for event-driven workflows. Auto-scales to zero. Pay per execution.
- VPS (DigitalOcean, Hetzner, Linode): Best for always-on agents. Fixed monthly cost. Full control.
- GPU Cloud (RunPod, Vast.ai, Lambda): Best for local LLM inference. Rent GPUs by the hour.
- Container platforms (Railway, Fly.io): Best for Docker-based deployments. Easy scaling.
Cost Optimization
- Use caching to reduce repeated API calls (many workflows include built-in caching).
- Choose the right model size — smaller models are cheaper and often sufficient for focused tasks.
- Use batch APIs for high-volume processing (Anthropic and OpenAI offer 50% discounts for batch requests).
- Set spending limits with your LLM provider.
- Monitor usage with tools like Helicone or LangSmith to identify optimization opportunities.