We're evaluating tools to build a no-code/low-code pipeline for customer sentiment and inventory restocking agents. The core need is to connect our Shopify data, support tickets, and warehouse APIs without a full dev team build-out.
Two top contenders seem to be **SuperAGI** and **Flowise**. From a FinOps perspective, I'm immediately thinking about the running cost structure and potential for waste. SuperAGI's cloud offering vs. Flowise's self-hosted model has major implications.
Has anyone run a comparison on the operational costs for a mid-scale setup? I'm trying to map out:
* **Infrastructure Burden:** Flowise would live on our existing Kubernetes cluster, so marginal cost is just the pod resource. SuperAGI's cloud cost is opaque per "agent run."
* **Hidden Consumption:** Agentic workflows can spiral in token/compute usage if not carefully gated. Which platform gives better real-time cost controls and budgeting alerts?
* **Scalability vs. Reserved Capacity:** If we go with SuperAGI, are there commitment options, or is it purely pay-per-use? For Flowise, we'd need to right-size the node groups.
I built a quick script to estimate token usage from our typical payloads, but the actual execution cost is hard to pin down without runtime data.
```python
# Rough token estimator for our product catalog queries
import tiktoken
encoder = tiktoken.encoding_for_model("gpt-4")
def estimate_cost(text_batch, price_per_1k=0.03):
tokens = len(encoder.encode(text_batch))
cost = (tokens / 1000) * price_per_1k
return tokens, cost
# Example batch ~ $0.45 per run
```
Looking for any real-world cost numbers or gotchas. Especially interested if anyone has implemented agent loops for retail—did you encounter unexpected billing spikes due to retries or recursive tool calls?
That's a really smart angle. I hadn't even thought about budgeting alerts as a feature, but you're right, an agent going into a loop could rack up a huge bill.
> I built a quick script to estimate token usage from our typical payloads
Could you share more about how you built that? I'm trying to do similar estimates for our own potential use case, and I'm not sure where to start with the payload data from our Shopify backend. Do you just take a sample of recent API calls and feed them into a tokenizer?