Spend Control for LlamaIndex
LlamaIndex agents live at the intersection of data and inference: RAG pipelines, data connectors, and tool-calling agents. Every connector and every paid tool is a spend point.
Why LlamaIndex agents overspend
Data connectors hit paid APIs on every sync — embeddings, enrichment, and lookup costs add up.
RAG pipelines call LLM providers per query; a spike in traffic is a spike in spend.
Tool-calling agents can trigger payments without a budget check.
How it works
Attach sipi.bot as a tool. Before any spend — data sync, paid connector, payment — the agent calls the guard and gets APPROVED, BLOCKED, or FLAGGED.
Rules that fit LlamaIndex workloads
Daily ceiling on data-connector spend.
Category rule: embeddings vs inference vs data purchases.
Merchant allowlist for approved data vendors.
Guard call
from sipi_guard import sipi_guard
# Before a data sync or paid tool call:
decision = sipi_guard(amount=50, merchant="enrichment-api.com", category="data")
# APPROVED | BLOCKED | FLAGGEDDeterministic, ~5 ms, fully logged.
FAQ
Can sipi.bot distinguish embeddings from inference spend?
Yes — category rules let you cap embeddings, inference, and data purchases separately.
Does it work with LlamaCloud?
sipi.bot is an API/MCP guard — it sits in front of any paid call your LlamaIndex agents make, including managed services.
What happens when a sync is blocked?
The agent receives BLOCKED with the reason and the audit log records the attempt. Nothing settles.
Related
Stop the next $12,400 night.
One API call (or MCP tool) in front of every agent transaction — APPROVED, BLOCKED, or FLAGGED, deterministic, ~5 ms, fully logged.
See plans — from $99/mo Try a live check