Real-time token cost attribution & runaway loop prevention
Autonomous agents can burn thousands of dollars in minutes if caught in infinite reasoning loops. Splyntra calculates per-span token costs in real time, tracks prompt caching savings, and triggers hard circuit breakers.
from splyntra.finops import enforce_budget, CircuitBreaker
# Prevent infinite recursive agent execution
@enforce_budget(
max_cost_usd=0.25, # Abort if run exceeds $0.25
max_reasoning_steps=12, # Abort if loop count > 12
on_breach=CircuitBreaker.ABORT_AND_NOTIFY
)
def run_autonomous_workflow(prompt):
return agent_executor.run(prompt)Engineered for high-throughput autonomous agents
Every capability is built into the OpenTelemetry streaming pipeline with sub-millisecond ingestion overhead.
Real-Time Span-Level Cost Attribution
Every LLM inference span calculates input, output, and cached token prices using our continuously updated model pricing catalog.
- ✓Support for OpenAI, Anthropic, Google Gemini, Groq, Mistral, and Bedrock
- ✓Custom self-hosted model cost configurations (vLLM / Ollama hardware costs)
- ✓Detailed breakdown of reasoning tokens vs generation tokens
Runaway Loop & Infinite Step Circuit Breakers
Protect your infrastructure from non-deterministic agent death spirals where models repeatedly call tools without progressing.
- ✓Hard step limits and maximum token count boundaries per run
- ✓Spend velocity anomaly detection (e.g. $10 spent in 60 seconds)
- ✓Automated fallback to cheaper models (e.g. GPT-4o -> GPT-4o-mini)
Prompt Caching & Token Optimization Insights
Analyze prompt structure to maximize Anthropic 90% cache read discounts and OpenAI prefix caching.
- ✓Cache efficiency ratio tracking across multi-turn sessions
- ✓Identifies cache-busting dynamic prompt prefixes
- ✓Estimated ROI savings calculated on executive dashboards
Frequently Asked Questions
How does Splyntra stay up to date with LLM price drops?▼
Can I set cost limits for different developer teams?▼
Related Platform Features & Guides
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