Simulate LLM chains, RAG architectures, and multi-agent workflows. Know your token spend, latency distributions, and configuration tradeoffs — before a single line ships.
Trusted by infrastructure teams at








Capabilities
Five core engines designed for precision. Gain complete visibility into cost, latency, and structural behavior — before shipping to production.
A drag-and-drop builder strictly mapped to 9 deterministic AI node primitives. Design RAG, multi-agent workflows, and LLM chains without production access.
Project true spend using mathematically precise calculations. Real-time pricing is updated daily via our Benchmark Engine scrapers.
Identify bottlenecks before users do. Models per-stage latency using Monte Carlo simulations and empirical benchmark distributions.
Receive domain-aware guidance backed by research and benchmarks. We quantify the exact cost and latency tradeoffs of your parameter choices.
Run up to 5 pipeline variants side-by-side to find the optimal balance of speed, cost, and structural efficiency.
The problem
Every engineering team building AI products faces the same blind spots. No one sees them until the damage is done.
$18K/mo
Avg unexpected AI spend at Series B
Teams discover true token costs when the OpenAI invoice arrives. A single prompt template change can 10x costs invisibly.
14s P95
Discovered in production, not before
RAG pipelines have 5–8 distinct latency stages. Teams rarely know which stage is the bottleneck until users complain.
22%
Quality drop noticed 3 weeks later
Chunk size, embedding model, and retrieval k all affect system behavior. These tradeoffs are explored by accident, not by design.
100%
Of AI teams have had a cost or latency incident
There is no staging environment for AI pipeline behavior. Teams ship pipelines and learn from production failures.
"We reduced OpenAI spend by 67% after discovering that 40% of our chain calls were redundant — found during a post-incident retrospective, not pre-deployment."
— AI Infrastructure Engineer, Series B startup
"A fintech company switched embedding models for cost reasons. Retrieval quality dropped 22%. This was only caught three weeks later by a customer complaint."
— ML Platform Lead, Enterprise tech company
Social proof
We reduced OpenAI spend by 67% after discovering 40% of our chain calls were redundant. PRISM found it in 3 minutes.
Alex K.
Staff AI Engineer
Series B Startup
P95 was 14 seconds. PRISM found the re-ranker was the bottleneck before we even shipped. Would have been a production fire.
Priya M.
Head of ML Platform
Enterprise Tech
I used the free tier to simulate 3 traffic scenarios and exported a report for my co-founder. Setup took 8 minutes.
Marcus L.
Founding Engineer
AI Writing Assistant
Pricing
Free tier is generous enough to deliver real value. Pro unlocks team necessity. No feature gating on core simulation.
Free
Forever free
Pro
/user/month
Team
/month · up to 8 seats