About
PRISM exists because production AI pipelines are too expensive to debug in production. We give engineering teams the tools to simulate, measure, and optimize before they deploy.
Origin
We were an engineering team running multi-model AI pipelines across OpenAI, Anthropic, and Cohere. Our monthly inference bill hit $18K with no clear breakdown of where the money was going. Latency spikes were invisible until users complained. Retry storms from rate limits cascaded silently.
We built an internal tool to simulate our pipelines before deploying changes — synthetic traffic, behavioral latency models, and deterministic cost projections. Within a month, we cut our inference spend by 40% and caught three critical failure modes that would have hit production.
That internal tool became PRISM.
7
Model providers supported
40+
Teams in Private Beta
9
Strict Pipeline Primitives
<5s
P95 Simulation Execution
Beliefs
These are the principles that shape every product decision at PRISM.
Production is not a testing environment. Every AI pipeline should be simulated, stress-tested, and cost-modeled before a single real request is served.
AI infrastructure spend is opaque by design. We believe teams deserve granular, real-time visibility into what every model call, retry, and fallback actually costs.
PRISM provides statistical data, not opinions. We surface latency confidence intervals, cost breakdowns, and configuration tradeoffs — the engineering team decides what to optimize.
Our behavioral models are calibrated against empirical, published API benchmarks. We document our methodology and update calibrations daily via automated scrapers.
Timeline
PRISM started as an internal tool for a team running multi-model pipelines that burned $18K/mo in inference costs with zero visibility into the bottlenecks.
Built the core simulation runtime — deterministic token math, behavioral Monte Carlo latency models, and cost projection for OpenAI and Anthropic endpoints.
Opened to 40 teams. Pipeline definition format stabilized around 9 core primitives. Added caching simulation, retry modeling, and the first iteration of the canvas.
Transitioned from dynamic accuracy scoring to static Configuration Intelligence. Integrated the Public Benchmark Engine to act as the automated calibration moat.
Targeting general availability with support for 7 tier-one providers, CI/CD integration, WebSocket streaming, and team collaboration layers.
Join us
We are a small, focused team solving hard problems at the intersection of simulation, cost optimization, and developer tooling. If that sounds interesting, we'd like to hear from you.