From pipeline to production insight in four steps.

Build your pipeline

Open the visual canvas and drag pipeline stage nodes into place. Connect them to model your LLM chain, RAG architecture, multi-agent workflow, or any custom AI pipeline topology.

Import existing configurations from LangChain LCEL (JSON), LlamaIndex pipeline configs, or define your own using PRISM's YAML schema. No code required to get started.

Configure parameters

For each pipeline stage, set the model provider, expected input/output token counts, parallelism settings, and failure modes.

At the pipeline level, define traffic expectations (requests/day), token distributions (mean, P50, P95), cache hit rates, and retry policies. PRISM uses real-time provider pricing and empirical latency models.

Run simulations

Execute simulations across three core dimensions simultaneously: cost projection, latency profiling, and configuration intelligence.

Create up to 5 pipeline variants and run side-by-side comparisons. See how changing an embedding model, adjusting chunk sizes, or switching providers affects your entire pipeline's structural behavior and budget.

Ship with confidence

Export simulation reports as PDFs, share via link, or integrate via API. Use PRISM's CI/CD integration to make simulation reports a mandatory pre-deployment gate.

Teams using PRISM report 67% fewer cost surprises and 3x faster pipeline optimization cycles. Every simulation run is versioned and auditable.