Everything you need to simulate AI pipelines.

Complete visibility into your pipeline's behavior before it touches production. No live API calls. No production access required.

Visual Pipeline Canvas

A drag-and-drop canvas where users build AI pipeline graphs. Each node represents a pipeline stage. Edges represent data flow between stages.

019 strict node primitives: Input, Embedding, Vector Store, LLM Call, Re-ranker, Output, Tool Call, Cache, Router
02Configure each node with model selection, expected input/output token counts, provider, and parallelism
03Pipelines can be saved, versioned, forked, and shared via link
04Import from LangChain LCEL definition (JSON), LlamaIndex pipeline config, or custom YAML schema

Given a configured pipeline and traffic parameters, PRISM computes projected costs using real-time pricing models for all major providers.

01Inputs: requests per day, token distribution (mean, P50, P95), cache hit rate, retry rate
02Outputs: cost per query, daily/monthly cost, cost breakdown by stage, cost at 10x/100x scale
03Pricing database updated daily via automated scrapers against provider pricing pages
04Support: OpenAI, Anthropic, Google (Gemini), Cohere, Mistral, AWS Bedrock, Azure OpenAI

Models end-to-end and per-stage latency using Monte Carlo simulations derived from empirical telemetry and public benchmarks.

01Outputs: P50, P95, and P99 latency projections with rigorous Confidence Intervals
02Waterfall diagram showing latency contribution of each stage
03Latency sensitivity analysis: "What happens if embedding latency increases 2x?"
04Models parallel vs. sequential execution paths

Receive domain-aware guidance backed by research and benchmarks. PRISM quantifies the exact cost and latency tradeoffs of your parameter choices.

01Optimal vs. Suboptimal configuration assessments
02Embedding model comparison matrix: cost vs. MTEB retrieval average vs. latency
03Research-backed citations for configuration changes
04No live API calls required to generate intelligence

Embedding Model	Input Cost / 1M	MTEB Retrieval Avg	Config Guidance
text-embedding-3-small OpenAI	$0.02	52.8	efficient
embed-english-v3.0 Cohere	$0.10	55	balanced
text-embedding-3-large OpenAI	$0.13	55.4	premium
voyage-3 Voyage AI	$0.12	67.1	efficient
voyage-3-large Voyage AI	$0.38	69.2	premium

* Derived from published MTEB Leaderboard benchmarks.

Users can define up to 5 pipeline variants and run a side-by-side simulation comparison.

01Compare: Cost, Latency bounds, Configuration efficiency, and Scalability at 10x/100x
02Visual diff of pipeline configurations
03Exportable comparison reports in PDF format
04Shareable comparison links for team review

Users define acceptable thresholds and PRISM flags configurations that violate them in real time.

01"This configuration will cost $8,200/month at your expected traffic (threshold: $5,000)"
02"P95 latency lower bound exceeds 4s — 3 stages are contributing above budget"
03Configurable thresholds for maximum cost and latency budgets

Export simulation results and integrate PRISM into your engineering workflow.

01Export simulation report as PDF or shareable link
02GitHub Action: run PRISM simulation as part of CI/CD, fail build if thresholds exceeded
03Slack notification when simulation report is ready
04REST API for programmatic pipeline submission and result retrieval

Roadmap

Live production monitoring mode via SDK

Anomaly detection on live pipelines

Team collaboration: comments, annotations, approvals

SOC 2 Type II certification