5-Lens Empirical Quadrants, Pareto Frontier & 17-Facet AI Catalog
Replaces subjective analyst pay-to-play quadrants with reproducible telemetry across LLMs, CPU/Edge SLMs, 3D/Multimodal, Time-Series Foundation Models (Chronos-Bolt, TimesFM 2.0, Moirai-MoE, TimeGPT, PatchTST, Toto), Agent SDKs/Orchestrators, 7-Stage RAG, DSPy, Embeddings, MCP + A2A, Security, Tracing vs Evaluation, Memory Policies vs Vector/SQL Substrates, and Durable Execution.
All 17 Facets (13) agent_development_orchestration ai_security_and_guardrails automation_ipaas_dag_durable embeddings_and_rerankers evaluation_and_regression_testing llm_frontier_cloud llm_open_weights_cloud memory_behavior_policies multimodal_vision_audio_3d observability_tracing_monitoring programmatic_prompt_workflow_optimization protocols_mcp_and_a2a rag_context_pipeline slm_cpu_edge_quantized teleon_team_simulation_provenance timeseries_foundation_models vector_and_transactional_storage
5 Orthogonal Ranking Lenses
- Lens 1 (Empirical 2x2 Quadrant): X = Verified Execution Reliability vs Y = Architectural Composability and Governance Fit.
- Lens 2 (Constraint-Weighted Wave): Custom workload weights plus Tversky hard vetoes (
spdx_license, blast class, CPU-only). - Lens 3 (Exact Pareto Frontier): Non-dominated set across Quality, Latency, Cost, Cliff Retention, and Lock-in Freedom (orange nodes).
- Lens 4 (Jagged-Frontier Cliff Index): Perturbed neighbor retention ratio.
- Lens 5 (Team Marginal Value / WAR): Paired holdout lift net of coordination token tax.
| Component & Publisher | Primary Facet | Quadrant & Frontier | Lens Scores | Cliff & WAR | License |
|---|---|---|---|---|---|
| Temporal Temporal Technologies ( durable_workflow_engine) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER PARETO | Exec: 98, Fit: 95, Wave: 96.6 | Cliff: 95%, WAR: +0.35 | MIT |
| Restate Restate ( durable_workflow_engine) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 97, Fit: 95, Wave: 95.08 | Cliff: 95%, WAR: +0.31 | MIT |
| Dagster Dagster Labs ( data_pipeline_dag) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 94, Fit: 93, Wave: 93.96 | Cliff: 91%, WAR: +0.27 | Apache-2.0 |
| Inngest Durable Step Functions Inngest ( durable_workflow_engine) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 94, Fit: 94, Wave: 93.36 | Cliff: 93%, WAR: +0.26 | Apache-2.0 |
| Prefect 3 Prefect ( data_pipeline_dag) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 93, Fit: 93, Wave: 92.76 | Cliff: 91%, WAR: +0.24 | Apache-2.0 |
| Apache Airflow 3 Apache Software Foundation ( data_pipeline_dag) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 92, Fit: 92, Wave: 92.48 | Cliff: 89%, WAR: +0.22 | Apache-2.0 |
| Kestra Kestra ( data_pipeline_dag) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 92, Fit: 93, Wave: 92.2 | Cliff: 91%, WAR: +0.23 | Apache-2.0 |
| Pipedream Pipedream ( app_integration_ipaas) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 91, Fit: 92, Wave: 90.92 | Cliff: 87%, WAR: +0.21 | MIT |
| Cloudflare Workflows Cloudflare ( durable_workflow_engine) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 96, Fit: 94, Wave: 88.2 | Cliff: 93%, WAR: +0.28 | Proprietary |
| n8n n8n ( app_integration_ipaas) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 89, Fit: 82, Wave: 87.94 | Cliff: 84%, WAR: +0.22 | Sustainable-Use |
| Make Celonis / Make ( app_integration_ipaas) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 90, Fit: 83, Wave: 84.86 | Cliff: 86%, WAR: +0.19 | Proprietary |
| Zapier Zapier ( app_integration_ipaas) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 90, Fit: 83, Wave: 84.78 | Cliff: 86%, WAR: +0.18 | Proprietary |
| Microsoft Power Automate Microsoft ( app_integration_ipaas) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 89, Fit: 82, Wave: 83.9 | Cliff: 84%, WAR: +0.17 | Proprietary |