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 |
|---|---|---|---|---|---|
| Amazon Chronos-2 Covariate AWS / Amazon Science ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER PARETO | Exec: 96, Fit: 93, Wave: 94.08 | Cliff: 89%, WAR: +0.3 | Apache-2.0 |
| Amazon Chronos-Bolt (Base) AWS / Amazon Science ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER PARETO | Exec: 97, Fit: 94, Wave: 94 | Cliff: 89%, WAR: +0.31 | Apache-2.0 |
| Nixtla StatsForecast (AutoARIMA/AutoETS) Nixtla ( rag_component) |
timeseries_foundation_models |
EMPIRICAL_LEADER PARETO | Exec: 99, Fit: 95, Wave: 93.32 | Cliff: 95%, WAR: +0.26 | Apache-2.0 |
| Google TimesFM 2.0 (500M) Google Research ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 95, Fit: 92, Wave: 93.3 | Cliff: 86%, WAR: +0.29 | Apache-2.0 |
| Datadog Toto (Open Weights) Datadog ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 94, Fit: 93, Wave: 92.96 | Cliff: 89%, WAR: +0.27 | Apache-2.0 |
| Amazon Chronos-Bolt (Tiny CPU) AWS / Amazon Science ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER PARETO | Exec: 98, Fit: 94, Wave: 92.32 | Cliff: 89%, WAR: +0.27 | Apache-2.0 |
| Salesforce Moirai-MoE Salesforce AI Research ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 92, Fit: 91, Wave: 91.62 | Cliff: 86%, WAR: +0.25 | Apache-2.0 |
| PatchTST IBM / Princeton ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 96, Fit: 93, Wave: 90.84 | Cliff: 87%, WAR: +0.2 | Apache-2.0 |
| MOMENT-1-Large CMU AutonLab ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 92, Fit: 91, Wave: 90.66 | Cliff: 86%, WAR: +0.22 | MIT |
| Sundial Time-Series Flow THUML ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 89, Fit: 90, Wave: 90.22 | Cliff: 84%, WAR: +0.23 | Apache-2.0 |
| Timer-XL THUML ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 90, Fit: 90, Wave: 90.14 | Cliff: 84%, WAR: +0.21 | Apache-2.0 |
| Lag-Llama Morgan Stanley / ServiceNow ( model_family_version) |
timeseries_foundation_models |
OPERATIONAL_WORKHORSE | Exec: 88, Fit: 81, Wave: 87.02 | Cliff: 80%, WAR: +0.16 | Apache-2.0 |
| Nixtla TimeGPT-1 Nixtla ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 91, Fit: 83, Wave: 85.11 | Cliff: 86%, WAR: +0.21 | Proprietary |