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 (12) 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 |
|---|---|---|---|---|---|
| SGLang RadixAttention Runtime LMSYS ( inference_runtime) |
llm_open_weights_cloud |
EMPIRICAL_LEADER PARETO | Exec: 95, Fit: 94, Wave: 93.88 | Cliff: 91%, WAR: +0.27 | Apache-2.0 |
| vLLM PagedAttention Engine vLLM Project ( inference_runtime) |
llm_open_weights_cloud |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 93.56 | Cliff: 91%, WAR: +0.25 | Apache-2.0 |
| Hugging Face Hub & TGI Hugging Face ( model_repository) |
llm_open_weights_cloud |
EMPIRICAL_LEADER PARETO | Exec: 92, Fit: 92, Wave: 93.43 | Cliff: 89%, WAR: +0.2 | Apache-2.0 |
| Hugging Face Text Generation Inference Hugging Face ( inference_runtime) |
llm_open_weights_cloud |
EMPIRICAL_LEADER | Exec: 93, Fit: 93, Wave: 92.44 | Cliff: 91%, WAR: +0.21 | Apache-2.0 |
| Ollama Cloud Inference Service Ollama ( inference_runtime) |
llm_open_weights_cloud |
EMPIRICAL_LEADER PARETO | Exec: 94, Fit: 94, Wave: 92.08 | Cliff: 93%, WAR: +0.22 | MIT |
| Muse-Spark-Open (Ollama Cloud) Meta / Ollama Cloud ( model_family_version) |
llm_open_weights_cloud |
EMPIRICAL_LEADER | Exec: 93, Fit: 92, Wave: 90.9 | Cliff: 86%, WAR: +0.24 | Apache-2.0 |
| GLM-5.3-Flash (Ollama Cloud) Zhipu / Ollama Cloud ( model_family_version) |
llm_open_weights_cloud |
EMPIRICAL_LEADER | Exec: 94, Fit: 92, Wave: 90.5 | Cliff: 86%, WAR: +0.23 | Apache-2.0 |
| Qwen3-235B-A22B Alibaba Qwen ( model_family_version) |
llm_open_weights_cloud |
OPERATIONAL_WORKHORSE | Exec: 89, Fit: 81, Wave: 89.83 | Cliff: 80%, WAR: +0.21 | Apache-2.0 |
| DeepSeek-R1 DeepSeek ( model_family_version) |
llm_open_weights_cloud |
OPERATIONAL_WORKHORSE | Exec: 86, Fit: 80, Wave: 88.96 | Cliff: 78%, WAR: +0.22 | MIT |
| Mistral Codestral 25.01 Mistral AI ( model_family_version) |
llm_open_weights_cloud |
EMPIRICAL_LEADER | Exec: 92, Fit: 91, Wave: 88.56 | Cliff: 86%, WAR: +0.22 | Mistral-MNPL |
| Meta Llama 4 Maverick Meta ( model_family_version) |
llm_open_weights_cloud |
EMPIRICAL_LEADER | Exec: 90, Fit: 82, Wave: 87.44 | Cliff: 80%, WAR: +0.21 | Llama-4 |
| Meta Llama 3.3 70B Instruct Meta ( model_family_version) |
llm_open_weights_cloud |
OPERATIONAL_WORKHORSE | Exec: 89, Fit: 81, Wave: 86.08 | Cliff: 80%, WAR: +0.16 | Llama-3.3 |