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 (188) 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 |
|---|---|---|---|---|---|
| TigerBeetle TigerBeetle ( storage_vector_backend) |
vector_and_transactional_storage |
EMPIRICAL_LEADER PARETO | Exec: 99, Fit: 96, Wave: 96.74 | Cliff: 98%, WAR: +0.33 | Apache-2.0 |
| Bytecode Alliance Wasmtime (Component Model) Bytecode Alliance ( inference_runtime) |
teleon_team_simulation_provenance |
EMPIRICAL_LEADER PARETO | Exec: 99, Fit: 96, Wave: 96.7 | Cliff: 96%, WAR: +0.33 | Apache-2.0 |
| 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 |
| SLSA Build Provenance v1.1 OpenSSF ( protocol_spec) |
teleon_team_simulation_provenance |
EMPIRICAL_LEADER | Exec: 98, Fit: 96, Wave: 96.46 | Cliff: 96%, WAR: +0.29 | Apache-2.0 |
| PostgreSQL + pgvector / pgvectorscale PostgreSQL / Timescale ( storage_vector_backend) |
vector_and_transactional_storage |
EMPIRICAL_LEADER | Exec: 97, Fit: 95, Wave: 96.04 | Cliff: 95%, WAR: +0.32 | PostgreSQL |
| DuckDB + VSS / Parquet DuckDB Labs ( storage_vector_backend) |
vector_and_transactional_storage |
EMPIRICAL_LEADER | Exec: 98, Fit: 95, Wave: 95.96 | Cliff: 95%, WAR: +0.32 | MIT |
| Model Context Protocol (MCP 2025-06-18) Model Context Protocol ( protocol_spec) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 96, Fit: 94, Wave: 95.76 | Cliff: 93%, WAR: +0.34 | MIT |
| SPIFFE / SPIRE Workload Identity CNCF ( protocol_spec) |
teleon_team_simulation_provenance |
EMPIRICAL_LEADER | Exec: 97, Fit: 95, Wave: 95.72 | Cliff: 95%, WAR: +0.29 | Apache-2.0 |
| Software Heritage SWHID v1 Software Heritage / ISO 18670 ( protocol_spec) |
teleon_team_simulation_provenance |
EMPIRICAL_LEADER PARETO | Exec: 99, Fit: 96, Wave: 95.6 | Cliff: 99%, WAR: +0.34 | GPL-3.0 |
| Qdrant Qdrant ( storage_vector_backend) |
vector_and_transactional_storage |
EMPIRICAL_LEADER | Exec: 96, Fit: 95, Wave: 95.48 | Cliff: 95%, WAR: +0.29 | Apache-2.0 |
| UK AISI Inspect UK AI Security Institute ( evaluation_harness) |
evaluation_and_regression_testing |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 95.2 | Cliff: 93%, WAR: +0.31 | MIT |
| LanceDB (Columnar Lance Format) LanceDB ( storage_vector_backend) |
vector_and_transactional_storage |
EMPIRICAL_LEADER | Exec: 96, Fit: 95, Wave: 95.16 | Cliff: 95%, WAR: +0.3 | Apache-2.0 |
| Restate Restate ( durable_workflow_engine) |
automation_ipaas_dag_durable |
EMPIRICAL_LEADER | Exec: 97, Fit: 95, Wave: 95.08 | Cliff: 95%, WAR: +0.31 | MIT |
| Traceloop OpenLLMetry Traceloop ( tracing_gateway) |
observability_tracing_monitoring |
EMPIRICAL_LEADER | Exec: 97, Fit: 95, Wave: 95.08 | Cliff: 95%, WAR: +0.28 | Apache-2.0 |
| Official MCP TypeScript & Python SDK Model Context Protocol ( protocol_sdk) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 94.88 | Cliff: 93%, WAR: +0.29 | MIT |
| EleutherAI lm-evaluation-harness EleutherAI ( evaluation_harness) |
evaluation_and_regression_testing |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 94.88 | Cliff: 93%, WAR: +0.28 | MIT |
| Envoy AI & MCP Gateway CNCF / Envoy ( mcp_server) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 96, Fit: 95, Wave: 94.84 | Cliff: 95%, WAR: +0.28 | Apache-2.0 |
| Langfuse Langfuse ( tracing_gateway) |
observability_tracing_monitoring |
EMPIRICAL_LEADER | Exec: 96, Fit: 94, Wave: 94.8 | Cliff: 93%, WAR: +0.27 | MIT |
| Promptfoo Promptfoo ( evaluation_harness) |
evaluation_and_regression_testing |
EMPIRICAL_LEADER | Exec: 96, Fit: 94, Wave: 94.76 | Cliff: 91%, WAR: +0.3 | MIT |
| Sentence Transformers UKPLab / Hugging Face ( embedding_library) |
embeddings_and_rerankers |
EMPIRICAL_LEADER | Exec: 96, Fit: 94, Wave: 94.48 | Cliff: 93%, WAR: +0.27 | Apache-2.0 |
| C2PA Content Credentials C2PA Coalition ( protocol_spec) |
teleon_team_simulation_provenance |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 94.28 | Cliff: 95%, WAR: +0.24 | Apache-2.0 |
| E2B Firecracker Code Sandboxes E2B ( team_simulation_substrate) |
teleon_team_simulation_provenance |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 94.24 | Cliff: 93%, WAR: +0.28 | Apache-2.0 |
| BAAI BGE-M3 BAAI ( embedding_model) |
embeddings_and_rerankers |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 94.2 | Cliff: 91%, WAR: +0.29 | MIT |
| Amazon Chronos-2 Covariate AWS / Amazon Science ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 96, Fit: 93, Wave: 94.08 | Cliff: 89%, WAR: +0.3 | Apache-2.0 |
| bm25s (Scipy Sparse Lexical) xhluca ( rag_component) |
rag_context_pipeline |
EMPIRICAL_LEADER | Exec: 98, Fit: 95, Wave: 94.04 | Cliff: 95%, WAR: +0.26 | MIT |
| Amazon Chronos-Bolt (Base) AWS / Amazon Science ( model_family_version) |
timeseries_foundation_models |
EMPIRICAL_LEADER | Exec: 97, Fit: 94, Wave: 94 | Cliff: 89%, WAR: +0.31 | Apache-2.0 |
| Cloudflare MCP Server Cloudflare ( mcp_server) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 94, Fit: 93, Wave: 93.96 | Cliff: 91%, WAR: +0.27 | Apache-2.0 |
| 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 |
| DSPy Stanford NLP ( programmatic_optimizer) |
programmatic_prompt_workflow_optimization |
EMPIRICAL_LEADER | Exec: 94, Fit: 93, Wave: 93.92 | Cliff: 89%, WAR: +0.32 | MIT |
| Nomic Embed Text v2 MoE Nomic AI ( embedding_model) |
embeddings_and_rerankers |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 93.92 | Cliff: 93%, WAR: +0.26 | Apache-2.0 |
| Snowflake Arctic Embed 2.0 Snowflake ( embedding_model) |
embeddings_and_rerankers |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 93.92 | Cliff: 93%, WAR: +0.26 | Apache-2.0 |
| sqlite-vec Alex Garcia ( storage_vector_backend) |
vector_and_transactional_storage |
EMPIRICAL_LEADER | Exec: 98, Fit: 96, Wave: 93.9 | Cliff: 96%, WAR: +0.27 | MIT |
| SGLang RadixAttention Runtime LMSYS ( inference_runtime) |
llm_open_weights_cloud |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 93.88 | Cliff: 91%, WAR: +0.27 | Apache-2.0 |
| llama.cpp (GGML / AVX-512 / NEON) ggml-org ( inference_runtime) |
slm_cpu_edge_quantized |
EMPIRICAL_LEADER | Exec: 96, Fit: 94, Wave: 93.84 | Cliff: 93%, WAR: +0.26 | MIT |
| Meta SAM 2.1 Meta ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 95, Fit: 93, Wave: 93.84 | Cliff: 89%, WAR: +0.27 | Apache-2.0 |
| Microsoft Presidio Microsoft ( security_guardrail) |
ai_security_and_guardrails |
EMPIRICAL_LEADER | Exec: 96, Fit: 94, Wave: 93.8 | Cliff: 91%, WAR: +0.26 | MIT |
| faster-whisper (CTranslate2 INT8) SYSTRAN ( inference_runtime) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 96, Fit: 94, Wave: 93.8 | Cliff: 91%, WAR: +0.26 | MIT |
| MCP Inspector & Conformance CLI Model Context Protocol ( protocol_sdk) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 93.6 | Cliff: 93%, WAR: +0.24 | MIT |
| 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 |
| SQLite / D1 Local Verify MCP MCP Contributors ( mcp_server) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 96, Fit: 94, Wave: 93.52 | Cliff: 93%, WAR: +0.24 | MIT |
| Weaviate Weaviate ( storage_vector_backend) |
vector_and_transactional_storage |
EMPIRICAL_LEADER | Exec: 93, Fit: 93, Wave: 93.44 | Cliff: 93%, WAR: +0.24 | BSD-3-Clause |
| Hugging Face Hub & TGI Hugging Face ( model_repository) |
llm_open_weights_cloud |
EMPIRICAL_LEADER | Exec: 92, Fit: 92, Wave: 93.43 | Cliff: 89%, WAR: +0.2 | Apache-2.0 |
| Whisper Large v3 Turbo OpenAI ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 96, Fit: 93, Wave: 93.4 | Cliff: 87%, WAR: +0.26 | MIT |
| 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 |
| FastMCP Prefect / jlowin ( protocol_sdk) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 94, Fit: 93, Wave: 93.32 | Cliff: 91%, WAR: +0.25 | Apache-2.0 |
| LangGraph Checkpointer & Cross-Thread Store LangChain ( memory_policy_layer) |
memory_behavior_policies |
EMPIRICAL_LEADER | Exec: 94, Fit: 93, Wave: 93.32 | Cliff: 91%, WAR: +0.25 | MIT |
| Nixtla StatsForecast (AutoARIMA/AutoETS) Nixtla ( rag_component) |
timeseries_foundation_models |
EMPIRICAL_LEADER | 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 |
| IBM Docling IBM ( rag_component) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 93.24 | Cliff: 91%, WAR: +0.25 | MIT |
| Chonkie Semantic & Token Chunker Chonkie ( rag_component) |
rag_context_pipeline |
EMPIRICAL_LEADER | Exec: 97, Fit: 95, Wave: 93.16 | Cliff: 95%, WAR: +0.23 | MIT |
| PydanticAI Pydantic ( agent_sdk) |
agent_development_orchestration |
EMPIRICAL_LEADER | Exec: 96, Fit: 93, Wave: 93.12 | Cliff: 89%, WAR: +0.28 | MIT |
| Daytona Isolated Dev Environments Daytona ( team_simulation_substrate) |
teleon_team_simulation_provenance |
EMPIRICAL_LEADER | Exec: 93, Fit: 93, Wave: 93.12 | Cliff: 93%, WAR: +0.25 | Apache-2.0 |
| Kokoro-82M TTS hexgrad ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 97, Fit: 94, Wave: 93.08 | Cliff: 91%, WAR: +0.25 | Apache-2.0 |
| GitHub MCP Server GitHub ( mcp_server) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 93, Fit: 93, Wave: 93.04 | Cliff: 89%, WAR: +0.26 | MIT |
| Filesystem MCP Server Model Context Protocol ( mcp_server) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 94, Fit: 93, Wave: 93 | Cliff: 91%, WAR: +0.24 | MIT |
| Protect AI (LLM Guard & ModelScan) Protect AI ( security_guardrail) |
ai_security_and_guardrails |
EMPIRICAL_LEADER | Exec: 94, Fit: 93, Wave: 93 | Cliff: 91%, WAR: +0.24 | MIT |
| Playwright MCP Server Microsoft ( mcp_server) |
protocols_mcp_and_a2a |
EMPIRICAL_LEADER | Exec: 93, Fit: 92, Wave: 93 | Cliff: 87%, WAR: +0.28 | 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 |
| Helicone Helicone ( tracing_gateway) |
observability_tracing_monitoring |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 92.96 | Cliff: 93%, WAR: +0.23 | Apache-2.0 |
| Milvus Zilliz / LF AI ( storage_vector_backend) |
vector_and_transactional_storage |
EMPIRICAL_LEADER | Exec: 91, Fit: 93, Wave: 92.92 | Cliff: 91%, WAR: +0.23 | Apache-2.0 |