Where do you stand? › Route 3 · Vendors & stack
I know which systems I need — who supplies them?
Pick the components you have to put in place. For each one you get the build-vs-buy reading and the market layer that supplies it, with the same scored recommendations and confidence the full analysis uses. Nothing is stored; the selection lives in the URL.
Target market(s)European UnionUnited States (federal)change
Legally-driven components are flagged when their requiring regulation sits outside your selected markets.
density
Step 1 of 2 — pick your components1 selected
Model Drift & Accuracy Monitor
Step 2 of 2 — the vendor & stack view
1 of 1 selected components are covered by 1 market layer.
Named vendors are community-maintained, disputable examples — not an endorsement. The stable object is the market layer. Compare with the reference stack for your regulatory profile →
Build or buy, per component (1)
Model Drift & Accuracy Monitor buy (products exist)
A Agent Observability & Model Risk Management product can carry this; the buyer's duties stay with you.
Agent Observability & Model Risk Management — covers 1 of your components
Covers: Model Drift & Accuracy Monitor. This layer supplies 10 components in the graph.
Confidence: moderate (65/100)
Community-maintained examples
LangSmith · Langfuse · Arize AI / Phoenix · Helicone · MLflow · Ragas · Deepchecks · Fairlearn · Fiddler AI · ValidMind · WhyLabs · Evidently AI · Galileo AI · Patronus AI · Arthur AI
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| LangSmith | agent tracing & evaluation | Trace capture and evaluation over LangChain/LangGraph runs with dataset-based scoring. Typical: step tracing, regression evaluation. | not checked | SOC 2 (claimed)supports Art. 12 record-keeping (claimed) |
| Langfuse | agent tracing & evaluation | Open-source tracing, prompt management and evaluation; self-hostable for retention control. Typical: self-hosted tracing, cost/latency analytics. | open source | GDPR-positionedsupports Art. 12 record-keeping (claimed) |
| Arize AI / Phoenix | ML & LLM observability | Production monitoring with drift and performance analysis; Phoenix is the open-source tracing side. Typical: drift monitoring, production analytics. | not checked | SOC 2 (claimed)drift-monitoring positioning (SR 11-7 style, claimed) |
| Helicone | LLM gateway & logging | Proxy-level logging of prompts, costs and latency across providers. Typical: gateway logging, cost control. | not checked | SOC 2 (claimed)supports Art. 12 record-keeping (claimed) |
| MLflow | experiment & model registry | Open-source tracking, model registry and lineage across training and deployment. Typical: model registry, validation records. | open source | model-validation positioning (SR 11-7 style, claimed) |
| Ragas | RAG evaluation | Open evaluation metrics for retrieval faithfulness and answer grounding. Typical: grounding checks, RAG regression. | not checked | OSS, no vendor certification |
| Deepchecks | validation & testing | Continuous validation suites for data and model behaviour. Typical: release gating, data validation. | not checked | evaluation-evidence positioning |
| Fairlearn | fairness toolkit | Open-source fairness assessment and mitigation for classification and regression. Typical: bias testing, fairness reporting. | not checked | OSS, no vendor certificationsupports Art. 10 bias examination (claimed) |
| Fiddler AI | model performance management | Explainability and monitoring platform aimed at regulated model risk teams. Typical: explainability, model monitoring. | not checked | SOC 2 (claimed)model-risk positioning (SR 11-7 style, claimed) |
| ValidMind | model risk management | Model validation documentation and workflow for banking model-risk functions. Typical: validation reports, MRM workflow. | not checked | SOC 2 (claimed)model-risk positioning (SR 11-7 style, claimed) |
| WhyLabs | data & model monitoring | Telemetry and drift monitoring over model inputs and outputs. Typical: drift detection, data quality monitoring. | SaaS (vendor cloud) | supports Art. 72 post-market monitoring (claimed) |
| Evidently AI | evaluation & monitoring | Open-source evaluation and monitoring reports for ML and LLM pipelines. Typical: evaluation reports, drift detection. | open source | supports Art. 72 post-market monitoring (claimed) |
| Galileo AI | LLM evaluation & observability | Evaluation metrics and traces for generative applications. Typical: LLM evaluation, trace inspection. | SaaS (vendor cloud) | supports Art. 15 accuracy measures (claimed) |
| Patronus AI · eingestellt (2026-08-31) | automated LLM evaluation | Automated scoring and adversarial test suites for generative output. Typical: automated evaluation, red teaming. | SaaS (vendor cloud) | supports Art. 15 robustness measures (claimed) |
| Arthur AI | model performance monitoring | Performance, bias and drift monitoring across deployed models. Typical: bias monitoring, performance monitoring. | SaaS (vendor cloud) | supports Art. 72 post-market monitoring (claimed)supports Art. 10 bias examination (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Select on
Trace completeness per agent step; log retention and immutability options; drift/quality metrics available out of the box; evaluation dataset support; export into your audit vault; self-host option.
Why this confidence
- 1 in-scope component of this use case is supplied by this layer (Model Drift & Accuracy Monitor) — a direct supplied_by path in the graph.
- The catalog use-case match is strong, so the component set this layer was derived from is reliable.
- High-risk tier: this layer carries mandatory Chapter III duties, so some tooling in it is non-optional.
- 15 community-maintained example vendors recorded on the layer node.
- Selection metrics for this layer are documented, so the shortlist can be compared objectively.
- Only 1 of 10 components this layer supplies are in your scope — evaluate a narrow subset of its capabilities.