Arize and Phoenix are powerful LLM eval and ML monitoring tools. Peekr is the compliance layer — in-process regulatory enforcement and claim-level hallucination scoring that runs inside your own process, before output reaches your app.
No credit card · MIT license · 17 compliance packs on Pro
The architecture
Arize / Phoenix
Peekr — in-process, framework-agnostic
Side by side
| Capability | Arize / Phoenix | Peekr |
|---|---|---|
| Instrumentation model | OTel spans → Phoenix / Arize backend | Class-level patch, no decorators |
| In-process compliance enforcement | eval infra only, no built-in packs | 17 packs (HIPAA, FDCPA, FINRA…) on Pro |
| Claim-level hallucination scoring | —LLM-as-judge evals (custom setup) | built-in, every sentence scored |
| LLM eval datasets / annotation queues | Phoenix eval datasets and annotations | —claim scoring; no eval dataset mgmt |
| ML model monitoring (non-LLM) | drift, data quality, performance | LLM-only |
| Self-host with MIT license | —Phoenix open-source; Arize cloud proprietary | MIT SDK; VPC self-host on Enterprise |
| Data stays in your process | —spans exported to Phoenix/Arize backend | compliance packs enforced in-process |
| Zero added network latency per call | —async export; OTel overhead minimal | no external hop in the enforcement path |
The compliance gap
Phoenix lets you run LLM-as-judge evals — but enforcement happens post-hoc, not in the call path. Peekr blocks violations and writes a tamper-evident audit log before output reaches your application. 17 regulatory packs on the $99/mo Pro plan.
Where Arize / Phoenix wins
Peekr is not an ML model monitoring platform or a retrieval eval framework.
You need full ML model monitoring
Arize is purpose-built for production ML observability — feature drift, data quality, model performance across non-LLM models. If your stack includes classical ML or computer vision alongside LLMs, Arize covers the whole model layer.
You want a rich LLM eval framework
Phoenix ships a comprehensive eval library: retrieval precision, Q&A correctness, toxicity, and hallucination via LLM-as-judge. If building and managing eval datasets is the primary use case, Phoenix is the more complete platform.
You have existing Arize infrastructure
Teams already shipping span data to Arize cloud can add LLM tracing without a new backend. Phoenix integrates natively with the Arize platform and its existing monitoring dashboards.
FAQ
For in-process regulatory compliance and hallucination detection, yes. Peekr runs inside your own process — it patches OpenAI, Anthropic, Gemini, and Bedrock at the class level and enforces 17 compliance packs (HIPAA, FDCPA, FINRA, GDPR…) before LLM output reaches your application. Arize is a mature ML observability cloud for monitoring model performance and drift; Phoenix is Arize's open-source LLM eval and tracing framework. Neither ships pre-built compliance packs as in-process enforced guardrails.
No. Phoenix instruments via OpenTelemetry traces that are exported to a backend (Phoenix server or Arize cloud). Peekr patches the SDK client class once with peekr.instrument() — every OpenAI() or Anthropic() instance is automatically traced in-process with no OTel exporter or separate backend required for enforcement. You can still export spans to your own backend, but compliance enforcement does not depend on it.
Arize and Phoenix do not ship pre-built regulatory compliance packs. Phoenix provides evaluation infrastructure where you can build custom LLM-as-judge evaluators — but blocking violations, enforcing packs like HIPAA or FDCPA on every call, and writing a tamper-evident audit log in your process are not built in. Peekr ships 17 regulatory packs enforced in-process on the $99/mo Pro plan.
No. Peekr patches the LLM SDK clients at the class level and observes calls inside your own process. Your prompts, responses, and provider API keys never leave your stack. Phoenix's OTel exporter sends spans to a Phoenix backend (self-hosted or Arize cloud). Arize cloud collects model data for monitoring. Peekr compliance enforcement runs fully in-process.
Auto-instruments OpenAI, Anthropic, Gemini, Bedrock, and more. 17 in-process compliance packs. Claim-level hallucination scoring. MIT-licensed. Free up to 10k spans/month.