AI Observability Platform for Regulated Enterprises
Trace every agent action, detect hallucinations, track LLM cost, and prove compliance — built on OpenTelemetry. Perimattic has been shipping production DevOps since 2018.
< 5ms
Trace overhead
100%
OTel-native
8+
Frameworks
8
Regulations
What is AI observability?
Perimattic AI Suite is an OTel-native AI observability platform that helps regulated enterprises trace every agent action, detect hallucinations and prompt injection, track per-model LLM cost, and prove governance compliance across HIPAA, EU AI Act, DORA, BaFin, MAS FEAT, and DPDP frameworks.
Full session tracing
Reconstruct every agent action, tool call, and retrieval from root span to leaf — across any LLM or framework.
Eval on live traffic
Faithfulness scores and hallucination rates run on production requests, not just curated test sets.
Evidence-ready audit trail
Structured, immutable logs formatted for HIPAA, EU AI Act, DORA, and MAS FEAT — no manual export.
Beyond traditional APM
AI observability is the discipline of instrumenting AI systems — LLMs, agents, and RAG pipelines — to collect distributed traces, metrics, logs, evaluation scores, and user feedback. Where traditional APM surfaces latency and error rates, AI observability adds a layer that is unique to probabilistic systems: faithfulness scores, hallucination rates, prompt injection classifications, and per-model token cost. Together, traces + metrics + logs + eval scores + user feedback give teams the signal they need to debug failures, attribute cost, and produce the compliance evidence that regulators require.
From LLM monitoring to multi-agent tracing
AI observability evolved directly from LLM observability as production systems expanded beyond single-model calls into multi-agent orchestration. A single LLM endpoint is observable with a request/response log; a multi-agent system — where an orchestrator delegates to specialist sub-agents, each of which calls tools, retrieves documents, and hands off results — requires distributed tracing to reconstruct the full session. The shift from LLM monitoring to AI observability mirrors the shift from single-server logging to distributed systems observability a decade ago: the complexity of the system demanded a more complete instrumentation model.
Why OTel-native is the enterprise default
For enterprise buyers, the defensible architecture for AI observability is OTel-native. OpenTelemetry is an open standard — it means no proprietary SDK in the critical path, no framework lock-in to a vendor that may not exist in three years, and composability with the Datadog, New Relic, or Grafana infrastructure your team already runs. The CNCF GenAI working group has published gen_ai.* semantic conventions that define exactly how LLM and agent spans should be structured. Perimattic AI Suite is built against those conventions, so the traces you emit today will be readable by every OTel-compatible tool tomorrow.
Six capability pillars for production AI systems
Purpose-built for AI systems — from agent tracing to RAG evaluation and compliance evidence — all in one OTel-native platform.
Agent Tracing
Trace every tool call, reasoning step, handoff and escalation across multi-agent workflows.
Learn moreHallucination Detection
Detect factual errors and faithfulness failures using LLM-as-judge and RAGAS scoring in production.
Learn morePrompt Injection Monitoring
Classify and block adversarial prompt injection attempts before they reach your LLM.
Learn moreLLM Cost Tracking
Attribute token spend per model, per agent, and per user for budget governance.
Learn moreRAG Evaluation
Score retrieval quality, faithfulness, and answer relevancy across your RAG pipelines.
Learn moreCompliance Audit Trail
Capture immutable, structured traces that satisfy HIPAA, EU AI Act, DORA, and MAS FEAT audit requirements.
Learn moreCompliance coverage across 8 regulatory frameworks
Audit-grade evidence exports ready for your compliance team.
HIPAA
PHI redaction, immutable audit logs, and BAA-eligible deployment for clinical AI systems.
Learn more USSOC 2
CC7/CC8 control evidence for LLM systems across your full SOC 2 Type II audit period.
Learn more EUEU AI Act
Post-market monitoring and technical documentation for Annex III high-risk AI systems.
Learn more EUDORA
ICT risk management and third-party LLM provider monitoring for EU financial entities.
Learn more DEBaFin AI
Model risk governance and audit trail for German financial institutions under BaFin guidance.
Learn more SGMAS FEAT
Fairness, ethics, accountability, and transparency evidence for MAS-regulated AI deployments.
Learn more CAOSFI E-23
Model risk management documentation and operational resilience for Canadian financial firms.
Learn more INDPDP Rules
Data principal consent tracing and personal data processing audit logs under India's DPDP Act.
Learn moreFrom instrumentation to compliance evidence in four steps
Get up and running in minutes with OpenTelemetry-native instrumentation — no proprietary agents, no middleware in your critical request path.
Instrument
Add OTel SDK and point the OTLP exporter at Perimattic. No proprietary agent, no proxy in the critical path.
Ingest
Traces, metrics, logs and eval scores flow into the Perimattic collector. Multi-model, multi-agent, multi-tenant.
Analyse
Dashboards show per-agent cost, hallucination rate, prompt injection attempts, latency p99 and RAGAS scores.
Alert & Prove
Set threshold alerts and export audit-ready evidence packages for your compliance team.
How Perimattic AI Suite compares to Langfuse, LangSmith and Datadog
See how Perimattic AI Suite stacks up across the dimensions that matter most — compliance, cost tracking, and production reliability.
| Dimension | Langfuse | LangSmith | Datadog LLM | Perimattic AI Suite |
|---|---|---|---|---|
| Architecture | OTel-native spans | Framework-native (LangChain) | APM add-on | OTel-native spans |
| Regulatory compliance | None built-in | None built-in | None built-in | HIPAA, EU AI Act, DORA, BaFin, MAS, OSFI, DPDP |
| Self-host | Yes (Apache-2.0) | Limited | Enterprise only | Yes (managed + self-host) |
| Framework lock-in | None | LangChain required | None | None |
| Cost attribution | Per-model | Per-model | Per-host + per-span | Per-model + per-agent + per-user |
| Enterprise support | Community + paid cloud | Paid | Enterprise SLA | Dedicated SLA-backed |
Built on DevOps discipline since 2018
When AI observability vendors were still being founded, Perimattic was already shipping production DevOps. That discipline is baked into every trace, eval, and audit log we produce.
We started out watching servers at 2 a.m., making sure other people's systems stayed up when nobody else was awake — DevOps automation and monitoring. That was 2018. Every AI observability startup you compare us against was founded in 2022 or later. We brought five years of production discipline into every trace, every alert, every compliance export.
Since 2018
Production AI & DevOps
2022+
When rivals were founded
5 yrs
Head-start on discipline
Purpose-built for regulated industries
From healthcare to financial services — purpose-built observability for the compliance frameworks your industry faces.
Healthcare
HIPAA-aligned observability for clinical LLMs, patient-facing chatbots and PHI-handling workflows.
Learn moreFinancial Services
Model risk management, fraud AI monitoring, credit decisioning compliance across DORA, BaFin, MAS and OSFI.
Learn moreGerman Mittelstand
BaFin + EU AI Act readiness for manufacturing and financial Mittelstand enterprises.
Learn moreLegal
Hallucination monitoring and citation verification to prevent fabricated case citations (Mata v. Avianca framing).
Learn moreCommon questions, answered
Everything you need to know about AI observability — from architecture to compliance — answered clearly.
What is AI observability?
AI observability is the practice of instrumenting AI systems — LLMs, agents, RAG pipelines — to collect distributed traces, metrics, logs, evaluation scores, and user feedback. It enables teams to debug failures, attribute cost, and produce compliance evidence. It evolved from LLM observability as systems expanded beyond single-model calls to multi-agent orchestration.
How is AI observability different from LLM monitoring?
LLM monitoring tracks surface metrics: latency, token cost, error rates. AI observability goes deeper — distributed traces that reconstruct full agent sessions, evaluation scores (faithfulness, hallucination rate, RAGAS), user feedback loops, and audit-grade immutable logs. Regulated enterprises and multi-agent systems need observability; simple internal chatbots may only need monitoring.
What does OTel-native mean for AI observability?
OTel-native means the platform ingests OpenTelemetry spans natively — no proprietary SDK or proxy in the critical path. You use the same OTel SDK you already use for your microservices, annotate LLM and agent spans with gen_ai.* semantic conventions, and point the OTLP exporter at Perimattic. This means no framework lock-in, composability with Datadog or New Relic for unified infra + AI observability, and alignment with enterprise procurement checklists that require open standards.
Which regulations require AI observability?
HIPAA requires audit trails for AI systems processing PHI. EU AI Act (Aug 2026 high-risk enforcement) requires post-market monitoring and technical documentation. DORA (Jan 2025, EU financial sector) requires ICT incident reporting and resilience testing — LLM API providers are third-party ICT concentration risks. BaFin (Germany), MAS FEAT (Singapore), OSFI E-23 (Canada), and India's DPDP Rules 2025 all have model risk management or audit trail obligations that observability directly satisfies.
How does Perimattic AI Suite compare to Langfuse?
Both use OTel-native span tracing. The key difference is regulatory coverage: Langfuse has none built-in (it is an open-source observability tool, not a compliance platform). Perimattic AI Suite adds HIPAA PHI redaction, EU AI Act audit trail capture, DORA incident logging, and managed compliance evidence exports. For teams that need observability plus compliance, Perimattic is the more complete solution.
Can I self-host Perimattic AI Suite?
Yes. Perimattic AI Suite is available in a managed cloud and a self-hosted deployment for teams with data residency requirements (EU Schrems II, HIPAA BAA, India DPDP, BaFin). Self-hosted deployments run on your own infrastructure — OVHcloud, Scaleway, IONOS, T-Systems, or AWS/Azure with regional endpoints.
What LLM providers does Perimattic AI Suite support?
Perimattic AI Suite ingests OTel spans from any LLM provider — OpenAI (ChatCompletions, Assistants, Responses API), Anthropic Claude (Messages API, tool use), AWS Bedrock (Agents + Foundation Models), Azure OpenAI, Google Vertex AI, Mistral, and Aleph Alpha. If your LLM emits OTel spans following gen_ai.* semantic conventions, it works.
How long does it take to integrate AI observability?
For a single-model chatbot, integration takes under 30 minutes: install the OTel SDK, configure the OTLP exporter, annotate your LLM span with gen_ai.* attributes. For multi-agent systems with compliance requirements (HIPAA, DORA), allow 1–2 days for full PHI redaction rules, audit-trail configuration, and compliance dashboard setup.
Ready to observe your AI systems?
Join the waitlist — we'll show you how Perimattic AI Suite helps regulated enterprises trace agents, catch hallucinations, and prove compliance.