Aporia is an Israeli ML-observability platform that monitors models in production for drift, bias and prediction quality issues.
Once models are live, they degrade: data drifts, features break and silent errors accumulate. Aporia monitors models and LLM applications for exactly these problems, alerting teams before customers notice and providing investigation tools to diagnose root causes. Its guardrails extend to generative AI, checking LLM outputs for hallucination and policy violations. The platform integrates with standard MLOps stacks and is used by teams running predictions at scale.
Aporia offers a free evaluation tier with enterprise plans priced by monitored models and usage; contracts are arranged with its sales team.
ML engineers and platform teams responsible for keeping production models and LLM features reliable.
Aporia watches what you shipped: models and LLM features running live.
Connect models via SDK, define key metrics and route alerts to your incident tools; the free tier covers evaluation.
Issues surface before customers notice, and post-incident reviews have real data instead of theories.
SDK integration, metric definition and alert routing take hours; the free tier supports evaluation before enterprise rollout.
Monitoring surfaces issues but remediation stays with your team, and noisy alert tuning is an ongoing discipline.
ML engineers value drift alerts that fire before model decay becomes an incident.
Models decay quietly. Aporia turns silent failures into alerts with context, covering classic ML and LLMs together. Any team with models in production and no monitoring is one data shift away from an incident.
A free tier supports evaluation and small deployments; enterprise monitoring at scale is subscription-based.
Yes — monitoring is integrated through SDKs and APIs, and alerts connect to existing incident workflows.
Against generic observability, Aporia understands ML semantics — drift, bias, hallucination — and unifies classic ML and LLM monitoring in one platform.