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SPECIFICTION
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Arthur vs Pulsar

Arthur

arthur.ai

Pulsar

pulsar.ml

Side by side

FigureArthurPulsar
Price from$60/mo—
Free planYes—
Free trial——
Open source——
WebYes—
APIYesYes
Self-hostedYesYes
Plans published30
Spec score · Machine Learning Model Monitoring Software8.3 (#7)6.8 (#18)
Spec score · Model Monitoring Software8.4 (#12)6.8 (#24)
DeploymentArthur offers hosted SaaS, on-premises, and hybrid deployment options; its hybrid evals engine runs next to customer workloads so sensitive data stays in the environment.Pulsar provides an SDK and a containerized data ingestion service for integration with a user's machine and production code.

Plans

Arthur

  • FreeFree
  • Premium$60 a month
  • EnterprisePriced on request

Arthur pricing →

Pulsar

No plans published.

Pulsar pricing →

In their own words

Arthur

Arthur helps enterprises find AI agents across their environments and provides tools to observe and govern them. Discovery sensors monitor OpenTelemetry streams, MCP servers, network traffic, cloud APIs, and employee endpoints. The platform traces agent steps, tool calls, retrieved context, costs, latency, and behavior over time. Its governance features apply machine-checkable policies and provide real-time guardrail verdicts for prompt injection, PII exposure, restricted models, and off-policy tool use. Deployment options include hosted SaaS, on-premises, and hybrid; for hybrid setups, the evals engine runs beside customer workloads so sensitive data stays in the environment. Arthur lists RBAC, SSO, and deployed engineering support with every deployment option. Documented integrations include OpenAI, Anthropic, LangChain, LiteLLM, CrewAI, LlamaIndex, OpenAI Agents, Google ADK, AWS Bedrock, and Mastra. The Free plan is free, with up to four use cases, two projects, and seven-day data retention. Premium is $60 a month; Enterprise pricing is custom.

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Pulsar

Pulsar is a free, open-source, self-hosted system for monitoring machine learning models in production. It tracks model drift, concept drift, and performance by comparing incoming behavior with training data used as a baseline. An SDK and containerised ingestion service collect model inputs and outputs into a time series database, supporting historical and real-time data. The current database integration is InfluxDB, and the project is designed for large ingestion volumes without degraded performance. A scheduler checks drift and can trigger standard or customised alerts when metrics reach thresholds. Grafana displays drift measures across selected time ranges and user-defined features. Available drift tests include Kullback-Leibler divergence, Kolmogorov-Smirnov, Levene, and Wasserstein; performance measures include accuracy, recall, F1 score, Brier score, mean squared error, and R2. Deployment logos cover Flask, FastAPI, TensorFlow Extended, Seldon, AWS, Azure, Google Cloud, and Grafana.

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