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Prefactor Review

Prefactor evaluates AI agents in real-time, scoring runs for quality, drift, and risk in production environments.

shipped Aug 6, 2026agentsfreemium
Domain rating45Monthly visits568/mo
agents
Prefactor — product screenshot

Why it matters

1Offers a free tier including 25,000 spans per month.
2Paid plans start at approximately $250 per month, with additional usage billed at $2.00–$2.50 per 1,000 spans.
3Integrates via SDKs for TypeScript and Python, supporting real-time monitoring.
4Backed by Antler, Black Nova VC, and Func Ventures.

About Prefactor

Business Model
Subscription SaaS
Usage Pricing
$0.04 per span
Free Credits
1M free spans for the first 50 sign ups
Funding
Backed by Antler, Black Nova VC & Func Ventures
Platforms
Web, API
Target Audience
Developers and AI teams needing real-time monitoring of AI agents.

Pricing Plans

Free Tier
Free for your first 25,000 spans / monthly
  • First 25,000 spans free
  • Real-time agent evaluation
Paid Plan
Contact for pricing
  • Additional spans beyond free tier
  • Enhanced features

Cost Examples

  • Evaluate 1 agent run: ~$0.04

Investors

Antler, Black Nova VC, Func Ventures

Specs

API Available

Yes, public API

overview

What is Prefactor?

Prefactor is an AI agent observability and evaluation tool developed by Prefactor Pty Ltd that enables engineering teams shipping agents to real customers to evaluate AI agents in real-time. It scores every agent run for quality, drift, and risk in production, enabling immediate action on failing agents and integrating an enforcement layer.

features

Key Features of Prefactor

Prefactor provides a comprehensive suite of features designed for real-time evaluation and management of AI agents in production, focusing on quality, risk, and cost.

  • Real-time Evaluation: Scores every AI agent run for quality, drift, risk, and cost.
  • Runtime Enforcement: Automatically pauses or blocks high-risk agent actions in milliseconds.
  • Human-in-the-Loop Control: Routes sensitive or uncertain agent actions to a human for approval with full context.
  • Quality and Drift Detection: Identifies quality regressions and behavioral drift in agents as they happen.
  • Sensitive Information Detection: Automatically detects 17 categories of sensitive information in agent traffic.
  • Cost Attribution: Tracks and attributes AI spend to specific agents, teams, or tasks.
  • Queryable Traces: Provides detailed, queryable traces for investigating AI issues.
  • Custom Spans: Enables enrichment of agent runs with context from external sources like GitHub or databases.

use cases

Who Should Use Prefactor?

Prefactor is primarily designed for engineering teams and developers who are deploying and managing AI agents in production environments, particularly those with customer-facing applications where reliability and risk mitigation are critical.

  • Engineering teams shipping agents to real customers: To evaluate AI agents in real-time and ensure performance at scale.
  • Teams requiring real-time risk mitigation: For automatically blocking or holding risky agent actions for approval during rollout.
  • Developers needing to surface quality regressions and drift: To identify and address behavioral changes in agents as they occur.
  • Organizations focused on sensitive data handling: To detect and manage sensitive information moving through agent traffic.
  • Teams implementing human-in-the-loop workflows: For routing sensitive agent actions to human review with an immutable audit trail.

how to use

How to Use Prefactor

Prefactor integrates via SDKs for quick installation and real-time monitoring of AI agents. Users can begin by installing the SDK and instrumenting their agent runs.

  • 1Install the Prefactor SDK (TypeScript or Python) into your AI agent application.
  • 2Instrument your agent runs to send data to Prefactor for real-time evaluation.
  • 3Configure evaluation criteria and risk thresholds within the Prefactor platform.
  • 4Monitor agent performance, quality, and drift through the Prefactor dashboard.
  • 5Utilize the human-in-the-loop system for approving or rejecting sensitive agent actions.
  • 6Investigate AI issues using queryable traces provided by the platform.

pricing

Prefactor Pricing & Plans

Prefactor operates on a freemium model, offering a free tier for initial usage and tiered paid plans for expanded capabilities and higher usage volumes. Usage is primarily measured by 'spans'.

  • Free Tier: Includes the first 25,000 spans per month.
  • Paid Plan: Starts at approximately $250 per month, with additional usage billed at $2.00–$2.50 per 1,000 spans.
  • Usage Pricing: $0.04 per span for evaluation of an agent run.
  • Free Credits: 1 million free spans for the first 50 sign-ups.

Pros

  • +Real-time runtime enforcement for proactive risk mitigation, pausing or blocking high-risk agent actions.
  • +Comprehensive scoring of every agent run for quality, drift, risk, and cost in production.
  • +Integrated human-in-the-loop control with full context and immutable audit trails for sensitive actions.
  • +Automatic detection of 17 categories of sensitive information in agent traffic.
  • +SDK-based integration (TypeScript, Python) for quick installation and real-time monitoring.
  • +Supports agent lifecycle management with 'eval-gated promotion' for new prompts and tool definitions.

Cons

  • Specific star ratings from major review platforms like Capterra are not yet widely available.
  • Paid plans start at approximately $250 per month, which may be a consideration for smaller teams or individual developers beyond the free tier.
  • Focus is primarily on AI agents, potentially less comprehensive for broader LLM application monitoring without agent components.
  • Requires SDK integration into existing agent applications, which involves an initial setup effort.

Policies

Pricing Page

View Pricing

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Prefactor vs Competitors

Prefactor differentiates itself in the AI agent evaluation and observability market primarily through its real-time enforcement layer, which allows for proactive intervention in agent actions, a capability not universally offered by competitors.

1

Provides a platform for debugging, testing, evaluating, and monitoring LLM applications, including agents, with a strong focus on tracing and dataset management.

Prefactor focuses on automated scoring for quality, drift, and risk of agent runs. LangSmith offers a broader suite for debugging, testing, and monitoring the entire LLM application lifecycle, providing detailed traces and evaluation tools that can be used to derive similar insights, but might require more manual configuration for specific drift/risk scoring.

2

Offers detailed logging, cost tracking, caching, and rate limiting for LLM applications, with a focus on performance and cost optimization.

Prefactor provides explicit scoring for quality, drift, and risk. Helicone offers comprehensive observability for LLM calls, including performance metrics and cost analysis, which helps identify issues, but requires more manual analysis or custom logic to derive specific quality or drift scores.

3

Provides real-time monitoring and debugging for LLM applications, focusing on traces, logs, and cost tracking, with an emphasis on ease of integration.

Prefactor provides automated scoring for quality, drift, and risk. LLMonitor offers real-time visibility into LLM application performance, logs, and costs, focusing on debugging and tracing. While it helps identify operational issues, it requires custom implementation to achieve the same level of automated quality, drift, and risk scoring as Prefactor.

4
Weights & Biases Prompts

Integrates LLM evaluation and prompt engineering directly into the MLOps workflow, allowing for systematic tracking of prompt and model performance.

Prefactor focuses on real-time production scoring for quality, drift, and risk of agent runs. W&B Prompts provides robust tools for systematic evaluation of LLM outputs and tracking experiments, which can be adapted for agent quality, but it's more geared towards development and deployment evaluation rather than immediate, automated production scoring for drift and risk.

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