Your LLM Audit Agent

Stop checking dashboards. Start getting answers.

Benchwright is an autonomous agent that continuously evaluates your AI models, detects regressions, benchmarks alternatives, and tells you exactly what to change. Zero setup. Flat pricing.

Try It Free → How It Works → No credit card required
benchwright eval --continuous
$ benchwright connect --stack production
Connected to 3 models, 12 endpoints
$ benchwright eval --run
Running 847 evaluation tasks...
Claude Sonnet 4: 94.2% accuracy (+1.3%)
GPT-4.1: 87.1% accuracy (-3.8% drift)
Cost anomaly: $0.41/req avg (was $0.28)
Recommendation: Route coding to Sonnet 4
Report generated. Next eval in 6h.

57% of companies deploy AI agents. Most have no idea if they still work.

Existing tools give you dashboards, charts, and metrics. Then they wait for you to look at them. You don't. Models drift, costs spike, quality degrades, and nobody notices until a customer complains.

Traditional Eval Platforms

  • Manual eval configuration
  • Check dashboards when you remember
  • Interpret results yourself
  • Decide what action to take
  • $500-5,000+/mo usage-based pricing
  • Forget about it for three weeks

Benchwright

  • Auto-configures evals from your API
  • Runs benchmarks on its own schedule
  • Analyzes results and spots anomalies
  • Recommends specific actions to take
  • $29-99/mo flat rate. No surprises.
  • Never forgets. Never sleeps.

Every competitor is a dashboard. Benchwright is an agent.

We analyzed the top 5 AI evaluation platforms. None of them do what Benchwright does: autonomous daily evaluation that detects regressions before your dashboards alert.

Platform Approach Autonomous Setup Time Pricing Best For
Benchwright Agent-first: auto-discovers, evaluates, recommends Yes 2 minutes $29-99/mo flat Mid-market AI teams
Maxim AI Full-stack eval + observability + gateway No 2-4 weeks $50K-250K+/yr Enterprise teams
Arize AI ML observability extended to LLMs No 1-2 weeks $500-5K+/mo Data science teams
LangSmith LangChain-native tracing + evals No 1-3 days $39/user/mo+ LangChain devs
Langfuse Open-source observability (self-hosted) No 3-5 days Free-$500/mo Cost-conscious devs

Evaluation that thinks for itself.

01

Continuous Benchmarking

Runs your models against real-world task sets on a schedule. Catches performance regressions before they hit production users.

02

Drift Detection

Monitors output quality over time. When a model update quietly breaks your pipeline, Benchwright knows within hours, not weeks.

03

Competitive Analysis

Benchmarks your current models against alternatives. Shows you exactly when switching providers would save money or improve quality.

04

Actionable Reports

No dashboards to check. Plain-language reports with specific recommendations: what to change, why, and the expected impact.


Flat rate. No per-user. No per-trace. No surprises.

While competitors charge per trace, per seat, or per thousand events, Benchwright charges one flat monthly fee. Know your bill before you sign up.

Starter
For teams getting started with LLM evaluation
$29/mo
Billed monthly
  • 1 application
  • Daily autonomous evals
  • 3 model providers
  • Email reports
  • 7-day history
Get Started
Team
For scaling AI operations
$99/mo
Billed monthly
  • Unlimited applications
  • Custom eval schedules
  • All model providers
  • API access
  • 1-year history
  • Priority support
Get Started

What competitors charge for the same thing

Maxim AI
$50K-250K+/yr
Contact sales required
Arize AI
$500-5K+/mo
Usage-based + seats
LangSmith
$39/user/mo+
10 users = $390+/mo
Langfuse
$100-500+/mo
Or self-host (DIY ops)

Try it right now.

Paste your API key, pick a model, and run a real evaluation in under 60 seconds. No signup required.

All Tasks (15)
Summarization (5)
Classification (5)
Code Generation (5)
Takes 15–60 seconds depending on model.
Evaluating... Running tasks, scoring outputs, calculating metrics.
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Score

Automate this. Run it every day.

Schedule daily evals, get regression alerts, and never wonder if your model is drifting.

Schedule Daily Evals →

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