How to get the best deals on AgentEval — pricing breakdown, savings tips, and alternatives
AgentEval offers a free tier — you might not need to pay at all!
Perfect for trying out AgentEval without spending anything
💡 Pro tip: Start with the free tier to test if AgentEval fits your workflow before upgrading to a paid plan.
per month
Don't overpay for features you won't use. Here's our recommendation based on your use case:
Most AI tools, including many in the voice agents category, offer special pricing for students, teachers, and educational institutions. These discounts typically range from 20-50% off regular pricing.
• Students: Verify your student status with a .edu email or Student ID
• Teachers: Faculty and staff often qualify for education pricing
• Institutions: Schools can request volume discounts for classroom use
Most SaaS and AI tools tend to offer their best deals around these windows. While we can't guarantee AgentEval runs promotions during all of these, they're worth watching:
The biggest discount window across the SaaS industry — many tools offer their best annual deals here
Holiday promotions and year-end deals are common as companies push to close out Q4
Tools targeting students and educators often run promotions during this window
Signing up for AgentEval's email list is the best way to catch promotions as they happen
💡 Pro tip: If you're not in a rush, Black Friday and end-of-year tend to be the safest bets for SaaS discounts across the board.
Test features before committing to paid plans
Save 10-30% compared to monthly payments
Many companies reimburse productivity tools
Some providers offer multi-tool packages
Wait for Black Friday or year-end sales
Some tools offer "win-back" discounts to returning users
If AgentEval's pricing doesn't fit your budget, consider these voice agents alternatives:
DeepEval: Open-source LLM evaluation framework with 50+ research-backed metrics including hallucination detection, tool use correctness, and conversational quality. Pytest-style testing for AI agents with CI/CD integration.
Free tier available
✓ Free plan available
LangSmith lets you trace, analyze, and evaluate LLM applications and agents with deep observability into every model call, chain step, and tool invocation.
Free tier available
Open-source LLM testing and evaluation framework for systematically testing prompts, models, and AI agent behaviors with automated red-teaming.
Free tier available
No. AgentEval is built exclusively for .NET and ships on NuGet (nuget.org/packages/AgentEval). Python teams should use DeepEval, PromptFoo, or LangSmith for equivalent AI agent evaluation capabilities. Based on our analysis of 870+ AI tools, AgentEval is one of the only mature agent evaluation frameworks targeting the Microsoft/.NET ecosystem specifically, which is precisely its positioning.
Yes. Any .NET agent that implements IChatClient can be tested via the IChatClient.AsEvaluableAgent() one-liner extension method. A Semantic Kernel bridge is also included for SK-based agents. This cross-framework design means you are not locked into MAF, though MAF is where the deepest integration exists with automatic tool call tracking and token/cost telemetry.
DeepEval and RAGAS are Python frameworks with larger communities and broader metric catalogs. AgentEval is their .NET counterpart, offering equivalent coverage for RAG metrics (Faithfulness, Relevance, Context Precision/Recall), plus unique additions like the 192-probe Red Team module and fluent tool-chain assertions. Choose based on language ecosystem — AgentEval for C#/.NET shops, DeepEval/RAGAS for Python. All three are open source.
It scales with repetition count: 100 tests × 50 repetitions equals 5,000 LLM calls, roughly $15–$30 per test suite at GPT-4 pricing. AgentEval's recommended pattern is to use live stochastic evaluation only for new scenarios and switch to trace record/replay for regression testing in CI, which eliminates API costs entirely. The comparer's RunsPerModel option (typically 5) gives statistical stability without runaway cost.
The Red Team module runs 192 attack probes across 9 attack types: Prompt Injection, Jailbreaks, PII Leakage, System Prompt Extraction, Indirect Injection, Excessive Agency, Insecure Output Handling, API Abuse, and Encoding Evasion. This covers 6 of the OWASP LLM Top 10 2025 vulnerabilities (60% coverage) with MITRE ATLAS technique mapping, and results can be exported directly to PDF for compliance reporting via result.ExportAsync("security-report.pdf", ExportFormat.Pdf).
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Get Started with AgentEval →Pricing and discounts last verified March 2026