# EvalCore vs Hacktron

EvalCore records 0 monthly visits with 0% month-over-month growth and Free pricing. Hacktron records 125,526 monthly visits with -40.9% growth and Freemium, Paid, Contact for Pricing pricing.

| Metric | [EvalCore](https://openaitools.app/tool/evalcore) | [Hacktron](https://openaitools.app/tool/hacktron) |
| :--- | :--- | :--- |
| Website URL | https://evalcore.cc | https://hacktron.ai |
| Categories | AI Testing, AI Developer Tools, Large Language Models (LLMs), AI Agent | AI Agent, AI Code Review, AI Developer Tools |
| Pricing Model | Free | Freemium, Paid, Contact for Pricing |
| Monthly Visits | 0 | 125,526 |
| MoM Growth Rate | 0% | -40.9% |
| Primary Market | Global | India |
| Direct Visits | 0 | 34,807 |
| Review Score | 4.8 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## EvalCore

EvalCore solves a common failure mode in AI engineering: you tweak a prompt, swap a model, or bump a dependency, and something quietly breaks in production. Instead of trying to re-run expensive, non-deterministic model calls in CI, EvalCore treats AI behaviour like a snapshot. An eval is just a YAML file describing a target and a JSONL dataset, with scorers stacked on top to define what "good" means.

The first run happens live against a real model — anything OpenAI-compatible, vLLM, Ollama, a custom REST API, a shell command, or OpenTelemetry/OpenInference traces. Every request and response is written to a local SQLite cassette, keyed by a hash of the canonical request, along with token and cost figures on the summary. From then on, CI replays those recordings byte-for-byte with `--cache replay`: no network, no API keys, no flaky judge model, and identical inputs always produce identical verdicts.

Because the cassette is committed to your repository, a `--baseline main` comparison fails only on genuine regressions, and the process exit code gates the pipeline. Trials, model comparisons, agent traces, gates, and reports are all a few lines of YAML or a flag rather than a framework. It ships as one small Rust binary, works with any language on the other side of the target, and collects no telemetry.

**Pricing & Plans:** Free and open source under the Apache-2.0 licence, with $0 cost in CI thanks to offline cassette replay. Install via cargo or prebuilt binaries; no signup, no server and no telemetry.

- **Record once, replay forever:** A live run captures every model request and response into a local SQLite cassette that CI can replay offline and deterministically at zero cost.
- **Anything can be a target:** Point EvalCore at OpenAI-compatible endpoints, vLLM, Ollama, your own REST APIs, shell commands, or OTel/OpenInference traces — no SDK required.
- **Scorers that define 'good':** Stack contains, judge-rubric and other scorers in YAML to express exactly what a correct answer looks like for each case.
- **Regression gates and baselines:** Compare against a baseline branch so only real regressions fail, and let the process exit code gate your pull requests.
- **Cost, tokens and reports built in:** Every summary reports token usage and spend for live runs, while trials, model comparisons and history give you a clear picture over time.

**Strengths:** Genuinely free and open source under Apache-2.0, with no usage-based pricing; Deterministic offline replay removes flaky, expensive model calls from CI; Single dependency-free binary that works regardless of your app's language; Configuration is plain YAML plus JSONL, so suites are easy to review and version

**Limitations:** Requires a Rust toolchain or a prebuilt binary and comfort with the command line; Cassettes must be recorded live first, so initial setup needs real API access; No hosted dashboard — results and history live in your repo and CI logs

[Profile (Markdown)](https://openaitools.app/tool/evalcore.md)

## Hacktron

Hacktron is an AI-native cybersecurity platform built around the idea that offensive security should run continuously rather than in occasional annual engagements. It bundles three products under one roof: Red for ongoing adversary-style testing of your entire attack surface, Whitebox for deep white-box penetration testing driven by source-code analysis, and Review for automated security checks on every pull request.

The approach leans on AI agents that read actual code, reproduce issues, and attempt to demonstrate exploitability. This matters because traditional static analysis tools generate large volumes of theoretical findings that security teams must triage manually. Hacktron's stated position is to report only what it can prove is exploitable, with remediation guidance attached, so engineers spend their time on real risk.

Reported customers are engineering and product-security teams, including CTOs and compliance officers who need audit-ready evidence for SOC 2 and ISO 27001. The company has publicly announced a partnership with OpenAI to find vulnerabilities in its systems, and it publishes security advisories, a changelog, and open-source tooling. Deployment ranges from a self-serve Review trial to on-premises enterprise installations for sensitive environments.

**Pricing & Plans:** Freemium with custom enterprise tiers. Hacktron Review Pro starts at about $40 per developer per month including 50 pull requests per developer, with $1 per additional PR, and is free for open source. Whitebox pentests are priced per engagement, roughly $50 to $25,000 depending on scope (about $500 per 100,000 lines of code). Red and enterprise deployments are quote-based.

- **Continuous red teaming:** Hacktron Red continuously attacks your entire organization the way a real adversary would, then shows you the breach path and helps close it.
- **Machine-speed white-box pentests:** Hacktron Whitebox reads your source code, proves each finding in a sandbox or against your deployed app, and produces an audit-ready report for SOC 2 or ISO 27001.
- **Pull request security review:** Every pull request is checked with your whole codebase as context, so issues are caught before they merge into main.
- **Exploitability-based findings:** Only findings that can be shown to be exploitable are reported, verified rather than predicted.
- **API, MCP, and automations:** An API, MCP server, and automation layer let teams wire Hacktron into existing CI/CD and security workflows.

**Strengths:** Combines red teaming, white-box pentesting, and PR review in a single platform; Focuses on verified, exploitable findings instead of raw theoretical alerts; Transparent published pricing for Review and Whitebox tiers; Free for open source, with self-serve trial and API/MCP access

**Limitations:** Red teaming and enterprise deployment require a sales conversation; Whitebox pentest pricing scales with codebase size, which can get expensive; Best value requires integrating it into your existing CI/CD and code review flow

[Profile (Markdown)](https://openaitools.app/tool/hacktron.md)

## Frequently Asked Questions

### How do EvalCore and Hacktron compare in monthly traffic?

EvalCore has 0 monthly visits, while Hacktron has 125,526. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

EvalCore has a month-over-month growth rate of 0%; Hacktron has -40.9%.

### How does pricing compare?

EvalCore: Free. Free and open source under the Apache-2.0 licence, with $0 cost in CI thanks to offline cassette replay. Install via cargo or prebuilt binaries; no signup, no server and no telemetry. Hacktron: Freemium, Paid, Contact for Pricing. Freemium with custom enterprise tiers. Hacktron Review Pro starts at about $40 per developer per month including 50 pull requests per developer, with $1 per additional PR, and is free for open source. Whitebox pentests are priced per engagement, roughly $50 to $25,000 depending on scope (about $500 per 100,000 lines of code). Red and enterprise deployments are quote-based.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | The AI coding agent for building ambitious software | [.md](https://openaitools.app/tool/cursor.md) |
| 2 | [Devin](https://openaitools.app/tool/devin) | 2,549,346 | +1% | Paid | Cognition's autonomous software engineer that ships real pull requests | [.md](https://openaitools.app/tool/devin.md) |
| 3 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | The coding agent that learns your coding taste | [.md](https://openaitools.app/tool/command-code.md) |
| 4 | [KaneAI](https://openaitools.app/tool/kaneai) | 901,340 | +13.2% | Freemium, Paid, Contact for Pricing | GenAI-native end-to-end QA agent that plans, writes and heals tests in plain language | [.md](https://openaitools.app/tool/kaneai.md) |
| 5 | [Aikido Security](https://openaitools.app/tool/aikido-security) | 757,491 | +9.6% | Freemium | All-in-one AppSec platform that finds and auto-fixes vulnerabilities | [.md](https://openaitools.app/tool/aikido-security.md) |
| 6 | [Augment Code](https://openaitools.app/tool/augment-code) | 326,215 | +24.1% | Freemium, Free Trial, Contact for Pricing | Agent orchestration platform for your whole software development lifecycle | [.md](https://openaitools.app/tool/augment-code.md) |
| 7 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) |
| 8 | [ProjectDiscovery Neo](https://openaitools.app/tool/projectdiscovery-neo) | 158,530 | +22.5% | Paid, Contact for Pricing | AI security platform that finds exposures, proves they're exploitable, and ships the fix | [.md](https://openaitools.app/tool/projectdiscovery-neo.md) |
| 9 | [Mindgard](https://openaitools.app/tool/mindgard) | 153,455 | +1.3% | Contact for Pricing | Attack-led AI security testing and red teaming | [.md](https://openaitools.app/tool/mindgard.md) |
| 10 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) |

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Source: https://openaitools.app/compare/evalcore-vs-hacktron

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