# EvalCore vs Glean

EvalCore records 0 monthly visits with 0% month-over-month growth and Free pricing. Glean records 3,301,875 monthly visits with +0.1% growth and Contact for Pricing pricing.

| Metric | [EvalCore](https://openaitools.app/tool/evalcore) | [Glean](https://openaitools.app/tool/glean) |
| :--- | :--- | :--- |
| Website URL | https://evalcore.cc | https://www.glean.com |
| Categories | AI Testing, AI Developer Tools, Large Language Models (LLMs), AI Agent | AI Productivity Tools, AI Agent, AI Assistant, AI Search Engine |
| Pricing Model | Free | Contact for Pricing |
| Monthly Visits | 0 | 3,301,875 |
| MoM Growth Rate | 0% | +0.1% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 2,628,666 |
| Review Score | 4.8 | 4.6 |
| Saved Count | 0 | 7 |
| 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)

## Glean

Glean addresses a problem most enterprises hit after their first wave of AI adoption: models are powerful but isolated from the context of the business. The platform builds an enterprise context layer by indexing and understanding content across the tools a company already runs on, then uses that grounding to improve both answer quality and efficiency. Because fewer irrelevant tokens need to be sent to a model, the same question costs less to serve.

The product surface spans four areas: enterprise search, an AI assistant for everyday work, an agent platform for automating long-running processes, and Glean Protect for security and governance. Administrators also get model routing and an AI gateway, letting them decide which models handle which requests without rebuilding integrations. Everything inherits the access permissions of the source systems, so answers stay within each employee's existing rights.

Glean serves large organisations across finance, healthcare, retail, technology, and government, with published customers including Booking.com, Zillow, and Ericsson. It holds SOC 2 Type II, ISO 27001, ISO 42001, HIPAA, GDPR, and TX-RAMP Level 2 attestations, and reports 275+ connectors, 40+ supported language models, and 99.9%+ uptime on core services.

**Pricing & Plans:** Contact for pricing. Glean is licensed to organisations based on seats, connectors, and deployment scope, and is evaluated through a demo rather than self-serve purchase.

- **Permission-aware enterprise search:** Ask a question in plain language and get a cited answer assembled from documents, tickets, wikis, and messages across every connected system, with permissions respected.
- **Agent builder and orchestration:** Reusable agents handle multi-step workflows such as onboarding, account research, or incident follow-up by orchestrating actions across business apps.
- **Model routing and AI gateway:** Glean automatically picks and routes requests to the right model and reasoning level, giving teams access to 40+ LLMs through a single governed gateway.
- **Grounded content and analysis:** Draft on-brand documents, summarise meetings, analyse data, and create content grounded in company context rather than generic model knowledge.
- **Governance and observability:** Administrators get usage controls, spend visibility, audit logging, and compliance-ready governance for every AI interaction.

**Strengths:** Answers cite source systems and inherit their permissions, which removes the main blocker to enterprise-wide rollout; One platform covers search, assistant, and agents instead of three separate vendors; Model routing and token efficiency directly reduce the cost per AI query at scale; 275+ pre-built connectors shorten integration work considerably

**Limitations:** Enterprise pricing is quote-based, so budget planning requires a sales conversation; Full value depends on connecting many internal data sources, which takes IT effort; Smallest teams may find the platform more than they need

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

## Frequently Asked Questions

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

EvalCore has 0 monthly visits, while Glean has 3,301,875. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

EvalCore has a month-over-month growth rate of 0%; Glean has +0.1%.

### 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. Glean: Contact for Pricing. Contact for pricing. Glean is licensed to organisations based on seats, connectors, and deployment scope, and is evaluated through a demo rather than self-serve purchase.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Notion](https://openaitools.app/tool/t) | 173,441,670 | +0.1% | Freemium | The AI workspace where teams and agents get work done together | [.md](https://openaitools.app/tool/t.md) |
| 2 | [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) |
| 3 | [Dust](https://openaitools.app/tool/dust) | 547,217 | +1.8% | Contact for Pricing | Builds collaborative enterprise AI agents connected to company knowledge, but offers less robust out-of-the-box universal search. | [.md](https://openaitools.app/tool/dust.md) |
| 4 | [Gumloop](https://openaitools.app/tool/gumloop) | 379,557 | -21.1% | Freemium, Paid, Contact for Pricing | Build, share, and control AI agents that actually do the work | [.md](https://openaitools.app/tool/gumloop.md) |
| 5 | [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) |
| 6 | [Moveworks](https://openaitools.app/tool/moveworks) | 168,527 | -4.3% | Contact for Pricing | Automates employee IT and HR support requests exceptionally well, but focuses less on general universal document search. | [.md](https://openaitools.app/tool/moveworks.md) |
| 7 | [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) |
| 8 | [Kore.ai](https://openaitools.app/tool/kore-ai) | 143,328 | -18.2% | Contact for Pricing | Deploys enterprise conversational assistants and automated workflows, but lacks Glean's unified organization-wide knowledge graph search. | [.md](https://openaitools.app/tool/kore-ai.md) |
| 9 | [Coworker AI](https://openaitools.app/tool/coworker-ai) | 129,346 | -1.8% | Free Trial, Paid, Contact for Pricing | Creates permission-aware context graphs and routes tasks across enterprise tools, but has a smaller connector library than Glean. | [.md](https://openaitools.app/tool/coworker-ai.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) |

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

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)