# Labelbox vs Realyst

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Realyst records 0 monthly visits with — growth and Free Trial pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Realyst](https://openaitools.app/tool/realyst) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://chrome.google.com/webstore/detail/realyst-single-family-inv/ofkklmgigbjmjmlgdcahokigbpginkgn |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Real Estate, AI Investing, AI For Finance, AI Report Generator |
| Pricing Model | Contact for Pricing | Free Trial |
| Monthly Visits | 762,803 | 0 |
| MoM Growth Rate | -22.8% | — |
| Primary Market | United States | Global |
| Direct Visits | 370,024 | 0 |
| Review Score | 4.4 | 3.8 |
| Saved Count | 0 | 4 |
| Supported Platforms | Website | Browser Extension |

## Labelbox

Labelbox occupies an unusual position in the AI stack: it does not primarily sell a chatbot or a generator, it builds the environments and data that models learn from plus the agents that take over recurring engineering and research work. The homepage claim is direct — frontier AI labs train on Labelbox environments and expert data, while engineering and research teams hand their recurring backlog to agents running on Recursion. A recent Series E of more than $100 million signals a heavily funded bet on that thesis.,For enterprises, the newer half of the product is Recursion. Agents are triggered by real signals — a failing build, a PagerDuty alert, a new dependency release, a fresh model checkpoint, or a Monday morning schedule — and then work in the background until the job is done. The examples the company promotes are concrete deliverables rather than summaries: a tested pull request with a preview link, a postmortem draft in Confluence with every event sourced, an ablation table naming the changes that mattered, or a patch for each exposed service after a critical CVE. A grader agent scores each run, and the graded session leaves behind memories, notes and skills that the next session reads before it acts.,The original foundation remains the data business: labeling, annotation, curation and model evaluation supported by expert human reviewers, used for computer vision, NLP, supervised fine-tuning, RLHF and red teaming. In practice the two halves reinforce each other, since the environments that produce high-quality training data are the same environments where agents learn to do real work. Pricing is enterprise-style, routed through sales conversations and shaped by data volume and agent usage, with no public self-serve tier.

**Pricing & Plans:** Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases.

- **Data labeling and expert annotation:** Structured annotation, curation, model evaluation and human-in-the-loop review across vision, language and generative tasks, used to build the training and eval data behind production models.
- **Autonomous agent sessions:** Recursion agents start on their own from builds, alerts, commits, checkpoints and schedules, then work in the background until the task is finished and reviewable.
- **Domain specialists by function:** Specialist agents cover software engineering, reliability, security, ML research, evals, R&D, product and finance workflows, from flaky test fixes to postmortem drafts and vulnerability patches.
- **Graded outcomes that compound:** Every agent run is scored by a grader, and graded sessions leave memories, notes and skills that the next session reads before acting, so quality compounds over time.
- **Native tool and data integrations:** Agents operate against the tools teams already use, including GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery, Databricks, Vercel and cloud providers.

**Strengths:** Covers both sides of the problem: training and eval data production plus agents that act on recurring work; Backed by a recent $100M+ Series E and adoption by frontier AI labs, indicating real funding and demand; Agent runs produce concrete deliverables with proof — tests, reruns, ablations — rather than unverified suggestions; Integrates with the toolchain teams already run: GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery and Databricks

**Limitations:** No self-serve pricing or open signup: everything routes through a sales conversation, which slows down evaluation; The platform assumes real engineering and research infrastructure (GitHub, cloud providers, data warehouses) to deliver value; Positioning has shifted quickly toward autonomous agent sessions, so teams wanting only labeling workflows may find it heavier than needed

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

## Realyst

Realyst is a browser extension built for people who evaluate single-family homes and want the numbers without leaving the listing page. Once installed, it adds its own analysis layer on top of Zillow.com, so a normal property page also shows a running model of what that home would look like as a rental: rent assumptions, mortgage terms, vacancy allowance, renovation estimates and the resulting return metrics. The pitch is speed — investors and realtors can screen listings far faster than rebuilding a spreadsheet for every address.

The extension leans on a configurable financial model rather than a single canned output. You adjust inputs such as expected rental rate, mortgage rate, vacancy rate and renovation budget, and the cash-on-cash return and ROI figures update to match your assumptions. A neighbourhood comparison view can line up the profitability of up to 80 nearby properties at once, which makes it easier to spot the outliers in a subdivision before scheduling showings. Results can be saved and exported to CSV or PDF for later review or sharing with a partner or client.

Realyst also layers in AI assistance: five GPT-4 powered prompts cover things like renovation estimates and neighbourhood market trends, and homeowners and buyers get a plain-language PDF summary with nearby comparables plus property, neighbourhood and demographic context. A two-day free trial is offered through in-app purchase, and the extension is distributed on the Chrome Web Store.

**Pricing & Plans:** Free two-day trial via in-app purchase; continued use requires a paid subscription (pricing shown in-app).

- **Adjustable financial model:** Tune rental rate, mortgage rate, vacancy rate and renovation estimates to see how each assumption moves cash-on-cash return and ROI.
- **Neighbourhood-level comparison:** Compare the projected profitability of up to 80 nearby single-family properties in a single click to spot the strongest candidates fast.
- **GPT-4 powered insight prompts:** Five built-in AI prompts generate quick takes on renovation costs, market trends and other decision inputs that are awkward to look up manually.
- **Export and reporting:** Save results and export to CSV or PDF, and generate a homeowner/buyer summary that includes comps, neighbourhood and demographic context.
- **Zillow-native workflow:** Everything runs on the listing page you are already viewing, so there is no separate app to open or address to re-enter.

**Strengths:** Analysis appears directly on Zillow, so screening requires almost no extra steps; Inputs such as rent, vacancy and mortgage rate are editable instead of fixed; Neighbourhood comparison over many properties speeds up shortlisting; Exports to CSV and PDF make results easy to share or archive

**Limitations:** Only works on Zillow listings for single-family homes; Insight quality on any given address depends on Zillow's underlying data; Free trial is short and pricing must be checked inside the extension

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

## Frequently Asked Questions

### How do Labelbox and Realyst compare in monthly traffic?

Labelbox has 762,803 monthly visits, while Realyst has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Labelbox has a month-over-month growth rate of -22.8%; Realyst has —.

### How does pricing compare?

Labelbox: Contact for Pricing. Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases. Realyst: Free Trial. Free two-day trial via in-app purchase; continued use requires a paid subscription (pricing shown in-app).

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Streamlines end-to-end computer vision workflows from annotation to model deployment, but lacks Labelbox's general enterprise agent orchestration. | [.md](https://openaitools.app/tool/roboflow.md) |
| 2 | [Appen](https://openaitools.app/tool/appen-com) | 665,840 | +0.2% | Contact for Pricing | Relies heavily on managed human-in-the-loop workforce services for data validation, offering less self-serve technical tooling than Labelbox. | [.md](https://openaitools.app/tool/appen-com.md) |
| 3 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | Delivers enterprise-grade labeling and model evaluation at massive scale, but often requires higher enterprise budgets than Labelbox. | [.md](https://openaitools.app/tool/scale.md) |
| 4 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Offers robust multimodal annotation and workflow automation, featuring slightly more intuitive data curation pipelines for computer vision teams. | [.md](https://openaitools.app/tool/super-annotate.md) |
| 5 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Track, evaluate, and ship AI models and agents in one connected environment. | [.md](https://openaitools.app/tool/prompts.md) |
| 6 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | Specializes in advanced computer vision and physical AI data pipelines, though with a narrower focus than general text-and-image labeling. | [.md](https://openaitools.app/tool/encord-com.md) |
| 7 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Provides a flexible open-source alternative for custom data labeling, but requires self-hosting and manual infrastructure maintenance. | [.md](https://openaitools.app/tool/label-studio.md) |
| 8 | [Confident AI](https://openaitools.app/tool/confident-ai) | 83,400 | -3.3% | Freemium, Paid, Contact for Pricing | Focuses strictly on LLM evaluation, testing, and observability rather than raw data annotation and training dataset curation. | [.md](https://openaitools.app/tool/confident-ai.md) |
| 9 | [reAlpha](https://openaitools.app/tool/realpha) | 43,871 | -4.6% | Free | AI-powered, zero-commission homebuying with a virtual agent | [.md](https://openaitools.app/tool/realpha.md) |
| 10 | [BasicAI Cloud](https://openaitools.app/tool/basicai-cloud) | 21,670 | -13.1% | Freemium, Enterprise Quote | Provides efficient smart annotation tools and managed labeling services, but has a smaller ecosystem for complex enterprise agent workflows. | [.md](https://openaitools.app/tool/basicai-cloud.md) |

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Source: https://openaitools.app/compare/labelbox-vs-realyst

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