# Dialpad vs Labelbox

Dialpad records 2,763,687 monthly visits with +10.7% month-over-month growth and Paid pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [Dialpad](https://openaitools.app/tool/dialpad) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://www.dialpad.com | https://labelbox.com |
| Categories | AI Customer Service, AI Sales Assistant, AI Call Center, AI Coaching | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Paid | Contact for Pricing |
| Monthly Visits | 2,763,687 | 762,803 |
| MoM Growth Rate | +10.7% | -22.8% |
| Primary Market | United States | United States |
| Direct Visits | 1,999,735 | 370,024 |
| Review Score | 4.4 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Dialpad

Dialpad positions itself as an AI platform for customer experience, built around the idea that customer signals are scattered across channels and mostly go unheard. Instead of treating phone, video, messaging, and the contact center as separate tools, Dialpad connects them so every interaction shares context. Its agentic AI layer, Dialpad Resolve, can resolve routine work, escalate to humans with context intact, and learn from each outcome so the next interaction is smarter.

The platform spans four main products: Resolve for AI voice and digital agents, Support for a voice and digital contact center with analytics and workforce management, Sell for outbound sales with real-time coaching, and Connect for team collaboration across voice, video, and messaging. Real-time transcription, AI Recaps, live coach, AI scorecards, sentiment analysis, and CSAT scoring sit across these products, and integrations with Salesforce, Zendesk, Microsoft Teams, Google Workspace, HubSpot, and others keep context flowing into existing systems.

Dialpad targets businesses from small teams to large enterprises, emphasizing governed AI, unlimited scale, and enterprise-grade security and reliability. It is a strong fit for support and sales organizations that want AI woven into everyday communication rather than bolted on, and pricing is sales-led with quotes based on seats and product mix.

**Pricing & Plans:** Paid, sales-led pricing. Dialpad does not publish fixed self-serve tiers; costs depend on which products you use (Connect, Support, Sell, Resolve), seat count, and add-ons. A free trial and demo are available upon request.

- **Agentic AI with Dialpad Resolve:** Prebuilt and custom AI agents handle voice and digital support, take action with context, and escalate to humans when judgment is needed.
- **Unified voice, video, and messaging:** One collaboration app brings calls, meetings, and messages together so teams work with full context instead of switching tools.
- **AI contact center and analytics:** Real-time transcription, sentiment analysis, AI scorecards, CSAT, and workforce management help teams understand and improve every conversation.
- **Sales coaching and outbound tools:** Real-time coaching, power dialing, and automated follow-up help sales teams move faster and close more deals.
- **Deep integrations and governance:** Connects with Salesforce, Zendesk, Microsoft Teams, and more, with enterprise controls for building, testing, and governing AI.

**Strengths:** Combines agentic AI, contact center, sales, and team communications on one platform; Real-time transcription, coaching, and analytics improve both humans and AI over time; Broad integrations with Salesforce, Zendesk, Microsoft Teams, Google, and HubSpot; Scales from small teams to large enterprises with governance controls

**Limitations:** Pricing is sales-led, so costs are less transparent than self-serve plans; The full value requires adopting multiple products and integrations; Small teams may find the platform broader than they need

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

## 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)

## Frequently Asked Questions

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

Dialpad has 2,763,687 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Dialpad has a month-over-month growth rate of +10.7%; Labelbox has -22.8%.

### How does pricing compare?

Dialpad: Paid. Paid, sales-led pricing. Dialpad does not publish fixed self-serve tiers; costs depend on which products you use (Connect, Support, Sell, Resolve), seat count, and add-ons. A free trial and demo are available upon request. 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.

## 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 | [JustCall](https://openaitools.app/tool/justcall) | 780,285 | +11.8% | Paid | Strong focus on sales workflows and CRM integrations, but less robust built-in native voice analytics than Dialpad. | [.md](https://openaitools.app/tool/justcall.md) |
| 3 | [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) |
| 4 | [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) |
| 5 | [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) |
| 6 | [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) |
| 7 | [Glassix](https://openaitools.app/tool/glassix) | 300,739 | +6.6% | Free Trial, Paid, Contact for Pricing | Unified omnichannel inbox with strong AI agent deployment across voice and chat, but a smaller user community. | [.md](https://openaitools.app/tool/glassix.md) |
| 8 | [MiiTel](https://openaitools.app/tool/miitel-com) | 276,982 | +10.8% | Free Trial, Paid | Superior conversation scoring and voice pitch analytics for sales reps, but narrower omnichannel customer service features. | [.md](https://openaitools.app/tool/miitel-com.md) |
| 9 | [CallHippo](https://openaitools.app/tool/callhippo) | 273,071 | +16.9% | Free Trial, Paid | Easier setup for small teams needing virtual phone numbers, but lacks Dialpad's advanced AI voice intelligence suite. | [.md](https://openaitools.app/tool/callhippo.md) |
| 10 | [[24]7.ai](https://openaitools.app/tool/247-inc-com) | 273,001 | -4.5% | Contact for Pricing | Robust enterprise-grade conversational AI automation, but geared toward massive deployments rather than mid-market teams. | [.md](https://openaitools.app/tool/247-inc-com.md) |

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

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