# Salesforce Agentforce (formerly Einstein) vs TypeSafe AI - Jev

Salesforce Agentforce (formerly Einstein) records 111,910,743 monthly visits with +2.2% month-over-month growth and Freemium pricing. TypeSafe AI - Jev records 6,148,688 monthly visits with -36.4% growth and Paid pricing.

| Metric | [Salesforce Agentforce (formerly Einstein)](https://openaitools.app/tool/salesforce-einstein) | [TypeSafe AI - Jev](https://openaitools.app/tool/typesafe-jev) |
| :--- | :--- | :--- |
| Website URL | https://www.salesforce.com/agentforce/ | https://typesafe.ai |
| Categories | AI App Builder, AI Developer Tools, AI CRM, No-Code&Low-Code | AI Models, AI Workflow, AI Agent |
| Pricing Model | Freemium | Paid |
| Monthly Visits | 111,910,743 | 6,148,688 |
| MoM Growth Rate | +2.2% | -36.4% |
| Primary Market | United States | United States |
| Direct Visits | 60,741,009 | 3,141,114 |
| Review Score | 4.4 | 4.2 |
| Saved Count | 20 | 0 |
| Supported Platforms | Website | Website |

## Salesforce Agentforce (formerly Einstein)

Originally launched as Salesforce Einstein for predictive CRM scoring and later Einstein Copilot for conversational assistance, Salesforce's flagship AI platform is now led by Agentforce. Built natively into the Salesforce Platform and grounded in unified enterprise data via Data 360 (formerly Data Cloud), Agentforce moves beyond rigid decision-tree chatbots and passive text generators to deploy autonomous AI agents capable of reasoning through multi-step problems and executing real business actions around the clock.

At the core of the platform is the Atlas Reasoning Engine, which evaluates incoming customer requests or system triggers, retrieves verified context from CRM records and external APIs, formulates an execution plan, and invokes Salesforce Flows, Apex classes, MuleSoft connectors, or verified Model Context Protocol (MCP) servers. In 2026, Salesforce expanded the platform with Long-Horizon Runtime—enabling agents to pursue multi-day workflows with human-in-the-loop oversight—alongside Multi-Agent Orchestration, Agentforce Voice for natural phone interactions beyond legacy IVR, and Agent Optimizer for real-time observability.

Organizations can launch pre-built role agents in clicks using the low-code Agent Builder or customize deterministic logic with Agent Script. Out-of-the-box roles include Customer Service Agents, autonomous Sales Development Representatives (SDRs) that qualify leads and book meetings, Sales Coaches for interactive deal role-play, Commerce Personal Shoppers, and Employee Coworker agents embedded directly inside Slack and Salesforce clouds.

**Pricing & Plans:** Existing Enterprise+ customers can start free via Salesforce Foundations ($0); usage-based options include Flex Credits at $500 per 100,000 credits ($0.10 per standard action, $0.15 per voice action), $2 per customer conversation, or $2 per Help Agent resolution, while unmetered employee seat add-ons cost $125–$150/user/month and Max (Agentforce 1) Editions start at $550/user/month.

- **Atlas Reasoning Engine & Long-Horizon Runtime:** Deconstructs complex queries into multi-step action plans grounded in Data 360, retaining state across days or weeks to complete multi-touch operational workflows with human escalation guardrails.
- **Low-Code Agent Builder & Agent Script:** Configure custom agents, subagents, natural-language instructions, and deterministic guardrails using existing Salesforce Flows, Prompt Builder templates, Apex code, and MuleSoft APIs.
- **Multi-Agent Orchestration & MCP Interoperability:** Coordinate specialized subagents across departments and connect securely to external enterprise tools through verified Model Context Protocol (MCP) servers from AgentExchange partners.
- **Agentforce Voice & Omnichannel Deployment:** Replace rigid telephone IVR trees with low-latency conversational voice agents alongside unified deployments across web chat, WhatsApp, SMS, mobile apps, and Slack.
- **Pre-Built Role Agents & Agent Optimizer:** Deploy ready-made Service, SDR, Sales Coach, Buyer, and Merchandiser agents out of the box, then monitor resolution accuracy and bottlenecks in near real time with Agent Optimizer.

**Strengths:** Deep native access to Salesforce CRM objects, Flows, Apex logic, MuleSoft integrations, and Data 360 without brittle third-party syncing.; Flexible 2026 billing models let enterprises choose between per-action Flex Credits ($0.10/action), $2 per conversation, $2 per resolution, or unmetered per-user licenses.; Enterprise-grade Einstein Trust Layer enforces zero data retention with external LLM providers, PII masking, and toxicity detection.; Supports open multi-agent interoperability via Model Context Protocol (MCP) and Long-Horizon multi-day task execution.

**Limitations:** Consumption pricing across Flex Credits, Data 360 credits, and seat add-ons is complex to forecast and prevents mixing Flex Credits and Conversation meters in the same org.; Requires clean, well-structured Salesforce CRM and Data 360 governance to avoid inaccurate agent actions.; Full enterprise capabilities carry a steep total cost of ownership, with top-tier Max / Agentforce 1 editions reaching $550 per user per month.

[Profile (Markdown)](https://openaitools.app/tool/salesforce-einstein.md)

## TypeSafe AI - Jev

TypeSafe AI takes the opposite research direction from mainstream chat models. Instead of optimising a language model for human preferences through RLHF — which the company argues introduces mode dropping, overconfidence and unreliability — it builds models designed to be consumed natively by machines. It calls this class System One Models and trains them with a new architecture, a new sampler and a new algorithm called Reinforcement Learning for Calibrated Decisions (RLCD). The first public member of this class is Jev, positioned for production automation.\n\nThe practical difference is in the output shape. LLMs produce text for people; Jev produces typed decisions that software can act on directly, each carrying a calibrated estimate of how confident the model is. That confidence signal is the core of the design: developers set thresholds for when the system acts autonomously and when it escalates for human review, then combine those decisions in code to build larger workflows. TypeSafe frames the results as reliable, fast, self-consistent and type-safe, and publishes benchmark comparisons showing Jev at roughly 193x faster and 444x cheaper than LLM workflows on System One tasks, with input pricing quoted at $42 per billion tokens.\n\nJev and the wider TypeSafe platform are aimed at technical teams building AI into products — screening, triage, classification and other places where a confident yes/no or judgement call is more useful than a paragraph. Access is currently via early access through the API console and documentation, and TypeSafe positions the offering as decision intelligence infrastructure for production software rather than another chat assistant.

**Pricing & Plans:** Paid, with early access to Jev. TypeSafe quotes Jev at $42 per billion input tokens and describes this as roughly 238x lower input pricing than comparable frontier chat models. Enterprise options and API access are arranged through the company, alongside documentation and an API console.

- **Typed decisions, not strings:** Jev outputs typed decisions that application code can act on directly, rather than prose that must be parsed and interpreted.
- **Calibrated confidence on every call:** Every decision carries an estimate of how confident the model is, so you can set thresholds for autonomous action versus human escalation.
- **Reported 193x faster, 444x cheaper:** TypeSafe reports Jev at roughly 193x faster and 444x cheaper than LLM-based workflows on comparable System One tasks, with a price of $42 per billion input tokens.
- **Built to reduce hallucination:** Because confidence estimates expose uncertainty, Jev is designed to avoid hallucination-style failures in automation, enabling zero-hallucination decision paths where confidence is high.
- **Composable workflows with human-in-the-loop:** Combine Jev's per-call decisions inside your own code to construct larger workflows, with explicit control over when intelligence acts and when it defers to a human.

**Strengths:** Purpose-built for machine consumers, not human chat; Calibrated confidence enables principled human-in-the-loop thresholds; Reported order-of-magnitude gains in speed and cost for decision tasks; Typed outputs remove the parsing and validation overhead of JSON-mode LLMs

**Limitations:** Early access — general availability and some product details are still being finalised; It is not a chatbot; teams expecting conversational output will need to restructure their workflows; Requires engineering effort to consume typed decisions and set confidence thresholds in your own code; Documentation and public pricing details are still evolving

[Profile (Markdown)](https://openaitools.app/tool/typesafe-jev.md)

## Frequently Asked Questions

### How do Salesforce Agentforce (formerly Einstein) and TypeSafe AI - Jev compare in monthly traffic?

Salesforce Agentforce (formerly Einstein) has 111,910,743 monthly visits, while TypeSafe AI - Jev has 6,148,688. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Salesforce Agentforce (formerly Einstein) has a month-over-month growth rate of +2.2%; TypeSafe AI - Jev has -36.4%.

### How does pricing compare?

Salesforce Agentforce (formerly Einstein): Freemium. Existing Enterprise+ customers can start free via Salesforce Foundations ($0); usage-based options include Flex Credits at $500 per 100,000 credits ($0.10 per standard action, $0.15 per voice action), $2 per customer conversation, or $2 per Help Agent resolution, while unmetered employee seat add-ons cost $125–$150/user/month and Max (Agentforce 1) Editions start at $550/user/month. TypeSafe AI - Jev: Paid. Paid, with early access to Jev. TypeSafe quotes Jev at $42 per billion input tokens and describes this as roughly 238x lower input pricing than comparable frontier chat models. Enterprise options and API access are arranged through the company, alongside documentation and an API console.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenAI Platform](https://openaitools.app/tool/openai) | 234,678,639 | -9.6% | Paid | Enterprise developer platform and API for deploying GPT-6 Astra, Sol, Luna, and realtime multimodal agents. | [.md](https://openaitools.app/tool/openai.md) |
| 2 | [Google AI Studio](https://openaitools.app/tool/google-ai-studio) | 112,620,020 | +2% | Freemium | Google's rapid prototyping IDE and developer API workbench for building multimodal applications with Gemini models. | [.md](https://openaitools.app/tool/google-ai-studio.md) |
| 3 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Offers access to diverse foundational models through one API, but lacks specialized typed decision scoring and confidence metrics. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 4 | [Zapier](https://openaitools.app/tool/zapier-com) | 5,065,248 | +0.4% | Freemium, Free Trial, Paid | Automate work across thousands of apps — no code required | [.md](https://openaitools.app/tool/zapier-com.md) |
| 5 | [Intercom](https://openaitools.app/tool/intercom-com) | 4,471,110 | +2.7% | Free Trial, Paid | Exceptional customer support and helpdesk workflow automation, but less suited for broad enterprise CRM and sales execution. | [.md](https://openaitools.app/tool/intercom-com.md) |
| 6 | [Gong – Revenue AI OS](https://openaitools.app/tool/gong-io) | 2,337,769 | +3% | Contact for Pricing | Superior revenue intelligence and call analytics, but specialized for sales insights rather than general agentic workflow automation. | [.md](https://openaitools.app/tool/gong-io.md) |
| 7 | [MindStudio](https://openaitools.app/tool/mindstudio) | 1,793,337 | +14.2% | Contact for Pricing | Visual no-code builder for custom AI agents, but lacks Salesforce's native enterprise data connectors and CRM structure. | [.md](https://openaitools.app/tool/mindstudio.md) |
| 8 | [Attio](https://openaitools.app/tool/attio) | 1,652,928 | -4.2% | Free Trial, Contact for Pricing | Modern customizable CRM with built-in AI agents, but lacks the massive enterprise ecosystem and scale of Salesforce. | [.md](https://openaitools.app/tool/attio.md) |
| 9 | [Chatbase](https://openaitools.app/tool/chatbase) | 396,383 | -7% | Freemium, Paid | Simple no-code AI chatbot deployment, but less equipped for complex enterprise-grade CRM automation and multi-system agents. | [.md](https://openaitools.app/tool/chatbase.md) |
| 10 | [Sierra](https://openaitools.app/tool/sierra) | 341,072 | +16.4% | Contact for Pricing | Delivers exceptionally polished customer service agents, but focuses on bespoke conversational AI rather than broad CRM workflows. | [.md](https://openaitools.app/tool/sierra.md) |

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Source: https://openaitools.app/compare/salesforce-einstein-vs-typesafe-jev

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