StackAI vs Relevance AI

Finding real talent is harder than ever. Your HR team drowns in CVs while begging for budget increases to headhunt qualified candidates. Meanwhile, projects move slowly, with lost productivity mounting. The concept of an AI workforce is gaining traction, where you can supplement human talent with machine intelligence, breaking through the talent ceiling holding you back.

But what’s the best way to implement and scale? We’ll compare two platforms with different visions.

Relevance AI tapped into the AI workforce concept as a platform. You can build virtual team members with pre-determined routines, names, and job titles. They can automate tasks across sales, marketing, and operations, checking in as needed.

Stack AI rejects human replacement. It focuses on augmentation: it builds tools that empower creativity, analytical skills, and thoughtful decision-making. With generative AI workflow automation and AI agent tools, you’ll have more free time to align your team, pursue new ideas, and gain a competitive edge.

Let’s explore the best option for your business.

Why Stack AI is the best alternative to Relevance AI

Platform approach

Relevance AI focuses on the AI workforce concept

The first look into Relevance AI, right after you create your account.

Logging into Relevance AI for the first time, you can't miss their AI workforce concept. Each virtual worker has a lo-fi profile picture, a name, and a job description. Here's a shortlist:

• Apla, the Prospect Researcher, ready to run nuanced research for every account

• Lima, the Lifecycle Marketer, hyper-personalizing outbound emails

• Suni, the Intercom Support Agent, handles customer support

This character-oriented approach is easy to grasp, as it follows a human understanding of teams and hierarchies, where people assign tasks and capabilities to each person, attaching them to their identity.

These virtual employees can collaborate among each other. The Workforce feature set, now in beta, lets you connect multiple agents to streamline tasks, with automated handoffs from agent to agent. This is an early version of a multi-agent system, a more modular approach to AI agent task automation.

Stack AI builds AI-powered agents and tools

Stack AI’s AI agent builder interface, where you connect data sources, set instructions, and configure actions in a simple interface.

Stack AI focuses on building solutions for work problems, without personifying them. It’s a function-first approach that emphasizes outcomes.

You can build these solutions on the platform in two ways. The first is by setting up an AI agent, connecting your knowledge bases, and setting up actions to interact with your systems. Once active, these can react to messages and requests to carry out tasks flexibly.

The other is the AI workflow builder. You’ll connect nodes representing inputs, outputs, LLMs, and data sources on an open canvas, configuring each step. The process is intuitive and encourages experimentation, helping you build a tool that exceeds requirements.

While Relevance AI explores multi-agent systems, Stack AI focuses on perfecting single, powerful agents and workflows. This approach prioritizes simplicity and effectiveness, as multi-agent systems can introduce unnecessary complexity in setup, interaction, growth, and troubleshooting.

Ease of use and interface

Relevance AI is a no-code/low-code platform

One of the later steps of a content writer agent in Relevance AI, where you have to use a code expression to combine outputs.

Relevance AI requires a technical mindset when creating or customizing a project, despite its many templates and the AI workforce philosophy.

When editing existing templates, you’ll see some steps require transforming data with advanced logical expressions, editing JSON to structure inputs and outputs, and minor coding for full customization.

The user interface oscillates between easy-to-understand screens with accessible language and features described in technical language. While developers are comfortable in this setting, non-technical users will struggle.

There are two main ways to export agents and share them with your team. The first is as a chat UI or widget, providing an interface like ChatGPT, where you can make requests and see results. The second is the task UI, adding a task list on the left side of the screen to the chat window.

Stack AI is a no-code platform

Stack AI’s workflow automation canvas, where you can drop nodes, connect them, and build complex functionality without code.

Stack AI is a no-code platform that doesn’t require technical skills to learn, grow, and build tools. The user experience consists of visual interfaces with clear language, intuitive drag-and-drop elements, and the right amount of guardrails and error messages to help you deploy solutions consistently.

When you’re ready to share your project with your team, you can export it into one of the available pre-made interfaces, including:

• A chat interface that replicates the ChatGPT experience, with added security features.

• A form interface for one-time processing, ideal for reports and assessments.

• And the batch interface to process lists of data, running the project’s logic on every line, and provide the results as a downloadable CSV

Beyond these interfaces, you can set up WhatsApp/SMS interactions, Slack integrations, or interact programmatically via API.

Performance

Relevance AI is slower and designed to work in the background

The SEO content writer agent at work in Relevance AI.

When working with others, you generally don’t expect instant results. People will handle their tasks and update you later. Relevance AI seems to replicate this experience, making it slightly slower. For example, the SEO blog writer agent can take up to 10 minutes to complete its workflow.

It’s not an intentional speed limiter. Most templates show multiple AI steps, where the agent runs data through an LLM repeatedly to devise strategies, improve outputs, and format the results. This approach may impact agility and scalability in time-sensitive operations.

Stack AI is designed for the fastest results

All the knowledge base nodes use Stack AI’s proprietary search algorithm to retrieve business data efficiently.

With Stack AI, all parts of the platform are designed for performance in three key areas.

When building a chatbot for your data, Stack AI’s knowledge bases use a fast proprietary algorithm that retrieves the most relevant data chunks to generate an accurate response. This means precise answers without sacrificing speed. If you add many data sources with a lot of content, you may experience performance adjustments, but each search action is still optimized for efficiency.

Next, when processing data through LLMs, Stack AI negotiated its integrations and contracts with AI model providers for maximum uptime, performance, and data security—thanks to the large existing user base. This translates to consistent response times even during peak usage.

Lastly, the infrastructure for the editor and interfaces is designed to be lightweight and responsive, balancing powerful functionality with minimal latency. This enhances the user experience, particularly for time-critical applications where immediate results drive business value.

AI integrations

Relevance AI only integrates with a selection of top AI models

Relevance AI integrates with:

• OpenAI

• Anthropic

• DeepSeek

• And Hugging Face

While covering the top competitors, it misses other useful LLMs for specialized use cases. This limits flexibility for businesses with special AI requirements or preferences.

Stack AI integrates with all leading AI providers

Stack AI integrates with all leading AI providers. Here’s the current full list:

• Heavyweights like OpenAI, Anthropic, Google, and Meta

• Competitive challengers such as XAI, Mistral, and Perplexity

• Customized model platforms such as Hugging Face and Replicate

• AI optimization and specialized services such as Cerebras, Groq, and Together AI

• AI infrastructure for containerized deployment such as AWS Bedrock and Azure AI

• Integration with locally-run LLMs

This integration approach gives users flexibility to choose the best model for each task, rather than forcing a compromise.

Relevance AI charges a premium for using LLMs without API keys

Relevance AI uses a credits system to meter platform actions, covering the compute needed for each step and the tokens used for each LLM call.

If you don’t provide an API key for the LLMs, the platform charges a 20% premium in credit usage. If you don’t want to pay these overages, register for a developer account with the AI model provider and set up billing. This approach requires higher maintenance and provides less visibility into costs.

Stack AI includes AI token costs, no API keys required

All connections to AI providers are fully managed by Stack AI. You don’t need to create extra developer accounts, generate API keys, or pay credit or token costs: these are included in your plan.

The platform integrates with new LLMs as soon as they’re available, making it easy to maintain and upgrade as AI technology evolves. It also makes the cost structure more predictable for your budget.

Privacy and security

Relevance AI has SOC II certification but no self-hosting

Relevance AI offers SOC II certification, demonstrating their commitment to security. The platform also includes enterprise-grade features like Single Sign-On (SSO) and role-based access control (RBAC) to manage user permissions.

However, Relevance AI lacks self-hosting capabilities. This is a significant limitation for companies in regulated industries with strict data residency requirements. Organizations that need to keep sensitive data within their own infrastructure may find this constraint problematic for compliance.

Stack AI offers SOC II, GDPR, HIPAA certifications, and self-hosting

Stack AI provides a comprehensive security framework with SOC II, GDPR, and HIPAA compliance. The platform's security features extend beyond certifications to include:

• Single Sign-On (SSO)

• Role-based access control (RBAC)

• Response guardrails to prevent inappropriate inputs and outputs

• Personally identifiable information (PII) masking capabilities

• Self-hosting options for maximum data sovereignty

This robust security architecture suits enterprises with complex compliance requirements or those in highly regulated sectors.

Relevance AI vs Stack AI: which one is best?

Relevance AI offers an intuitive workforce concept with personified AI assistants that may appeal to teams wanting a character-based AI implementation approach.

Stack AI offers a tool-oriented approach with its no-code platform and focus on workflow automation. The platform emphasizes speed and performance, making it suitable for time-sensitive operations. Its broader AI model integration and inclusive pricing model that bundles token costs could simplify management for some organizations.

Both platforms offer SOC II certification, but Stack AI provides additional compliance options like GDPR, HIPAA, and self-hosting, crucial for businesses in regulated industries.

The interface differences are notable—Relevance AI uses a combination of no-code and low-code approaches that sometimes requires technical knowledge, while Stack AI maintains a consistent no-code environment throughout.

Consider your organization's technical capabilities, security requirements, and whether you prefer an AI workforce concept or a pure tool-based approach when making your decision. Both platforms offer valuable capabilities that can enhance productivity, but with different implementation philosophies.

Try out Stack AI’s ease of use and performance with a free account.