Nexus vs Dify: AI App Builder vs Agents That Run Business Processes
Dify helps build AI apps and agent workflows. Nexus deploys agents that complete critical processes at scale with FDEs and built-in governance. Compare both.
Last updated: February 2026
Quick honest summary
Dify is one of the most popular open-source platforms for building LLM applications and AI agents. With 130k+ GitHub stars, a visual workflow editor, RAG capabilities, and flexible self-hosted or cloud deployment options, it has built a significant developer community. It is, at its core, an AI app builder: a strong environment for assembling chatbots, RAG pipelines, and agent workflows through a visual interface. If your team wants to experiment with agent architectures, prototype AI applications, or build on an open-source foundation with full code-level control, Dify is a strong starting point.
Nexus is an enterprise agent platform paired with Forward Deployed Engineers (FDEs), white-glove support, and a deployment model designed to get AI agents into production running critical business processes at scale. It is not just software, and it is not just a builder. It is a solution: platform plus dedicated engineering support, change management, and ongoing optimization. Where Dify helps you build an AI app, Nexus deploys agents that complete deep, multi-system business workflows with full governance and compliance from day one. The platform connects to 4,000+ enterprise systems and deploys agents across Slack, Teams, WhatsApp, email, phone, and web. Every engagement starts with a 3-month proof of concept tied to measurable business outcomes.
The difference between the two is not primarily about features. Both can build AI agents. The difference is about what happens after you build the agent, and how deep into your business processes the agent can actually go. Dify is fundamentally an AI app builder: it excels at creating chatbots, RAG pipelines, and agent workflows through a visual interface, but the depth of complex, multi-system enterprise automation it can handle is limited by that builder paradigm. There is a real gap between "building an agent" and "deploying agents that run critical business processes at scale." Nexus sits on the other side of that gap. It gives you a partner accountable for getting agents into production across your organization, with the governance, compliance, and organizational change support that enterprise deployment requires. Nexus agents go beyond the app-builder model: they execute deep business processes end-to-end, make autonomous decisions within your governance guardrails, and handle the exceptions and edge cases that break simple workflows. Forward Deployed Engineers embed with your team to ensure the agents actually work in your operational reality, not just in a sandbox. For teams with strong engineering resources who want full control over a bounded AI application, Dify's model works well. For enterprises where the challenge has shifted from "can we build it?" to "how do we deploy agents that run critical business processes at scale, with governance and adoption?", the models diverge significantly.
Side-by-side comparison
| Dimension | Dify | Nexus |
|---|---|---|
| What it is |
|
|
| Who builds agents |
|
|
| Deployment model |
|
|
| Support model |
|
|
| Agent architecture |
|
|
| Exception handling |
|
|
| Governance and compliance |
|
|
| Integrations |
|
|
| Knowledge and RAG |
|
|
| Pricing |
|
|
| Best for |
|
|
When Dify is the better choice
Dify is genuinely strong in several scenarios, and it is worth being direct about where the builder paradigm is the right fit:
-
Your team wants to experiment and prototype with full code-level control. If you are in the early stages of understanding what AI agents can do, building proofs of concept, testing different LLM configurations, or exploring agent architectures (ReAct, Function Calling, chain-of-thought), Dify's open-source foundation and visual workflow editor make it an excellent environment. No cost commitment, fast iteration, and full visibility into the underlying code.
-
You have dedicated AI/ML engineers who want to own the full stack. If your team has engineers comfortable managing Docker deployments, handling Kubernetes scaling, maintaining vector databases, debugging production agent behavior, and building custom integrations, and they have the bandwidth to do this alongside their other work, Dify's self-hosted model gives you complete control over every layer. The open-source community (460+ contributors) also means you can inspect, modify, and extend the codebase.
-
Budget is the primary constraint and engineering time is available. Dify's open-source core is free to self-host. For teams willing to invest engineering hours instead of dollars in infrastructure management, deployment, security hardening, compliance setup, and ongoing maintenance, this is a real advantage. The trade-off is clear: time and expertise in exchange for cost savings.
-
You are building a single, well-defined AI application with technical ownership. If you have one specific AI app to build (a chatbot, a RAG-powered search tool, a simple agent workflow), your engineering team will own it end-to-end, the use case does not require deep cross-system enterprise process execution, and compliance frameworks are not a hard requirement, Dify handles this well. The builder paradigm is well-suited for bounded, well-scoped problems where the agent does not need to orchestrate across many systems or handle complex business logic autonomously.
-
You want to contribute to and build on open-source foundations. Dify's open-source model means you benefit from community improvements and can contribute back. For organizations with a philosophical commitment to open source, or those building products on top of agent infrastructure, this matters.
When Nexus is the better choice
Enterprises that partner with Nexus tend to share a specific pattern: they have already experimented with open-source tools, developer frameworks, or AI app builders like Dify. They built something that works in a sandbox. Then they hit a wall when trying to move from a working prototype to agents running critical business processes at enterprise scale. The technology worked. The organizational deployment did not. The app-builder architecture hits a ceiling when work requires orchestrating across enterprise systems, making real business decisions, handling complex exceptions, and executing with governance. There is a structural gap between "building an agent app" and "deploying agents that complete deep business processes reliably." That gap is where Nexus operates.
-
You need agents that run critical business processes, not just agent apps that work in a demo. Many teams can build a working agent in Dify or a similar builder. The agent answers questions, retrieves documents, follows a workflow. But deploying that agent to complete real, multi-system business processes across an organization with proper governance, compliance, change management, and adoption is a fundamentally different challenge. Nexus is built specifically for this gap between building and operating. Forward Deployed Engineers embed with your team to handle integration complexity, design agents for your specific business reality (not generic templates), and run pilots without requiring your internal resources. Change management support ensures the organization actually adopts what you build. This is why Nexus maintains a 100% POC-to-contract conversion rate: every pilot delivers measurable value before you commit.
-
Business teams need to own the agents, not depend on engineering queues. Dify requires engineering to build, deploy, maintain, and debug agent apps in production. That creates a bottleneck: the people who understand the business processes are not the people who can build and maintain the agents. This is the same challenge enterprises face with other developer frameworks like LangGraph and CrewAI. Nexus closes that gap. Business teams, the people who understand the workflows, define and own the agents. FDEs handle the deployment complexity. At a European consulting firm (400+ employees), non-technical teams built five different agents across their entire consulting lifecycle: interviews, proposals, staffing, CVs, and HR support. No engineering queue. No IT backlog. Proposal turnaround went from days to hours. Tens of thousands of hours freed monthly.
-
Compliance and governance are requirements, not nice-to-haves. If you are in a regulated industry or a public company, "we self-host and manage our own compliance" means your team carries the full burden of building and maintaining audit trails, decision traceability, access controls, regulatory reporting, and security certifications on top of Dify's open-source app builder. That is a substantial project in itself, and it is entirely separate from building the agent. Nexus provides SOC 2 Type II, ISO 27001, ISO 42001, and GDPR compliance out of the box, with audit trails built into every agent interaction and decision logic that is fully traceable. Governance is not layered on top of the agent; it is woven into how the agent executes business processes. A multi-billion euro telecom operator deployed agents for support, compliance, and registration using Nexus. Full regulatory compliance across millions of interactions. 40% of support capacity freed. 12-week deployment. FDEs ensured compliance requirements were met from day one, not retrofitted after launch.
-
Your agents need to execute processes that span dozens of enterprise systems. This is where the app-builder paradigm breaks down most clearly. Connecting an agent to one API is straightforward in Dify. But a business process that touches your CRM, ERP, ticketing system, communication platforms, legacy databases, and custom internal tools, and that needs to maintain those connections reliably in production as systems change, goes well beyond what a builder tool is designed for. Dify's plugin ecosystem is growing, but most enterprise system integrations still require custom development, and orchestrating across all of them for a single business process is engineering-intensive. Nexus connects to 4,000+ enterprise systems natively. One agent, multiple systems, zero custom integration code. FDEs handle the integration complexity during deployment so your team does not carry that burden.
-
You need someone accountable for business outcomes, not just a builder tool. Dify gives you software to build agent apps. Nexus gives you a partnership to deploy agents that run business processes, which is central to the build vs buy decision. Forward Deployed Engineers are real engineers embedded in your organization. They help identify the highest-impact processes for agent deployment, design agents for your specific workflows, handle integration complexity, and run pilots without requiring your internal resources. Every engagement starts with a 3-month proof of concept tied to specific, measurable business outcomes. You see the math before committing, and you can exit anytime.
What enterprises experienced
Orange Group: enterprise-grade deployment in 4 weeks
Orange, a multi-billion euro telecom with 120,000+ employees, had every option available: internal engineering, open-source app builders like Dify, enterprise AI assistants, external agencies. Their engineering team could have assembled agent workflows in any visual builder. They chose Nexus because the gap between building an agent app and deploying agents that run critical business processes at enterprise scale required more than a toolkit. Their business team (not engineering) built autonomous customer onboarding agents deployed across multiple European markets. 4-week deployment timeline. 50% conversion improvement. $4M+ incremental yearly revenue.
The deployment model made the difference. Forward Deployed Engineers handled integration complexity. Change management ensured adoption. The result: 100% of the team uses the agents daily because the agents live inside the channels they already work in. No new tools to learn. No infrastructure to manage. No engineering maintenance burden.
Building the agent was never the hard part. Any capable team can assemble an agent app. Getting that agent into production running a critical business process at enterprise scale, with governance, adoption, and measurable financial outcomes, was the challenge. That is the gap between a builder and a deployment partner, and it is the gap an open-source app builder alone does not close.
Lambda.ai: a $4B+ AI company chose to buy, not build
Lambda is a $4B+ AI infrastructure company with 500M+ ARR. They employ world-class AI engineers. If any company could build and deploy agents using open-source tools, it is Lambda. AI is literally their business.
Their Head of Sales Intelligence, Joaquin Paz, had explored both approaches before Nexus. Open-ended AI agents (like ChatGPT Deep Search) were intelligent but inconsistent: same question, different results every time. Traditional automation tools and builder platforms were reliable but rigid: heavy hard-coding, extensive upfront configuration, brittle integrations. Neither worked for a deep, multi-system business process like enterprise-grade sales intelligence.
Joaquin, who has no engineering background, built Lambda's research agent on Nexus in days, not months. The result: $4B+ pipeline visibility, 24,000+ hours of research capacity added annually (equivalent to 12 full-time analysts), 12,000+ accounts monitored with deep intelligence. Projected value: $7M+ by 2026. Lambda is now expanding from a single agent to an agent fleet across sales and marketing operations.
In Joaquin's words: "I'm not an engineer. I built this in days. With the automation tools we looked at before, I would have needed to spec everything out and wait months for development."
The question for any enterprise considering the app-builder path: if Lambda, with AI as their core competency, chose not to build, what is your opportunity cost?
A European consulting firm: five agents, one platform, no engineering
A European consulting firm (400+ employees) needed to automate workflows across their entire consulting lifecycle, not a single use case but an agent fleet. They built five agents on Nexus: an interview agent that conducts and evaluates candidate interviews, a CV generator that converts recordings and LinkedIn profiles into standardized PowerPoint CVs, a project matchmaker that optimizes consultant allocation, a proposal copilot that generates full proposals from client requirements, and an HR agent that handles employee questions and escalations.
Proposal turnaround went from days to hours. Tens of thousands of hours freed monthly. Every hour freed from non-billable work translated directly to revenue.
The key: none of this was built by engineers. Business teams designed and deployed every agent, with FDEs handling integration and deployment complexity. With an open-source app builder, each of these five agents would have required engineering resources to build, integrate, test, deploy, and maintain. That is five times the engineering burden, and five agent apps that still would not have executed deep business processes the way Nexus agents do. With Nexus, the business teams who understood the workflows owned the entire process.
Key differences explained
Open-source app builder vs. enterprise deployment model (platform + FDEs + service)
This is the fundamental distinction, and it matters more than any feature list.
Dify gives you open-source flexibility to build AI applications. You can self-host, inspect the code, customize, extend, and assemble chatbots, RAG pipelines, and agent workflows. For technical teams that want full stack control over a bounded AI app and have the resources to manage everything from Docker infrastructure to production monitoring to compliance, that flexibility is genuinely valuable. The community is active, the plugin ecosystem is growing, and MCP protocol support extends the platform's reach. But it is still a builder. The output is an AI application. Getting that application to run a critical business process across your enterprise, with governance, adoption, and reliability, is a separate challenge that the builder paradigm does not address.
Nexus gives you an enterprise deployment model designed to cross that gap. It is not just a platform; it is a solution. Forward Deployed Engineers embed with your team. Change management is built into every engagement. Compliance is handled from day one. The platform is designed not just to build agents, but to deploy agents that complete deep business processes across an organization and keep them running reliably. Deploying AI at scale is 10% technology and 90% organizational change. Nexus is built for both.
The question is: what is the constraint your team is actually facing? If it is flexibility and code-level control for a bounded AI app, Dify addresses that well. If it is deploying agents that run critical business processes at enterprise scale, with governance, adoption, and someone accountable for outcomes, that is a different problem entirely. Dify is designed for building AI applications. Nexus deploys agents that own entire business processes from trigger to resolution. And that is the problem Nexus is built to solve.
Community support vs. Forward Deployed Engineers
Dify has an impressive open-source community: 130k+ GitHub stars, active Discord, 460+ contributors, regular releases. For common issues, documentation and community answers are available. The enterprise plan adds priority support and SSO.
For enterprise-specific challenges, the community model has inherent limitations. Complex integrations with legacy systems, edge cases in production at scale, organizational adoption across departments, compliance requirements for regulated industries, and the gap between a working app and a running business process: these are problems that community forums and documentation do not solve. Your team is ultimately on its own.
Nexus operates on a fundamentally different model. Forward Deployed Engineers are real engineers embedded in your organization. They help identify the highest-impact business processes for agent deployment (not guessing based on templates), design agents that fit your specific operational reality, handle integration complexity so your team does not have to learn a new platform, and run pilots without requiring internal resources. FDEs are the bridge between "we built an agent" and "agents are running our critical processes." This is paired with change management guidance, because the best agent that nobody uses is worthless.
This is why Nexus maintains a 100% POC-to-contract conversion rate. Every engagement is designed to deliver measurable value before you commit. Lambda's Head of Sales Intelligence said it directly: "We got both: intelligent and consistent." That outcome required more than software.
Self-managed infrastructure vs. managed enterprise platform
When you self-host Dify, you control your data and your infrastructure. That is a real advantage for data sovereignty and teams with strong DevOps capabilities. But it also means your team owns the entire operational burden: Docker/Kubernetes deployment and scaling, database management (the default Docker Compose setup lacks enterprise features like high availability and disaster recovery), vector database maintenance, security hardening, upgrade management, and production monitoring.
At prototype scale, this is manageable. At enterprise scale with production SLAs, multiple agents, thousands of users, and regulatory requirements, infrastructure management becomes a significant ongoing investment. Several deployment guides and community discussions highlight the complexity of configuring multi-container setups, persistent volumes, and enterprise-grade database configurations for production Dify deployments.
Nexus is fully managed. Infrastructure, scaling, security, upgrades, and production monitoring are all handled. Your team focuses on deploying agents that run business processes and deliver outcomes, not on maintaining the platform underneath. FDEs ensure the infrastructure scales with your agent deployment, so the operational burden never lands on your team. This is the same gap that separates self-serve agent builders like Relevance AI from enterprise deployment solutions.
Self-managed compliance vs. built-in governance
Both platforms reference enterprise compliance capabilities. Dify's enterprise tier includes SOC 2 reports, SSO integration (SAML, OIDC, OAuth2), and audit logging. When you self-host, you control your data entirely, which is valuable for data sovereignty.
The distinction is in the compliance burden. With Dify, your team is responsible for configuring and maintaining compliance infrastructure, building decision traceability into your agent workflows, ensuring audit trails meet regulatory requirements, managing access controls across your organization, and validating that the compliance capabilities meet your specific procurement requirements. Independent reviewers have recommended that organizations request current attestations directly from Dify during procurement rather than relying solely on marketplace listings.
Nexus provides SOC 2 Type II, ISO 27001, ISO 42001, and GDPR compliance out of the box. Every agent interaction generates audit trails automatically. Decision logic is traceable by design. Role-based access controls are built into the platform. For enterprises where compliance is a requirement (not an afterthought), this difference is significant. In an app builder, governance is something you configure after building the app. In Nexus, governance is woven into how agents execute business processes. At Orange, when the agent can confidently approve, it approves. When uncertain, it escalates to the sales person with full context. Every step is visible. Every decision is logged. Result: 100% compliance alongside 50% conversion improvement. Governance woven into the work itself, not layered on top.
Engineering-dependent vs. business-team ownership
Dify is built for developers. The visual workflow editor lowers the barrier to entry for building AI apps, but the platform relies on concepts familiar to technical users: managing arrays, assigning variables, handling JSON outputs, configuring ReAct or Function Calling strategies. Production deployment, maintenance, integration management, debugging, and infrastructure operations all require engineering resources. As you scale from one agent app to many, engineering becomes a bottleneck, and the builder paradigm means each new agent is another engineering project.
Nexus is built for business teams to own the outcome. Non-technical users define agents step-by-step: objectives, behaviors, decision logic, data connections, deployment channels. No code required. At Lambda, the sales operations team, not engineering, built and owns the research agent that monitors 12,000+ accounts. Joaquin Paz has no engineering background. At Orange, the business team deployed customer onboarding agents in 4 weeks without engineering dependency. At a European consulting firm, non-technical teams built five agents across their entire consulting lifecycle.
This distinction compounds as you scale. One agent app maintained by engineering is manageable. Five agents, ten agents, an agent fleet running critical processes across multiple departments: that becomes an engineering headcount problem with a builder like Dify, and a business enablement capability with Nexus. FDEs scale with the engagement, handling the increasing complexity so your business teams stay focused on outcomes. Lambda's expansion from one agent to a fleet across sales and marketing illustrates this trajectory.
App-builder workflows vs. agent-first process execution
Dify organizes work around workflows in its builder. You design a flow, connect tools and LLM calls, define conditions and branches. This is effective for well-defined, predictable agent apps. When exceptions occur (unexpected data formats, new edge cases, system changes), the workflow typically needs to be redesigned by a developer. The builder paradigm works well for bounded problems, but it limits how deeply agents can penetrate complex business processes where conditions change and exceptions are routine.
Nexus uses an agent-first architecture where agents are the control layer for deep process execution. Agents understand your business logic, execute it reliably across multiple systems, and adapt when reality does not match the template. When something unexpected happens, the agent either handles it intelligently or escalates with full context. The agent is not following a rigid flow built in a visual editor; it is reasoning within your business constraints and making decisions within your governance guardrails.
This matters in production at enterprise scale, where edge cases are the norm, not the exception. A European consulting firm built five different agent types on Nexus: conversational agents (interviews), workflow-driven agents (proposals, CVs), background automation agents (project matching), and hybrid agents (HR support with escalation). One platform, five different agent architectures, each executing deep business processes. That range of process depth is not something a visual app builder is designed to support.
Frequently asked questions
Can I use Dify for prototyping and then move to Nexus for production?
Yes, and some teams follow this path. Dify is excellent for early experimentation: understanding what AI agents can do, testing different LLM configurations, building technical intuition about agent architectures within the builder. When the use case is validated and the challenge shifts from "can we build an agent app?" to "how do we deploy agents that run critical business processes at enterprise scale with governance and adoption?", Nexus addresses that next phase. The transition involves moving from a prototype app to production-grade process execution with FDE support, not rebuilding the core agent logic from scratch.
We have strong engineering resources. Why would we need Nexus?
Lambda has world-class AI engineers and still chose Nexus. The reason was not capability; it was opportunity cost and process depth. They had explored building internally. Their engineers could have assembled agent apps in any builder or framework. But every engineering hour spent building and maintaining internal AI tools was an hour not spent on their core AI infrastructure product. And the business process they needed to automate (multi-system sales intelligence across 12,000+ accounts) went well beyond what an app builder could deliver. Nexus let their engineering team focus on what differentiates Lambda's business while FDEs handled agent deployment, integration, and optimization. The question is not whether your team can build an agent app. It is whether that app will actually run the critical business process you need it to, and what your engineers are not building while they try.
Is Dify really free? What is the hidden cost?
The open-source builder software is free to download and run. The costs that are not immediately visible: infrastructure to host and scale it (compute, storage, vector databases), engineering time to deploy, configure, and maintain agent apps in production, security hardening and compliance management, integration development and ongoing maintenance as enterprise systems change, DevOps effort for upgrades and monitoring, and the opportunity cost of engineering bandwidth. And even after paying those costs, the output is still an AI application, not an agent executing deep business processes with governance built in. For a small prototype or bounded use case, these costs are minimal. At enterprise scale with production SLAs and regulatory requirements, they add up quickly. Companies we work with often find the total cost of self-managing an app builder exceeds the cost of a managed enterprise platform with FDE support included.
How does pricing compare?
Dify is free to self-host (you pay for infrastructure and engineering time) or starts at $59/month for their Professional cloud plan ($159/month for Team, custom pricing for Enterprise). Nexus pricing is per-agent and tied to the value delivered. Every engagement starts with a 3-month POC tied to measurable outcomes. Orange generates $4M+ yearly revenue from agents deployed in 4 weeks. Lambda projects $7M+ in annual value from their agent fleet. You see the math before committing, and you can exit anytime.
We are worried about vendor lock-in with Nexus. Dify is open-source.
Vendor lock-in concerns are valid and worth addressing directly. Nexus agents connect to your systems via standard integrations; your data stays in your systems, not in Nexus. The platform supports any AI model, integrates with any tool, and your agent configurations are yours. You are not locked into a proprietary AI model or a closed ecosystem.
Open source gives you code-level control, which is genuinely valuable. But it also creates a different kind of dependency: reliance on internal engineering resources to build, maintain, and scale your agent infrastructure. Custom configurations, institutional knowledge about how your agents work, and infrastructure expertise often end up concentrated in a few engineers' heads. If those engineers leave or shift priorities, your agent infrastructure is at risk. Both models have trade-offs worth evaluating for your specific situation.
We tried building with open-source tools and it worked in our sandbox. Why would production be different?
This is the most common pattern we see, and it illustrates the gap between building and deploying. A sandbox proof of concept succeeds because conditions are controlled: clean data, single system, no compliance requirements, one or two people testing it, no organizational adoption challenge. The builder paradigm works well in that environment.
Running critical business processes at enterprise scale is different: messy data from multiple sources, dozens of interconnected systems, regulatory and compliance requirements, hundreds or thousands of users, edge cases that never appeared in testing, exception handling that needs to be reliable, and organizational adoption across teams who may be skeptical of AI. This is where the app-builder model hits its ceiling, because you are no longer building an app. You are trying to deploy an agent into the fabric of how your organization operates.
Lambda experienced this directly. They tried open-ended AI agents first (intelligent but inconsistent) and traditional automation and builders (reliable but rigid). Neither worked at the scale and process depth they needed. A multi-billion euro telecom operator spent six months trying to build production use cases with another platform (see our Copilot comparison for details) before deploying a dozen use cases with Nexus in 12 weeks. The technology was never the blocker. The gap between building agent apps and deploying agents into business processes was. FDEs, governance, and a deployment model designed for that gap made the difference.
What about Dify's enterprise plan? Does that close the gap?
Dify's enterprise plan adds important capabilities: SSO, priority support, higher usage limits, and a commercial license for self-hosting. These are meaningful improvements over the open-source community edition.
The gap that remains is not about software features. It is about what the platform is designed to do. Dify's enterprise plan makes it a better AI app builder. It does not make it a platform for deploying agents into critical business processes. Enterprise compliance certifications on paper are different from compliance woven into every agent interaction by design. Priority email support is different from Forward Deployed Engineers embedded in your organization designing agents for your specific processes. A self-hosted enterprise license is different from a managed platform with change management, ongoing optimization, and accountability for business outcomes. The enterprise plan makes Dify more enterprise-ready as builder software. It does not close the structural gap between building agent apps and running deep business processes at scale. Your team still carries the operational, compliance, and organizational change burden. FDEs are not a feature you can add to a builder.
Worth exploring?
If your team has been building AI apps with open-source tools and the challenge has shifted from "can we build an agent?" to "how do we deploy agents that run critical business processes at enterprise scale?", it might be worth seeing how enterprises with more resources, more engineering capacity, and more AI expertise than most still chose a deployment partner over the app-builder path.
Orange deployed agents into their customer onboarding process in 4 weeks. $4M+ yearly revenue. 100% team adoption. Lambda, a $4B+ AI company, chose to buy instead of build. $4B+ pipeline visibility. $7M+ projected annual value. In both cases, the difference was not the technology. It was the gap between building an agent app and deploying agents that complete deep business processes: Forward Deployed Engineers, change management, governance built in, and accountability for outcomes.
Every engagement starts with a 3-month proof of concept tied to specific outcomes. You see the math before committing. You can exit anytime.
Related comparisons
- Nexus vs LangGraph -- Developer framework comparison: code-first agent building vs. enterprise agent platform
- Nexus vs Zapier -- Rigid automation vs. intelligent agents that adapt to exceptions
- Nexus vs Microsoft Copilot -- AI assistants vs. autonomous agents that complete work
- Nexus vs Glean -- Enterprise AI that finds information vs. enterprise AI that completes work
- Back to all comparisons -->
Related comparisons
Your next
step is clear
Every engagement starts with a 3-month proof of concept tied to specific, measurable business outcomes. Forward Deployed Engineers embed with your team from day one.