Nexus vs Haystack: RAG Framework vs AI Agent Platform

Quick honest summary

Haystack is an open-source AI orchestration framework built by deepset for developers who want to build production-ready RAG pipelines, semantic search, and AI agents in Python. It has 24,000+ GitHub stars, a clean component-based pipeline architecture, and strong retrieval capabilities out of the box. deepset (the company behind it) raised $45.6M+ from investors including Balderton Capital, GV (Google Ventures), and Harpoon, and offers the Haystack Enterprise Platform (formerly the deepset AI Platform) as a managed layer for teams that want deployment, monitoring, and governance without managing infrastructure themselves. Enterprise customers include Airbus, Siemens, and The Economist.

Nexus is an enterprise AI agent platform paired with white-glove service: Forward Deployed Engineers embedded with your team, change management support, and ongoing optimization. It is not just software you buy and figure out on your own. Nexus is built for enterprises that need agents completing business workflows in production, with business teams owning the outcome, not waiting on engineering.

The right choice depends on two questions: what are you building, and who is building it?

If you have a dedicated AI engineering team building RAG-heavy applications (document search, question answering, knowledge retrieval systems) as part of your product, Haystack is one of the strongest frameworks available. For a broader view of the build-vs-buy decision, see our enterprise analysis. Its pipeline-first architecture is particularly well-suited for retrieval use cases, and the modular component system makes it straightforward to swap in different models, document stores, and rankers. If the goal is internal business workflows (sales operations, customer support, HR, marketing) that span multiple enterprise systems and need to be in production in weeks rather than quarters, without creating a permanent engineering dependency, that is where Nexus fits.

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Side-by-side comparison

Dimension	Haystack	Nexus
What it is	Open-source Python framework Built for RAG pipelines, semantic search, AI agents Modular, composable components	Enterprise AI agent platform + embedded service Forward Deployed Engineers included Change management and ongoing optimization
Who builds and owns it	Engineering teams design, build, maintain pipelines in Python Requires knowledge of retrieval architectures Document stores and pipeline orchestration expertise needed	Business teams build and deploy agents with FDE support They own the outcome No permanent engineering dependency
Core strength	Retrieval and search Pipeline-first architecture Strong components for document processing, embedding, retrieval, re-ranking, generation	End-to-end business workflow automation Agents complete work autonomously across any enterprise system Intelligent escalation built in
Time to production	Weeks to months depending on complexity Pipeline design, component configuration, integration Infrastructure, monitoring, evaluation setup required	Days to weeks FDEs work alongside your team Handle configuration, integration, testing, deployment
Deployment model	Open-source framework (free) Haystack Enterprise Platform available Managed cloud, self-hosted, or on-premise options	3-month POC tied to measurable business outcomes Platform + embedded service See results before committing
Agent capabilities	Agent component for tool calling with LLMs Supports multi-step reasoning and tool invocation Still primarily optimized for retrieval-centric workflows	Agents as the primary unit Conversational, workflow-driven, or background automation Intelligent exception handling and escalation built in
Integrations	90+ integrations Model providers: OpenAI, Anthropic, Cohere, Azure 16+ document stores: Elasticsearch, Pinecone, Weaviate, Qdrant, pgvector Monitoring tools	4,000+ native integrations CRMs, ERPs, communication tools, productivity suites Deploy across Slack, Teams, WhatsApp, email, phone, web
RAG capabilities	Excellent, purpose-built for retrieval Real-time and stored RAG Multimodal pipelines, hybrid retrieval Re-ranking and evaluation	Built-in RAG (real-time and stored) Connects to CRMs, ERPs, knowledge bases, document stores Integrated but not the sole focus
Security and compliance	Enterprise Platform adds governance, access controls, observability SOC 2, ISO, GDPR compliance is your responsibility Beyond what the platform provides	SOC 2 Type II, ISO 27001, ISO 42001, GDPR Full audit trails, decision traceability Role-based access from day one
Support model	Community support (open source), documentation Enterprise Starter: 4 hrs/month technical consultation Direct support from core maintainers	Forward Deployed Engineers embedded with your team Change management guidance Ongoing optimization, white-glove partnership
Pricing	Framework is free Enterprise Platform and Enterprise Starter: custom pricing Based on organization size, contact deepset	Per-agent pricing tied to value delivered 3-month POC with measurable outcomes Decide before annual commitment
Best for	AI engineering teams building retrieval-heavy applications Semantic search and document QA RAG-first agent systems	Business teams needing production agents Enterprise workflows completed end-to-end Engineering-grade support without engineering dependency

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When Haystack is the better choice

Haystack is genuinely strong, and there are scenarios where it is the right call:

• You are building a RAG-first product. If your core product is document search, question answering, or knowledge retrieval, Haystack's pipeline architecture is purpose-built for this. Its modular design for retrieval, re-ranking, and generation gives developers fine-grained control over every step of the retrieval process. Few frameworks handle this as cleanly.

• You have a dedicated AI engineering team with retrieval expertise. Haystack gives developers composable pipeline components for building custom retrieval and generation systems. If your team has strong Python engineers who understand embedding models, vector databases, retrieval strategies, and ranking, Haystack provides the building blocks without unnecessary abstraction.

• Your use case is primarily search and retrieval. Semantic search across internal documents, customer-facing knowledge bases, technical documentation systems, compliance document retrieval. Haystack was originally built for search (deepset's roots are in NLP and enterprise search), and that heritage shows in the quality of its retrieval components.

• You want a production-ready framework with less "magic" than LangChain. Developers who have compared the two often note that Haystack's pipeline architecture is more explicit and predictable than LangChain's chain-based abstraction. The component system validates compatibility before runtime, and pipeline serialization makes it easier to save and reproduce pipelines. If you value clarity and explicitness in your retrieval stack, Haystack delivers.

• You want full control over your data and infrastructure. Haystack's open-source core means you can self-host everything. The Haystack Enterprise Platform also supports VPC, on-premise, and air-gapped deployment. For organizations with strict data sovereignty requirements, this matters.

• You need multimodal retrieval. Haystack supports pipelines that combine text, images, tables, and scanned documents. For use cases like processing invoices, analyzing PDFs with mixed content, or building search over heterogeneous document collections, the multimodal pipeline support is a genuine strength.

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When Nexus is the better choice

Enterprises that partner with Nexus tend to share a specific pattern: they evaluated developer frameworks (or tried building internally), realized the engineering investment was too high for internal business workflows, and chose a platform + service approach instead.

• Your engineering team is already stretched, and this is not their core product. Most enterprise engineering teams are juggling core product work, infrastructure, and a growing backlog. Asking them to build and maintain internal AI pipelines means those pipelines compete with revenue-generating product work. Lambda, a $4B+ AI infrastructure company with world-class engineers, ran this exact calculation and concluded: the opportunity cost is too high. Nexus removes the engineering dependency. Business teams build and deploy agents, supported by Forward Deployed Engineers.

• You need more than retrieval. You need complete workflow automation. Haystack excels at the retrieval layer: finding information, ranking results, generating answers from documents. But enterprise workflows rarely stop at retrieval. Even workflow automation tools that handle the execution side break on exceptions and judgment calls. They involve collecting data, validating against multiple systems, routing decisions, handling exceptions, escalating to humans, updating CRMs, sending notifications, and coordinating across departments. Nexus agents handle the entire workflow end-to-end, not just the retrieval step.

• You need production agents in weeks, not quarters. With Haystack, a production pipeline requires architecture design, component selection, document store setup, embedding pipeline configuration, evaluation, infrastructure, security, and monitoring. For retrieval use cases, this is manageable. For complex multi-system workflows, add integration development, exception handling, and deployment across channels. With Nexus, most agents go live within 2-6 weeks. A Forward Deployed Engineer works alongside your team from day one.

• Business teams need to own the agents, not file tickets with engineering. With Haystack, every modification requires engineering time: updated pipeline logic, new components, adjusted retrieval strategies, additional integrations. With Nexus, the business teams who understand the workflows own and iterate on the agents directly. When Lambda's Head of Sales Intelligence needed to adjust data sources or account segmentation, he did it himself. No engineering tickets. No backlog.

• Your workflows span dozens of enterprise systems, and you need native integrations. Haystack supports 90+ integrations, primarily focused on model providers, document stores, and monitoring tools. That is strong for building retrieval pipelines. But connecting to CRMs, ERPs, communication tools, ticketing systems, and custom APIs requires building and maintaining each integration individually. Nexus connects to 4,000+ enterprise systems natively and deploys across any channel: Slack, Teams, WhatsApp, email, phone, web.

• You want enterprise governance without building it yourself. Haystack Enterprise Platform adds observability, governance, and access controls. But enterprise compliance frameworks (SOC 2 Type II, ISO 27001, GDPR, full audit trails, decision traceability) require additional work beyond what the platform provides. Nexus ships with all of this from day one. For regulated industries and public companies, this is not optional.

• You need more than software. You need a partner. Most vendors sell software and disappear. Nexus embeds Forward Deployed Engineers with your team. They help identify the highest-impact use cases, design agents that fit your specific reality, handle integration complexity, run pilots, manage change, and optimize continuously. Deploying AI at scale is 10% technology and 90% organizational change. Nexus is built for that reality.

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What enterprises experienced

Lambda: a $4B+ AI company chose to buy instead of build

This is the proof point that matters most for anyone evaluating developer frameworks versus a platform approach.

Lambda, a $4B+ AI company with world-class engineers, evaluated building retrieval pipelines with Haystack and other frameworks before choosing Nexus's platform approach. They build supercomputers for AI training and inference. Their customers include some of the world's top AI labs. If any company had the engineering depth to invest months of build time into custom RAG pipelines and agent infrastructure, it was Lambda.

They chose to buy because the engineering investment in building beyond retrieval into full workflow automation could not be justified.

What they tried first: Lambda explored open-ended AI agents (like ChatGPT Deep Search) and traditional workflow automation. Open-ended agents were intelligent but inconsistent: same question, different results every time. Workflow automation was reliable but rigid: heavy hard-coding, brittle integrations, breaks when systems change. Neither worked for enterprise-grade sales intelligence.

What they built with Nexus: Joaquin Paz, Lambda's Head of Sales Intelligence, built an autonomous research agent that monitors 12,000+ enterprise accounts annually, identifies buying signals across dozens of data sources, and synthesizes competitive intelligence. The critical detail: Joaquin is not an engineer. He built this in days.

The results:

• $4B+ in cumulative pipeline identified across accounts Lambda was not actively monitoring
• 24,000+ research hours added annually (equivalent to 12 full-time analysts)
• 12,000+ enterprise accounts analyzed with deep intelligence
• Deployed in weeks, not the months it would have taken to build internally

"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."

"We looked at open-ended AI agents; they were smart but inconsistent. Same question, different answer every time. We looked at traditional automation; it was reliable but felt heavy, lots of hard coding. With Nexus, we got both: intelligent and consistent."

-- Joaquin Paz, Head of Sales Intelligence, Lambda

Why Lambda's CTO chose to buy: The opportunity cost of engineering time was too high. Every hour Lambda's engineers spent building internal pipelines was an hour not spent on their core product: AI cloud infrastructure for customers. They deployed in days what would have taken months internally.

Lambda has since expanded from a single agent to a fleet across sales and marketing. They are building what they call an "agentic layer," a persistent intelligent system across their go-to-market organization. Anticipated value: more than $7M by 2026.

Orange Group: business teams built agents in 4 weeks

Orange Group is a multi-billion euro telecom operator with 120,000+ employees across Europe and Africa. They have significant internal engineering resources and every option available: build internally, hire agencies, deploy enterprise AI assistants.

They chose Nexus. Their business team (not engineering) built customer onboarding agents deployed across multiple European markets. The results: 50% conversion improvement, $4M+ incremental yearly revenue, 100% adoption, and 100% compliance. Deployed in 4 weeks.

When the agent can confidently handle an onboarding step, it proceeds autonomously. When uncertain, it escalates to the salesperson with full context. Every step is visible, every decision logged. The business team owns the agents and iterates without engineering involvement.

European telecom: tried Copilot Studio for 6 months, zero production use cases

A multi-billion euro European telecom operator tried Microsoft Copilot Studio for 6 months. The result: zero production use cases deployed. In the same timeframe, they deployed a dozen use cases with Nexus. The difference was not just the technology. It was Forward Deployed Engineers who identified high-impact workflows, handled integration complexity, and managed organizational change. The platform alone would not have been enough. The combination of platform and embedded expertise made production deployment possible.

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Key differences explained

RAG framework vs. enterprise platform + service: different problems, different models

This is the core distinction.

Haystack is a retrieval-focused framework. It gives engineers composable components to build RAG pipelines, semantic search, and increasingly, AI agents. It is strong at what it does: document processing, embedding, retrieval, re-ranking, and generation. The pipeline architecture is clean and predictable. But the fundamental model is: your engineering team designs, builds, deploys, and maintains everything. The Haystack Enterprise Platform adds managed infrastructure, a visual pipeline editor, and governance tooling, but the core work of building and owning the application still falls on your team.

Nexus is a platform + service. Business teams (sales operations, customer support, marketing, HR) build and deploy agents that complete their workflows. The platform handles infrastructure, integrations, security, and compliance. Forward Deployed Engineers work alongside your team to identify use cases, design agents, handle complexity, and optimize over time. The business team focuses on outcomes, not pipeline architecture.

These are not just different products. They are different models for how AI gets deployed in an enterprise. Haystack assumes your engineering team will build and own it. Nexus assumes deploying AI at scale requires both a platform and embedded expertise, and that business teams should own what they build. See how all nine developer frameworks compare across the same dimensions.

Beyond retrieval: when the workflow is bigger than search

Haystack is optimized for the retrieval layer: getting the right information from the right documents at the right time. This is valuable. Many enterprise use cases start with retrieval.

But most enterprise workflows do not stop at retrieval. Consider a customer onboarding process: you need to collect customer information in real-time, validate data against CRM and billing systems, check product compatibility, route unusual cases to specialized teams, escalate complex issues with full context, update multiple systems, and send confirmations across different channels. The retrieval step (pulling relevant product information or customer history) is one component in a much larger workflow.

With Haystack, you would need to build each of these steps as custom components, integrate them with enterprise systems, handle exceptions in code, and manage the entire orchestration yourself. That is feasible, but it is a significant engineering project.

With Nexus, the agent handles the entire workflow. Orange built exactly this kind of customer onboarding agent and deployed it across multiple European markets in 4 weeks. Business teams own it. No engineering dependency.

The opportunity cost calculation: Lambda proved the math

The decision between a developer framework and a platform approach often reduces to a single question: what is the opportunity cost of your engineering team's time?

Building production-grade pipelines with Haystack is not just configuring retrieval components. It is designing the pipeline architecture, integrating with enterprise systems, building custom components for non-retrieval steps, implementing security and access controls, setting up evaluation and monitoring, managing document store infrastructure, and maintaining everything as systems change. For a single RAG pipeline, that is manageable. For a fleet of agents that span entire business workflows, it is a permanent engineering investment.

Lambda, with $4B+ in valuation, $500M+ ARR, and engineers who build AI infrastructure for a living, ran this calculation and concluded: the opportunity cost is too high. Every engineering hour spent building internal agents was an hour not spent on the core product.

This is the pattern we see. Companies evaluate developer frameworks, estimate the true engineering cost (not just initial build, but ongoing maintenance, iteration, component updates, and infrastructure management), and realize the math does not work for internal business workflows. The engineering team should be building the product. Business workflows should be handled by a platform built for that purpose.

Forward Deployed Engineers: why Nexus is a solution, not just software

Most enterprise AI vendors sell software and leave you to figure out the rest. Nexus is different.

Every engagement includes Forward Deployed Engineers (FDEs), real engineers embedded with your team who:

• Identify the highest-impact use cases first. Not guessing based on templates, but analyzing your specific operations to find where agents deliver the most value.
• Design agents that fit your reality. Not generic off-the-shelf configurations, but agents tailored to your workflows, systems, edge cases, and business logic.
• Handle integration complexity. So your team does not have to learn a new platform or pull engineers off product work.
• Manage organizational change. Because deploying AI at scale is 10% technology and 90% organizational change. FDEs help frame the change, train teams on new workflows, build confidence through small wins, and address concerns about transparency and control.
• Optimize continuously. Agents improve with use. FDEs help analyze performance, refine escalation logic, and scale agents to new teams and processes.

This is why Nexus converts 100% of POCs to annual contracts. The engagement is structured to deliver measurable value before you commit.

Integrations: 90+ vs. 4,000+

Haystack's integrations are focused on the AI/ML stack: model providers (OpenAI, Anthropic, Cohere, Azure, Hugging Face, AWS Bedrock), document stores (Elasticsearch, Pinecone, Weaviate, Qdrant, pgvector, Chroma, Milvus, MongoDB, and others), and monitoring tools. This is exactly what you need for building retrieval pipelines.

Enterprise workflows require a different integration surface. CRMs (Salesforce, HubSpot), ERPs (SAP, NetSuite), communication tools (Slack, Teams, Gmail, WhatsApp), productivity suites (Google Workspace, Microsoft 365), ticketing systems, and custom APIs. Nexus connects to 4,000+ enterprise systems natively. The agent deploys into Slack, Teams, WhatsApp, email, phone, and web, where work actually happens. No new systems for employees to learn.

The difference reflects the different problems each is solving. Haystack integrates with AI infrastructure. Nexus integrates with business infrastructure.

Enterprise governance: built in vs. build it yourself

For public companies, regulated industries, and enterprises with compliance requirements, governance is not optional.

Haystack's open-source framework leaves security, audit trails, access controls, and compliance entirely to your engineering team. The Haystack Enterprise Platform adds observability, governance tooling, and access controls, but enterprise compliance certifications (SOC 2, ISO 27001, GDPR) are your responsibility to implement and maintain.

Nexus ships enterprise governance from day one:

• SOC 2 Type II, ISO 27001, ISO 42001, GDPR certified
• Decision transparency: Every agent decision is traceable. What data informed it, which rules applied, why it escalated or approved
• Full audit trails: Because agents operate within existing enterprise systems (Slack, Teams, CRM), every action is logged
• Role-based access control: Control who can create, edit, and deploy agents
• No shadow AI: Because agents are integrated into systems employees already use, there is no reason for teams to adopt unauthorized external AI tools

At Orange, when the agent can confidently approve, it approves. When uncertain, it escalates to the salesperson with full context. Every step is visible, every decision logged. Result: 100% adoption, 50% conversion increase, 100% compliance.

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Frequently asked questions

Can our engineering team use Haystack alongside Nexus?

Yes. Some enterprises use developer frameworks for product-facing AI capabilities (where deep customization and retrieval architecture ownership matter) and Nexus for internal business workflows (where speed, business ownership, and embedded support matter). The two solve different problems for different teams. Haystack is strong for building RAG-powered features in your product. Nexus is built for operational workflows across sales, support, marketing, and HR. Lambda made exactly this distinction: their engineers focus on their core AI infrastructure product, while business teams own their operational agents through Nexus.

Haystack is strong at RAG. Does Nexus do RAG too?

Yes. Nexus supports both real-time RAG (connecting agents to live data from CRMs, ERPs, and databases) and stored RAG (uploading documents from Confluence, Google Drive, SharePoint for vectorized knowledge). The difference is scope. Haystack gives developers fine-grained control over every step of the retrieval pipeline: embedding models, document stores, retrieval strategies, re-ranking, evaluation. Nexus handles RAG as one capability within a broader workflow, with the retrieval layer managed by the platform. For teams that need to optimize retrieval performance at a granular level, Haystack offers more control. For teams that need retrieval as part of a larger business workflow without building the infrastructure, Nexus handles it.

We have strong AI engineers. Why would we choose Nexus over building with Haystack?

Having strong engineers is exactly the reason to consider whether their time is best spent on internal business workflows. Lambda has world-class AI engineers who build supercomputers for a living, and chose to buy instead of build. The question is not capability. It is opportunity cost. Your engineers could build this. But should they, when their time could be spent on your core product? Lambda's leadership concluded: no. And they got agents in production faster than they would have building internally.

How does Haystack Enterprise Platform compare to what Nexus includes?

The Haystack Enterprise Platform adds managed infrastructure, a visual pipeline editor, pipeline templates, testing tools, governance, and access controls on top of the open-source framework. It makes it easier to build, test, deploy, and operate Haystack pipelines at scale. However, it does not change the fundamental model: your team still designs, builds, and maintains the pipelines. Nexus includes all platform infrastructure plus Forward Deployed Engineers embedded with your team, change management support, and ongoing optimization. The gap is not just features. It is who does the work and who is responsible for outcomes.

Is Haystack really free?

The open-source framework is free. But production deployment is not. The Haystack Enterprise Platform and Enterprise Starter have custom pricing based on organization size (contact deepset for details). Beyond platform costs, the real expense is engineering time: building pipelines, selecting and configuring components, integrating with enterprise systems, implementing compliance, and maintaining everything over time. That is the cost Lambda concluded was too high.

What does the 3-month POC look like?

Every engagement starts with a 3-month proof of concept tied to specific, measurable outcomes defined upfront. Most agents are in production within the first 2-6 weeks. A Forward Deployed Engineer is embedded with your team for the entire period. You see the results, measure the impact, and decide whether to continue. You can exit anytime. This is why our POC-to-contract conversion rate is 100%: we do not move forward unless the value is clear.

Haystack supports agents now. Does that close the gap?

Haystack's Agent component supports tool calling with LLMs, multi-step reasoning, and tool invocation. This is a meaningful addition. But the agent capabilities are still oriented around the pipeline architecture, primarily designed for retrieval-augmented workflows where the agent reasons over documents and calls tools within the Haystack ecosystem. Enterprise business workflows require something different: agents that complete entire processes across dozens of systems, handle exceptions intelligently, escalate to humans with context, deploy across multiple channels, and are governed with full audit trails. Haystack's agent support is a useful building block. Nexus is the complete solution for enterprise workflow automation.

What if we have already started building with Haystack?

The investment in Haystack is not wasted, especially if it powers product-facing RAG capabilities or search features. For internal business workflows, though, it is worth asking: will the engineering team maintain these pipelines long-term? Will they iterate quickly enough as business needs change? Will they handle component updates, framework changes, and infrastructure without pulling focus from the core product? If the answer creates tension with product priorities, Nexus can handle the business workflow layer while engineering stays focused on what matters most. Lambda made exactly this separation.

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Worth exploring?

If your team has been evaluating retrieval frameworks and wrestling with the engineering trade-off (how much engineering time to allocate, how to move beyond RAG into complete workflow automation, who maintains it, who iterates when business needs change), it might be worth seeing how Lambda approached the same decision.

Lambda is a $4B+ AI company with world-class engineers who build AI infrastructure for a living. They explored open-ended AI agents and traditional automation. Neither worked. They concluded the opportunity cost of building was too high. They deployed in days what would have taken months internally. Their non-technical team owns the agents. Anticipated value: more than $7M by 2026.

Every Nexus engagement starts with a 3-month proof of concept tied to specific outcomes. Forward Deployed Engineers work alongside your team from day one. You see results before committing. You can exit anytime.

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Related comparisons

• Nexus vs LangGraph -- Graph-based agent framework: powerful for engineers, requires engineering
• Nexus vs CrewAI -- Multi-agent framework comparison: powerful for engineers, requires engineering
• Nexus vs Microsoft Copilot -- AI assistant vs. autonomous agents: assists individuals vs. completes workflows
• Nexus vs Dust -- AI assistant comparison: assists individuals vs. completes workflows
• AI Agents vs Developer Frameworks -- The full build vs. buy comparison: LangGraph, CrewAI, Haystack, and custom builds
• Back to all comparisons -->