Best iPaaS for SaaS Integrations in 2026: Paragon, Nango, and the Rise of Real-Time Unified APIs

March 9, 2026

For more than a decade, iPaaS platforms (Integration Platform as a Service) have been the default way to connect SaaS applications. Tools like Zapier, Workato, and embedded integration platforms such as Paragon helped companies automate workflows and move data between systems.

However, the needs of modern SaaS platforms have changed.

Today's products often need to:

• access customer SaaS data in real time
• power AI agents and copilots
• embed integrations directly inside their product
• avoid storing sensitive third-party data
• scale integrations across hundreds of providers

These requirements expose the limitations of traditional iPaaS architecture.

In this guide we look at two major platforms often considered when evaluating integration infrastructure—Paragon and Nango—and explain why many engineering teams are increasingly adopting real-time unified APIs like Unified.to instead.

Architecture Comparison

What iPaaS Platforms Are Designed For

iPaaS platforms were originally built to solve internal automation problems, not product-embedded integrations.

Typical characteristics include:

• workflow automation engines
• scheduled data synchronization
• stored integration datasets
• orchestration across multiple SaaS apps
• event pipelines and job queues

Examples of traditional iPaaS platforms include Workato, MuleSoft, Tray.io, and Zapier.

These tools work well when the goal is automating internal processes, such as:

• sending Slack alerts when a CRM record changes
• syncing marketing leads into a data warehouse
• running scheduled business workflows

But when SaaS companies need to embed integrations inside their own product, iPaaS architectures introduce several challenges:

• polling-based updates
• stored customer data
• complex workflow infrastructure
• higher operational overhead
• slower launch timelines for integrations

This is where newer architectures—particularly real-time unified APIs—are gaining traction.

Limitations of iPaaS for SaaS Product Integrations

When used inside SaaS products rather than internal automation workflows, iPaaS platforms often introduce architectural tradeoffs.

Batch-oriented data flow

Most iPaaS systems rely on scheduled polling or sync pipelines to retrieve updates from external systems.

Even with fast polling intervals, this creates unavoidable latency between when data changes and when it becomes available to the product.

Data replication and storage

iPaaS platforms typically store synchronized copies of customer data in their infrastructure to power workflows.

For SaaS companies working with sensitive data (HRIS, CRM, financial systems), this can create additional:

• compliance requirements
• security considerations
• data residency concerns

Workflow infrastructure complexity

iPaaS tools are built around workflow engines. That means:

• integration logic runs in pipelines
• debugging often requires inspecting workflow runs
• event queues and job workers must be managed

For product integrations that simply need read/write API access, this architecture can be unnecessarily complex.

Embedded iPaaS Platform: Paragon

Paragon is one of the most prominent embedded iPaaS platforms for SaaS products.

Instead of simple API access, Paragon focuses on workflow-driven integrations.

Key features

• embedded integration marketplace for SaaS products
• workflow automation builder
• Managed Sync pipelines for data ingestion
• ActionKit library of integration actions
• microservice architecture with Redis queues and worker execution

Paragon's Managed Sync system performs scheduled ingestion of third-party data into Paragon's own infrastructure, where it is normalized into "Synced Objects."

Incremental sync jobs run frequently, while full refreshes periodically reconcile the dataset.

When Paragon works well

Paragon is a good fit when SaaS products need:

• complex multi-step workflows
• automation pipelines across apps
• low-code integration orchestration
• durable ingestion pipelines for analytics

Architectural tradeoffs

For real-time product integrations, Paragon's architecture introduces some limitations:

• synced data is stored inside Paragon
• updates arrive after scheduled sync cycles
• workflow orchestration increases operational complexity
• record update webhooks often require follow-up fetch calls

These tradeoffs are typical of embedded iPaaS platforms.

Code-First Integration Infrastructure: Nango

Nango represents another approach to integration infrastructure.

Rather than offering a unified API or low-code automation platform, Nango provides developer-defined sync engines.

Key features

• OAuth infrastructure for 700+ APIs
• code-first integration logic written in TypeScript
• scheduled polling syncs
• proxy gateway for direct API calls
• open-source deployment options

Developers write custom sync functions that run on Nango's infrastructure and periodically fetch data from external systems.

These functions store the results in Nango's database so the application can retrieve them later.

When Nango works well

Nango is well suited for teams that want:

• full control over integration logic
• open-source infrastructure
• custom data models
• self-hosted integration pipelines

Architectural tradeoffs

However, the code-first sync approach also means:

• engineers must maintain mapping logic for every provider
• polling schedules determine data freshness
• customer data is stored in Nango's database
• webhook retry behavior is limited

While Nango provides flexibility, the maintenance burden increases as the number of integrations grows.

First-Generation Unified APIs

Platforms such as Merge.dev, Finch, and Kombo represent another category: unified APIs with scheduled sync pipelines.

These systems normalize data models across categories like HRIS, CRM, or ATS.

However, they still rely on sync-and-store architectures.

Typical characteristics include:

• scheduled background synchronization
• normalized schemas served from stored datasets
• incremental sync jobs and backfills
• webhook notifications after sync completion

This design simplifies querying but introduces data staleness between sync intervals.

The Rise of Real-Time Unified APIs

Modern SaaS products increasingly require live access to customer systems, especially when powering:

• AI agents
• real-time dashboards
• collaborative SaaS features
• embedded analytics
• operational workflows

This demand has led to the emergence of real-time unified APIs.

A real-time unified API executes requests directly against the source system, without caching or replicating customer data.

One platform built around this architecture is Unified.to.

How the architecture works

Unified's platform uses a pass-through proxy model:

• every API call executes directly against the provider API
• customer records are never stored in Unified's infrastructure
• normalized schemas map provider fields to unified objects
• native and virtual webhooks deliver updates in real time
• optional database sync writes data into the customer's infrastructure, not Unified's

Benefits of the real-time unified API model

This architecture offers several advantages compared with iPaaS and sync-based unified APIs.

Fresh data

Requests are executed live against the provider API, so responses reflect the current state of the source system.

Reduced compliance surface

Because customer records are not stored by the integration platform, the security footprint is smaller.

Simpler infrastructure

Applications interact with integrations through a consistent API instead of orchestrating workflows or maintaining sync pipelines.

AI-ready architecture

Unified's Model Context Protocol (MCP) server exposes integrations as callable tools for LLM agents, enabling AI applications to safely interact with external SaaS systems.

Why Many SaaS Teams Are Moving Beyond iPaaS

iPaaS platforms remain valuable for automation and internal data orchestration.

But SaaS companies building customer-facing integrations increasingly need:

• real-time data access
• scalable integration coverage
• minimal infrastructure overhead
• AI-ready APIs
• reduced compliance risk

Architectures designed for workflow automation often struggle to meet these requirements.

Real-time unified APIs offer a different approach: instead of replicating data and orchestrating workflows, they provide direct access to live SaaS systems through normalized APIs.

Final Thoughts

Choosing an integration platform depends on the architecture your product requires.

• Embedded iPaaS platforms like Paragon are powerful for workflow automation and internal orchestration.
• Code-first sync engines like Nango offer flexibility for teams willing to maintain integration logic themselves.
• First-generation unified APIs simplify schemas but often rely on scheduled sync pipelines.

However, for SaaS companies building real-time integrations and AI-driven features, architectures that avoid caching and data replication are increasingly attractive.

Real-time unified APIs such as Unified.to represent this new approach—combining normalized schemas, webhook-driven updates, and live API execution to power integrations across hundreds of SaaS platforms.

As integration needs evolve, the question is no longer simply which iPaaS platform to choose, but whether iPaaS is the right architecture at all.

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