10 Best SaaS Integration Platforms to Consider in 2025

What’s the hidden cost of managing disconnected systems?

It’s frustrated teams, fragmented data, and time-consuming manual workarounds. The solution? A modern data infrastructure built on well-connected and synchronized applications.

That’s where SaaS integration platforms come in. These tools connect your scattered applications, automate workflows, and ensure data flows smoothly across systems.

This article explores the top 10 SaaS integration platforms in 2025. Compare tools across their key features, pros & cons, use cases, and pricing models to choose the perfect fit for your data stack.

To begin, here’s a quick overview of the top 3 platforms (our pick):

What are SaaS Data Integration Platforms?

When evaluating modern tools, it’s important to understand data integration vs ETL. ETL focuses on extracting, transforming, and loading data into a warehouse. 

Whereas SaaS integration platforms are cloud-based tools that connect multiple SaaS applications and data sources. They automate, synchronize, and transform data flows across systems, ensuring centralized, reliable business data.

But why is it important?

SaaS integration platforms:

• Ensures information flows seamlessly across all applications.
• Automates repetitive tasks and reduces manual data entry.
• Reduces errors caused by duplicate or inconsistent records.
• Provides a unified view of customer interactions across systems.
• Helps maintain data security and governance.

Top 10 SaaS Integration Platforms in 2025

Below are the top 10 SaaS data integration tools to keep on your radar in 2025:

1. Hevo Data

Hevo overcomes SaaS integration hurdles by offering a fully managed data pipeline that unifies data from SaaS apps, databases, and files into a single warehouse in near real time. This eliminates fragmented reporting and manual syncing, providing a reliable view of datasets.

Hevo processes billions of records at scale without performance issues. Supporting both pull (scheduled fetch) and push (webhook) methods for data ingestion, it simplifies the connection between SaaS apps and databases, helping teams reduce manual integration efforts.

Hevo is a multi-tenant platform, designed to serve multiple customers on shared infrastructure while maintaining data isolation. This architecture automates scaling of pipelines across tenants, promoting consistent performance under heavy workloads.

Key features:

• Multi-layered transformations: Hevo offers transformations at different stages: in-flight normalization, SQL-based modeling, and Python scripts, which help users prepare analysis-ready data within the same platform.
• Incremental syncs: Supports full historical and incremental syncs, fetching either complete legacy data or only new changes since the last run. This ensures maximum data coverage while minimizing system load.
• Built-in deduplication: Hevo removes duplicate records using destination primary keys, ensuring only unique data is loaded into the warehouse.

Use case:

• E-commerce analytics: Hevo integrates data from platforms like Shopify, Stripe, and Google Analytics into BigQuery or Redshift. Retailers can track revenue attribution, product performance, and customer cohorts in near real time.
• IoT data integration: With support for real-time ingestion, Hevo can stream IoT or app event data directly into warehouses. Businesses in logistics and health tech can monitor operations in real time and act instantly on anomalies.

Pros:

• Intuitive, easy-to-use interface.
• Real-time replication keeps data consistently current.
• Well-documented features make implementation smoother.

Cons:

• Limited Customization for Complex Data Integration Workflows
• Stronger focus on cloud integrations, with limited on-premises support.

Pricing:

• Starts as a free tier with limited connectors up to 1 million events
• Starter: $239/month up to 5 million events
• Professional: $679/month up to 20 million events
• Business: Custom pricing

User review:

I love the simplicity and ease free nature of setting up pipelines. As some members in our team who come from non-tech background having knowledge in data, this tools helps them get the work done faster without having to worry about the programming and infrastructure side of it. It easily integrates in our platform. The customer support is excellent as well.

Nikhil S.

Data Science Engineer

2. Zapier

Zapier is a workflow orchestration platform that connects SaaS applications by executing event-driven, multi-step automations with built-in data conditioning and error handling.

Zapier enables users to create automated workflows called “Zaps”, each composed of triggers (events in one app) and actions (tasks performed in another). It’s ideal for teams looking to integrate disparate tools and maintain reliable workflows during API outages.

Zapier’s uniqueness lies in its hybrid approach, which combines no-code visual builders with platform CLI for custom integrations, accelerating solution deployment while maintaining enterprise-grade governance.

Key features:

• Data transformation: Zapier performs text, numeric, date, and structured data transformations before transmitting to target apps, standardizing datasets across SaaS systems.
• Partner program: Allows developers and companies to publish and maintain integrations with tiered support based on usage and health metrics. Partners get access to integration analytics, developer support, and promotional opportunities.
• API connectivity: Zapier leverages webhooks and direct API calls to integrate with any SaaS application, enabling centralized workflow orchestration and real-time data exchange.

Use case:

• Automated customer onboarding: Zapier can trigger a sequence when a new customer signs up. This creates a Slack notification for the support team to generate a personalized welcome email.
• Two-way CRM: Keep Salesforce or HubSpot in sync with Zendesk or Freshdesk so sales teams see support tickets instantly, and support reps get customer context without switching tools.

Pros:

• Runs consistently with minimal oversight.
• User-friendly, drag-and-drop interface for non-technical users.
• Performance insights via workflow analytics.

Cons:

• Limited logic in complex workflows.
• Poor error handling and debugging.
• Interface slows with many Zaps.

Pricing:

• Free tier
• Pro: $19.99 per month
• Team: $69 per month
• Enterprise: Contact for pricing

3. Stitch

Stitch by Talend is a cloud-native ETL platform built on the open-source Singer framework, which provides flexibility for moving data from multiple sources into cloud warehouses with minimal engineering effort.

Stitch extracts data via its Singer-based engine and Import API, applies light transformations, and loads it into the preferred destination. This architecture ensures efficient data replication, preserves data integrity, and supports scalable ETL pipelines.

By leveraging Apache Kafka for fault-tolerant, asynchronous data handling and Amazon S3 for secure buffering and storage, Stitch handles growing volumes of data across multiple SaaS sources.

Key features:

• Data replication: Stitch supports multiple data replication methods, including log-based replication, CDC, and full table replication. Businesses requiring near real-time updates can use CDC, while static datasets can use full table replication.
• Data normalization: Stitch preserves semi-structured data where supported or normalizes it into relational tables, making complex datasets ready for analytics with minimal preprocessing.
• Logging: Stitch provides detailed logs and a log explorer to identify and resolve errors. This helps IT and data teams monitor pipelines and maintain reliable, consistent data flows.

Use case:

• Consolidating SaaS analytics: Stitch can extract customer interactions from SaaS tools and load them into a single warehouse, giving analysts a 360° view of customer journeys without data silos.
• Real-time marketing attribution: Stitch streams campaign data from ad platforms and syncs it with sales or CRM data, enabling accurate attribution models and ROI tracking in near real-time.

Pros:

• Includes built-in analytics features for data visualization and BI reporting.
• Offers comprehensive documentation along with tailored onboarding support.
• Has a usage-based pricing model that offers transparency.

Cons:

• Limited when it comes to real-time data streaming capabilities.
• Customer support can be slow and less responsive at times.
• Provides only basic transformation options, with more advanced functions dependent on Talend integration.

Pricing:

• Standard: $100 monthly
• Advanced: $1,250 monthly
• Premium: $2,500 monthly

4. IBM DataStage

IBM DataStage is a cloud-based SaaS integration platform that connects applications and data across hybrid environments through its native integration solutions. Its built-in AI-powered insights facilitate secure, real-time, and compliant integrations.

IBM’s integration platform ingests data from multiple SaaS, on-premises, and legacy systems via connectors or APIs. It transforms data using schema validation and business logic, then routes messages via event streams or APIs. This helps data engineers manage complex, large-scale integration workflows efficiently.

IBM uniquely combines hybrid and multi-cloud integration with AI-assisted mapping, extensive connectors, and strong governance. Its collaborative no-code interface and Sterling B2B capabilities enable secure, large-scale workflows, making it ideal for both technical and business teams.

Key features:

• Connector ecosystem: IBM provides hundreds of metadata-aware connectors for SaaS, cloud services, databases, and legacy applications. These prebuilt templates accelerate deployment and simplify connecting a diverse range of systems.
• B2B integration: IBM’s Sterling enables organizations to streamline high-volume EDI and API transactions, maintaining continuous, secure, and compliant data flows while simplifying onboarding and coordination across complex supply chains.
• Multi-protocol support: IBM supports a wide range of protocols (MQ, JMS, HTTP, TCP, IP) and data formats (XML, JSON, CSV, EDI), enabling smooth integration between SaaS, cloud, on-premises systems, and B2B networks.

Use case:

• Hybrid cloud data movement: IBM DataStage helps organizations move data seamlessly between on-prem ERP systems and cloud-based SaaS tools. This enables businesses to modernize without abandoning legacy infrastructure.
• AI-driven customer insights: By integrating SaaS CRMs (Salesforce, HubSpot) with IBM Watson, businesses can analyze customer interactions in real time.

Pros:

• Enables creation of reusable components and pipelines with version control.
• Cuts costs by automating CI/CD processes.
• Works seamlessly with IBM Data Fabric to transfer data across multiple cloud environments.

Cons:

• Demands technical expertise and proper training to manage effectively.
• Deployment costs can be high for smaller organizations.
• Missing built-in source control features, which may limit collaboration.

Pricing:

Offers a pay-as-you-go model.

5. Informatica

Informatica Intelligent Data Management Cloud (IDMC) is a comprehensive, cloud-native Integration Platform as a Service (iPaaS) designed to manage the full lifecycle of data integration, governance, and quality across multi-cloud and hybrid environments.

Informatica extracts data via incremental CDC or full-batch loads, profiles schemas, and applies transformations including normalization and deduplication. Transformed data is loaded into cloud or hybrid targets with bi-directional sync, while AI-driven monitoring ensures reliable integration.

Informatica’s AI-powered CLAIRE engine automates tasks like data mapping, cleansing, and workflow design, providing intelligent recommendations to accelerate integration processes. This combines advanced automation with enterprise-grade scalability, making complex SaaS integration faster and more reliable.

Key features:

• API management: Informatica’s API Center manages the entire API lifecycle, including design, deployment, security, monitoring, and retirement. It provides centralized control to ensure that integrations are managed across all environments.
• Connectivity: Informatica offers over 400 pre-built connectors for SaaS applications, APIs, on-premises databases, big data sources, and legacy systems. This broad connectivity reduces integration time while also providing SDKs for custom connectors when needed.
• Schema handling: Automatically discovers and profiles source schemas, detects data types, and adapts to schema changes in real time. It maps source fields to target structures, handles type conversions, and ensures analytics-ready data.

Use case:

• Financial data synchronization: It connects ERP systems with SaaS accounting tools. Informatica ensures schema mapping, and compliance rules are applied during integration, keeping finance data accurate across systems.
• SaaS data consolidation: Informatica automates the movement of historical data from legacy platforms into SaaS apps. Its CDC feature ensures ongoing sync, preventing downtime or lost records during the transition.

Pros:

• Prebuilt templates for major warehouses.
• AI-powered automation with the CLAIRE engine.
• Broad cloud, hybrid, on-prem connectivity.

Cons:

• Complex setup for non-technical users.
• Limited logging and error visibility.
• Performance issues with bulk loads.

Pricing:

Informatica uses a flexible, consumption-based pricing model, which means you pay for what you need.

6. Skyvia

Skyvia is a cloud-based, no-code ETL/ELT platform that enables bi-directional SaaS data integration, replication, and synchronization across 190+ applications and databases.

Skyvia’s integration process is structured around its Data Flow Designer, a no-code, visual interface that allows users to design complex data pipelines with ease. It empowers IT teams to automate multi-source data pipelines and enforce data transformations across SaaS APIs.

Skyvia’s API offers advanced mapping and transformation options to support complex data workflows without coding. Combined with control-flow components like conditional splits and buffers, it allows efficient orchestration of large-scale, multi-source pipelines.

Key features:

• Bi-directional sync: Skyvia supports real-time and scheduled data synchronization between SaaS applications and databases. Users can maintain unified data across CRM, marketing, finance, and other platforms, enhancing operational efficiency.
• Relation mapping: Skyvia caches source-to-target keys during imports, preserving one-to-one and one-to-many relationships to maintain referential integrity across SaaS systems.
• Data replication: Skyvia supports both full and incremental replication to create backups and maintain historical versions of datasets. It also preserves schema changes, ensuring structural consistency and historical accuracy across SaaS data pipelines.

Use case:

• ERP integration: By linking platforms like Shopify or WooCommerce with ERP systems, Skyvia helps businesses sync orders, inventory, and financial data in real time.
• Data warehousing: Skyvia extracts SaaS data and loads it into warehouses like Snowflake or BigQuery. This enables centralized reporting, advanced analytics, and cross-platform insights without manual exports.

Pros:

• Cost-effective and transparent pricing options.
• Offers a no-code interface with drag-and-drop setup.
• Solid automation with scheduling, monitoring, and backup built in.

Cons:

• Interface can feel less intuitive for advanced querying needs.
• Fewer advanced customization options for complex data teams.
• Limited connector coverage.

Pricing:

• Free for 2 scheduled integrations.
• Basic: $79 per month for 5 scheduled integrations.
• Standard: $159 per month for 50 scheduled integrations.
• Professional: $199 per month for unlimited scheduled integrations.
• Enterprise: Request a quote.

7. Mulesoft

MuleSoft is a unified iPaaS and API lifecycle management solution. The platform is designed to facilitate seamless integration of SaaS applications, legacy systems, and APIs, making it particularly beneficial for enterprises undergoing digital transformation.

MuleSoft uses an API-led connectivity approach to integrate SaaS applications, legacy systems, and data sources through secure and reusable workflows. Business analysts can orchestrate API-led data flows and real-time data synchronization across systems.

MuleSoft’s standout feature is its native data transformation language, DataWeave, which enables seamless conversion between JSON, XML, CSV, and other formats. It supports nested structures and reusable functions, integrated directly into API-led flows.

Key features:

• Anypoint platform: Offers an end-to-end suite for designing, deploying, managing, and monitoring integrations. With tools like Design Center, Exchange, Management Center, and CloudHub, organizations gain complete control over SaaS integrations.
• Analytics: MuleSoft provides real-time dashboards and API analytics, offering visibility into integration performance, error rates, and SLA adherence.
• Event-driven architecture: MuleSoft enables event-driven architectures for real-time data processing. By leveraging Anypoint MQ and Anypoint Event Mesh, it ensures low-latency data delivery across distributed systems.

Use case:

• IoT & SaaS data convergence: MuleSoft merges IoT device data with SaaS analytics platforms like Snowflake or Tableau, allowing companies to analyze operational data alongside customer behavior for predictive insights.
• Unified customer data: MuleSoft connects Salesforce and HubSpot to build a 360-degree customer view. By synchronizing contact, support, and sales data, businesses eliminate data integration problems and facilitate personalized customer engagement.

Pros:

• Offers real-time monitoring for APIs and integrations.
• Strong security measures with powerful debugging support.
• Streamlined authentication process for target systems.

Cons:

• Pricing can be high for smaller projects.
• Set up and management often need skilled professionals.
• May not deliver optimal efficiency in very large-scale integrations.

Pricing:

Pricing is based on business needs and additional features.

8. Workato

Workato is a cloud-based iPaaS that combines enterprise-grade automation with a low-code/no-code interface, enabling users to connect multiple cloud applications and automate complex, secure, and scalable workflows without extensive coding.

Workato enables the creation of automated integration workflows, called “recipes,” which define a sequence of triggers and conditional logic to connect multiple applications and data sources. It helps teams optimize and automate routine business processes.

AI by Workato is a unique utility connector that integrates advanced AI capabilities directly into your automation workflows. It leverages models from Anthropic’s Sonnet 4 and OpenAI to bring sophisticated AI functionalities to your processes.

Key features:

• Connectivity: Offers over 1000 pre-built connectors for major SaaS applications, like Salesforce, NetSuite, Slack, HubSpot, and SAP. Workato accelerates the deployment of automated workflows across the organization’s SaaS ecosystem.
• Event-driven integrations: The platform supports event-based triggers, polling, and CDC, allowing workflows to run in near real-time. This capability ensures that critical updates, such as CRM changes or order updates, are propagated instantly to relevant systems.
• Multi-app orchestration: A single recipe in Workato can handle tasks across Salesforce, NetSuite, Slack, and custom APIs, ensuring end-to-end process automation for complex business operations.

Use case:

• Employee onboarding: Workato connects HR systems with IT and collaboration tools. New hires get accounts and communication set up instantly, reducing manual effort and speeding up the process.
• IoT device data: For companies using IoT devices, Workato can capture sensor data, transform it, and push it into analytics platforms or trigger service workflows for proactive responses.

Pros:

• Offers advanced features like real-time monitoring and built-in error handling.
• Helpful customer support and active community.
• Easy-to-use drag-and-drop recipe builder.

Cons:

• Steep learning curve for complex recipes.
• Cluttered visual interface in large workflows.
• Vague error messages make debugging hard.

Pricing:

Offers a pay-as-you-go model. You pay for one workspace and the number of recipes you want.

9. Funnel.io

Funnel.io specializes in centralizing marketing and advertising data from multiple cloud applications. Unlike general ETL tools, Funnel.io focuses specifically on marketing performance data, acting as a bridge between diverse SaaS marketing tools.

Funnel.io centralizes marketing data by extracting it via API connectors from SaaS platforms. It then automatically loads the cleaned, analysis-ready data into BI tools or warehouses, enabling real-time reporting and automated insights.

Funnel.io’s Conventions (Beta) feature enforces a structured, pattern-based naming system with variable validation, platform-specific adaptation, and AI-assisted setup to standardize marketing data across sources.

Key features:

• Data transformation: Funnel.io offers robust transformation capabilities, including calculated metrics, filtering, grouping, and enrichment. Users can apply custom SQL or Python transformations to implement business-specific logic within the platform.
• Flexible data loading: Once data is cleaned and transformed, Funnel.io can push it directly to BI tools like Tableau and Power BI. Both push and pull models are supported with real-time or scheduled syncs, delivering up-to-date data for reporting.
• AI-assisted insights: Funnel AI provides anomaly detection, highlights unusual trends, and flags inconsistencies. This layer of intelligent monitoring reduces the need for manual validation and accelerates decision-making using integrated SaaS data.

Use case:

• Centralized marketing data: Funnel.io pulls campaign data from platforms like Google Ads, Facebook, and LinkedIn into one structured dataset. Marketers can then compare performance across channels without manually exporting spreadsheets.
• Dynamic dashboards: By feeding clean data into tools like Looker Studio, Tableau, or Power BI, Funnel.io enables real-time dashboards. Executives can track ROI, CAC, or customer journeys instantly.

Pros:

• Automates reporting and dashboard creation.
• Strong onboarding and customer support.
• Usage-based, transparent pricing with flexpoints.

Cons:

• Steep learning curve for advanced modeling.
• Performance slows with very large datasets.
• Limited customization in reporting and monitoring.

Pricing:

Starts with a free plan. For the pricing details of Starter, Business, and Enterprise plans, you can “talk to sales.”

10. Integrate.io

Integrate.io is a cloud-native, low-code ETL/ELT platform that unifies ETL, ELT, CDC, and Reverse ETL in a single solution. It leverages CDC and in-pipeline transformations to sync data in real time across SaaS applications and databases.

Integrate.io captures incremental changes via CDC from SaaS apps, performs in-pipeline schema mapping and complex transformations to deliver synchronized data. This workflow empowers analytics teams to maintain real-time actionable data across multiple SaaS environments without custom coding.

Integrate.io’s Custom Primary Key feature lets users set one or more unique columns as the primary key for tables that don’t have a natural primary key. It is beneficial for systems where tables don’t have an inherent primary key but still require data synchronization.

Key features:

• Salesforce connectors: Integrate.io provides specialized, bi-directional Salesforce integrations for seamless CRM data synchronization and automated workflow execution across systems.
• REST API: Integrate.io features a powerful REST API connector that connects to any RESTful API by configuring HTTP requests, including API keys and OAuth.
• In-pipeline transformation: Users can apply SQL, Python, or drag-and-drop transformations directly within the pipeline. It handles joins, aggregations, type casting, and other complex operations without relying on external tools.

Use case:

• Multi-cloud integration: For organizations using hybrid or multi-cloud architectures, Integrate.io can move and synchronize data across AWS, Azure, GCP, and private cloud environments, without complex custom scripts.
• Advanced data transformation: Complex transformations such as joins, aggregations, denesting JSON, and conditional logic can be performed directly in pipelines. Downstream analytics platforms receive clean, analysis-ready data without manual intervention.

Pros:

• Complies with major security standards and regulations.
• Supports 150+ pre-built connectors for batch integrations.
• Offers dedicated support and solution engineers.

Cons:

• Lacks strong real-time data streaming capabilities.
• Limited documentation makes integration harder.
• UI struggles to scale with complex ETL workflows.

Pricing:

Available at $1,999 per month, offering complete platform access, 60-second pipeline frequency, and unlimited connectors. You can further customize plans and add more features to the plan.

What Are the Key Factors in Evaluating SaaS Integration Platforms?

The key factors in evaluating SaaS integration platforms include:

1. Connector availability

The platform should support a wide range of SaaS apps, databases, and APIs, including legacy and niche systems. Pre-built connectors with full API coverage, OAuth support, and management support advanced data integration techniques.

2. Data transformation

Look for platforms that enable in-pipeline transformations without external tools. This includes joins, aggregations, type conversions, conditional logic, and schema evolution handling.

3. Real-time vs batch data integration

Check whether the tool supports real-time data sync via CDC batch-only processing. Real-time integration keeps dashboards up to date, which is critical for marketing automation and decision-making.

4. Ease of use

Choose a platform that allows both technical and business users to visualize data flows, debug pipelines, and implement transformations. Drag-and-drop interfaces accelerate deployment, while scripting layers provide the flexibility to handle complex use cases.

5. Scalability

Platforms should efficiently handle high-frequency API calls, large payloads, and parallel processing. A scalable data integration architecture with distributed processing or auto-scaling pipelines prevents bottlenecks as SaaS usage grows.

6. Security & compliance

End-to-end encryption, granular access control, API authentication, and audit trails are essential. Compliance with standards like SOC 2, HIPAA, and GDPR protects financial data and mitigates regulatory risk.

7. Transparent pricing

Pricing should be clear, predictable, and aligned with usage, whether per row, per API call, per pipeline, or subscription, to avoid hidden costs.

Why Does Hevo Stand Out for SaaS Integration?

Hevo simplifies SaaS integration with its no-code, fully managed data pipeline that supports over 150+ connectors across SaaS apps, databases, and cloud warehouses.

Its real-time CDC ensures that business systems always reflect the latest data, while automatic schema mapping and in-pipeline transformations guarantee data consistency without manual intervention.

With built-in fault-tolerant architecture, monitoring, and alerting, Hevo offers reliable, scalable, and secure data integration, making it a powerful choice for teams seeking to unify their SaaS ecosystem.

Altogether, if you want a secure, compliant, and real-time solution to unify fragmented data, Hevo is the platform to consider in 2025.

Sign up for Hevo’s 14-day free trial today!

FAQs on SaaS Data Integration Platforms