Everyone wants to use all their data to get smarter, to deliver better service, and to build better products. But acquiring all your data in order to use it is hard … really hard. Today the average enterprise uses dozens if not hundreds of SaaS platforms. Add on-prem systems, databases, files, and APIs to the mix, and you can see why data integration platforms are a big deal.
In fact, they’re critical.
Data integration platforms are the tools that make data unification possible, even if that unification is virtual, not actual. This data integration typically involves data extraction (which is what Extract is all about and where Extract excels) plus other activities, like:
- Transformation
- Loading (yeah, Extract works here too)
- Orchestration
- Synchronization
Enterprises use data integration platforms so they can build a 360-degree customer view, or feed a data warehouse or lakehouse with raw data for BI and analytics, or enable real-time data syncing between apps. Companies also use them for supporting AI or ML training data pipelines, or migrating legacy systems.
But what are the top data integration platforms?
Key takeaways
- Data integration tools unify data from multiple SaaS, on-prem, and cloud sources for analytics, AI, and real-time operations.
- Top 7 vendors: Informatica, IBM DataStage, Oracle (ODI & GoldenGate), Microsoft (ADF & SSIS), Talend (Qlik), SAP Data Intelligence, MuleSoft.
- Each excels in different scenarios — from API-led integration (MuleSoft) to SAP-native connectivity (SAP Data Intelligence) or open-source flexibility (Talend).
- Key capabilities to compare: deployment options, real-time vs. batch support, scalability, source coverage, governance, and cost model.
- The “best” choice depends on your tech stack, data strategy, compliance needs, and budget.
Top data integration platforms: overview
We’ll look at 7 different data integration platform providers:
- Informatica
- IBM DataStage
- Oracle Data Integrator (and GoldenGate)
- Microsoft (Azure Data Factory & SQL Server Integration Services)
- Talend
- SAP Data Intelligence
- MuleSoft
Each of these platforms has unique strengths.
- Informatica and IBM excel in all-around enterprise data integration and governance
- Oracle is great in high-performance data replication, especially for Oracle environments
- Microsoft works very well in Azure-centric and cost-effective integration
- Talend has significant open-source flexibility and data quality
- SAP is strong in native SAP data integration
- MuleSoft specializes in API-led connectivity for complex architectures
All are proven data integration platform solutions for enabling a unified, responsive data infrastructure across cloud and on-premises boundaries. As always, the best choice depends on your organization’s specific requirements, your existing tech stack, budget, and your strategic approach to data (e.g. API-first or ETL-centric).
Here’s a high-level overview of some of their capabilities before we dive into each one:
| Deployment | Real-time integration | Batch Processing | Scalability | Data Sources Support | |
| Informatica | On-prem Cloud Hybrid | Yes | Yes | Very high | Extensive |
| IBM DataStage | On-prem Cloud Hybrid | Yes | Yes | Very high | Extensive |
| Oracle | On-prem Oracle Cloud | Yes | Yes | Very high | Medium |
| Microsoft | On-prem Azure Cloud | Limited | Yes | High | Medium |
| Talend | On-prem Cloud Hybrid | Limited | Yes | High | Extensive |
| SAP | On-prem SAP Cloud | Limited | Yes | High | Medium |
| MuleSoft | On-prem Cloud Hybrid | Yes | Yes | Very high | Extensive |
Data integration platforms: deep dives
Each of these data integration platforms is a massive, complex suite of products, so we’ll just hit the highlights for each, looking at:
- Key features
- Strengths
- Weaknesses
- Pricing models
The data integration tools are listed in no particular order.
Informatica
Informatica is a long-standing leader in the data integration platforms space, offering a broad suite of tools and supporting complex hybrid environments. It’s often used in finance, healthcare, and other regulated industries that demand high performance, governance, and security. Typical use cases include large-scale ETL/ELT pipelines, data warehouse feeding, data governance, and real-time data synchronization for mission-critical systems.
Key features
- Comprehensive integration & data management
Supports batch ETL, ELT, data replication, data quality, metadata management, and data cataloging in one platform. Informatica’s CLAIRE AI engine provides intelligent recommendations for mappings, data matching, and optimization. - Hybrid and multi-cloud support
Deployable on-premises or in cloud, with a broad range of connectors for databases, applications, mainframes, big data platforms, and SaaS sources. Facilitates integration across AWS, Azure, GCP, etc., and on-prem systems. - Real-time integration
Offers change data capture (CDC) and streaming integration capabilities (e.g. via Informatica Data Replication and streaming modules) to enable low-latency data updates in addition to batch processing. - Strong governance and data quality
Built-in data quality tools, master data management options, and lineage tracking ensure compliance and trusted data … critical for industries like finance and healthcare. - Scalability and performance
Designed for enterprise scale. Can handle very large data volumes with high throughput, parallel processing, and optimization for various data architectures (from traditional ETL on SQL databases to big data and cloud warehouses).
Strengths
Gartner recognizes Informatica as a leader for its “comprehensive data integration across multiple ecosystems” and flexible licensing model. It is praised for end-to-end functionality covering cloud integration, data lake/warehouse ingestion, real-time replication, and robust metadata-driven governance. Informatica’s scalability and rich feature set make it ideal for complex, high-volume environments. Its large user community and support for virtually all data sources are additional strengths.
Weaknesses
The platform’s breadth comes with complexity: it has a steep learning curve and often requires specialized expertise to implement and maintain. Users have reported that migrations and upgrades can be challenging. Cost is another consideration: Informatica’s solutions tend to be high in price, which can be prohibitive for smaller teams. The licensing and pricing structure may be difficult to navigate and expensive at enterprise scale.
Pricing model
Informatica offers enterprise licenses and subscriptions, often customized to the client. Pricing can be based on factors like number of connectors, processing capacity, or nodes. Informatica has moved toward a consumption-based or processing-unit based model in its cloud offerings. In general, it is known to be premium-priced software, reflecting its enterprise focus. Smaller organizations often start with limited editions or specific tool licenses, while large enterprises negotiate broader platform agreements.
IBM DataStage
IBM’s data integration offering centers on IBM InfoSphere DataStage (now often deployed as part of IBM’s Cloud Pak for Data platform). IBM has a long legacy in enterprise ETL and is also a Leader in Gartner’s rankings.
DataStage is designed for large enterprises with mature, complex data environments like banks, insurance, telcos, and manufacturers that require reliability and strong governance. It supports both on-premises deployments and containerized cloud or hybrid setups. Common use cases include large-scale ETL for data warehouses/lakes, integration of mainframe or legacy systems with modern platforms, and near-real-time data replication for analytics or operational sync.
Key features
- Enterprise ETL engine
DataStage provides a high-performance parallel processing ETL engine, capable of handling huge volumes of data with complex transformations. It supports a wide range of sources/targets (relational, files, enterprise applications, big data frameworks) and provides a visual job design interface with extensive transformation libraries. - Multiple integration styles
Beyond batch ETL, IBM’s portfolio includes IBM Data Replication for CDC (low-latency, real-time database replication) and recently IBM StreamSets for streaming data pipelines. This means IBM can address batch, micro-batch, and real-time streaming use cases under one umbrella. - Data virtualization
IBM offers data virtualization capabilities (through products like IBM Cloud Pak for Data’s Virtualization service) to query and integrate data without physical movement, complementing ETL with a logical data fabric approach. - Governance and metadata
IBM’s built in strong metadata management plus integration with IBM’s governance tools. This is valuable for data quality, regulatory compliance, and implementing enterprise data governance policies. - Hybrid/multicloud flexibility
IBM’s integration tools are designed for hybrid cloud. DataStage can run on IBM Cloud or other clouds via Kubernetes (Cloud Pak for Data) or on traditional on-prem servers. Remote engine capabilities allow processing data close to where it resides (on-prem or in specific clouds) to minimize data movement and latency. IBM’s architecture emphasizes compatibility with existing systems to avoid “rip and replace” and reduce lock-in.
Strengths
Overall, IBM has a comprehensive and cohesive integration approach.
Its strengths include support for multiple integration patterns (batch, real-time, virtualization) and the ability to optimize workloads across different environments. IBM’s tools are known for their performance and scalability on big data, and for handling complex transformations. The platform’s focus on data fabric and new innovations (like AI assistance for pipeline building and DataOps features) help modernize it. Additionally, IBM’s long track record (19 consecutive years as a Magic Quadrant Leader) inspires a degree of confidence.
Weaknesses
Despite more flexible cloud-native options, IBM’s integration suite still has a reputation for high total cost and complexity. It can be difficult to deploy and tune without IBM specialists, and the learning curve for DataStage and related tools is steep. Also, IBM’s many tools can feel fragmented, and integrating them can require additional effort.
Pricing model
IBM typically sells its data integration capabilities through enterprise licensing, either standalone or as part of Cloud Pak for Data. It’s often custom-negotiated for large installations. IBM has introduced more modular as-a-service pricing for some components like IBM DataStage on Cloud, but generally it remains a significant investment.
Oracle
Oracle offers a two-pronged data integration suite: Oracle Data Integrator (ODI) for high-performance bulk data integration (ELT), and Oracle GoldenGate for real-time data replication. These tools cater especially to enterprises invested in the Oracle ecosystem that need to integrate data across on-premises and cloud.
Oracle’s integration solutions are commonly used in financial services, retail, and telecom for scenarios like data warehouse ETL, database migrations/upgrades with zero downtime, and real-time data streaming to analytics platforms. Oracle has positioned these tools for both on-prem deployments and as cloud services.
Key features
- Oracle Data Integrator
ODI is an ELT tool optimized for Oracle and heterogeneous sources. It uses a push-down architecture, leveraging source/target databases to do transformations for efficiency, and provides a GUI for designing data transformations and workflows. ODI has strong support for Oracle’s own technologies (PL/SQL, Oracle DB features) and can work with other databases, big data, and files. It includes data quality and governance features (metadata repository, lineage) to maintain data integrity. - Oracle GoldenGate
GoldenGate is a log-based replication tool enabling real-time, bi-directional data synchronization. It captures and delivers database changes with very low latency across homogeneous or heterogeneous databases. GoldenGate supports numerous databases (Oracle, SQL Server, DB2, MySQL, etc.) and even some non-DB sources, making it possible to keep a cloud data warehouse in sync with an on-prem transactional DB, for example. It’s known for high-volume, mission-critical replication with features for filtering, transformations, and ensuring consistency. - Hybrid Cloud Integration
Oracle’s integration tools can be run on-premises or in the cloud. Oracle GoldenGate in particular has a cloud service (OCI GoldenGate) for easy deployment in Oracle Cloud, and it also supports multi-cloud or on-prem to cloud replication. This allows customers to replicate on-prem Oracle ERP database changes out to an AWS or Azure analytics environment, for example. ODI can be installed on-prem or on cloud VM environments, and Oracle has integrated ODI capabilities in its Oracle Integration Cloud for cloud ETL. - Data fabric and advanced patterns
Oracle supports modern data architectures like data mesh and data fabric, allowing data products to be created from real-time feeds and batch data combinations. GoldenGate’s ability to feed event streams to Kafka and other destinations, and ODI’s integration with Oracle’s data catalog help to create an integrated data ecosystem.
Strengths
Oracle’s integration tools are renowned for performance and low-latency in their domains. GoldenGate, in particular, is an industry-leading CDC solution trusted for mission-critical replication. These tools excel in Oracle-centric environments: if an organization runs many Oracle databases or applications, ODI and GoldenGate offer native optimization that can be hard to match. Oracle also provides strong support for heterogeneous integration between Oracle and non-Oracle systems, giving it versatility. The combination of real-time and batch capabilities means Oracle’s portfolio can cover a wide range of integration needs. Furthermore, Oracle has a long history with large enterprise clients, meaning these tools are proven in high-volume, high-reliability scenarios.
Weaknesses
It’ll be no surprise to data experts that the biggest drawbacks are cost and Oracle-centricity. Oracle’s licensing costs for GoldenGate and ODI are notoriously high, and some perceive the tools as being best suited for Oracle-to-Oracle use cases. While they do support other technologies, organizations with diverse environments sometimes shy away due to a fear of vendor lock-in or suboptimal support for non-Oracle endpoints. ODI, while powerful, can be complex to set up, and it’s often seen as a tool that requires skilled developers.
Pricing model
Oracle GoldenGate and ODI are typically licensed per processor, per source/target, or as cloud subscriptions. On-premises, GoldenGate has been sold by per-core or per-module licensing, which adds up for large deployments. Oracle has introduced GoldenGate Base licensing and cloud hourly pricing on OCI, which can be more cost-effective for short-term use. ODI is usually part of the Oracle technology license portfolio, and in Oracle Cloud, GoldenGate is a managed service charged on an hourly usage basis.
See how data integration fits into the full modern data stack in our 2025 tools roundup
Microsoft (Azure Data Factory & SQL Server Integration Services)
Microsoft is a top contender in the data integration platforms space, providing data integration capabilities both on-premises and in the cloud.
SQL Server Integration Services (SSIS) is Microsoft’s veteran on-prem ETL tool, bundled with SQL Server, while Azure Data Factory (ADF) is the cloud-based ETL and data pipeline service on Azure. Together, these cover hybrid needs: SSIS for on-prem SQL-centric ETL, and ADF for orchestrating data movement across on-prem and cloud sources.
Microsoft’s integration tools are commonly used by organizations heavily invested in the Microsoft ecosystem, using tools like SQL Server, Azure databases, or Power BI. With the introduction of Microsoft Fabric (in preview in 2025), Microsoft is further unifying data integration, engineering, and analytics on Azure.
Key features
- Azure Data Factory
ADF is a fully managed cloud service for building data pipelines. It has a low-code visual interface plus support for coding (via Synapse Pipelines or Azure Data Factory UI) to create pipelines that perform extract, transform, load or ELT operations at scale (even though it’s probably not as simple as Extract). It offers 90+ pre-built connectors to various data stores, and supports scheduling, trigger-based runs, and mapping data flows. - SSIS
SSIS provides extract-transform-load functionality within the SQL Server environment. It’s a mature tool with a drag-and-drop designer in Visual Studio, used for tasks like data warehouse population, data cleansing, and migrations. SSIS packages can also be deployed to Azure, facilitating lift-and-shift of existing on-prem workflows to cloud. - Real-time and streaming support
While not a streaming platform per se, ADF can do near-real-time integration using event triggers or frequent pipeline runs, and Azure offers complementary services for true streaming. SSIS can respond to messages or run continuously for minimal-latency integration, but generally Microsoft addresses real-time needs through its Azure messaging and event grid ecosystem rather than SSIS/ADF directly. - Scalability and performance
ADF is cloud-native and can scale out processing dynamically. Its copy activity can use parallelism and its data flows run on Azure Spark clusters that scale to large data volumes. SSIS on-prem can scale with the underlying SQL Server hardware but is typically suited for moderate batches unless scaled out via Azure Data Factory integration runtimes or SQL Server clusters. - Integration with Microsoft stack
As you’d expect from a Microsoft tool, one of Microsoft’s strengths is integration with other Microsoft tools: Power BI dataflows, Azure Machine Learning, and Logic Apps can all interoperate with Data Factory. Additionally, Microsoft’s solution is appealing for those already using Azure Synapse Analytics, as Data Factory is essentially the orchestration component of Synapse.
Strengths
For Microsoft-centric shops, these tools offer seamless integration and a relatively gentle learning curve for those familiar with Microsoft products. Azure Data Factory is relatively easy to use and has the ability to quickly connect to a wide array of sources.
The cost model can be attractive with a pay-as-you-go set-up, which can be economical for intermittent workloads.
Another strength is hybrid capability: ADF’s self-hosted integration runtime help you securely connect on-prem data to the cloud without complex custom solutions. Scalability in Azure is another plus: ADF can handle large data volumes by leveraging Azure’s elastic resources, and Microsoft continually improves performance and adds features.
Weaknesses
In pure feature-by-feature comparisons, Microsoft’s tools sometimes lag specialized integration vendors. For example, metadata management and data cataloging for ADF pipelines are not as rich out-of-the-box, and some users find ADF’s debugging and error handling less mature than older ETL tools.
Hybrid deployments can present challenges, and SSIS, while powerful, is a bit dated and not well-suited for modern cloud-centric or big data scenarios: it doesn’t natively handle unstructured data or Hadoop well.
Additionally, Microsoft’s focus is often on Azure-first; support for other clouds or vendor-neutral scenarios may not be as deep.
In addition, while ADF is user-friendly for basic use, doing very complex workflows might require custom coding.
Pricing model
Azure Data Factory uses a pay-as-you-go model based on pipeline orchestration runs and data movement/processing volume. Activities like copy operations have an hourly rate, and data flow execution is billed per vCore-hour of compute used. This granular consumption model can be cost-efficient for small jobs and scales with usage.
Talend Data Fabric (Qlik)
Talend is 1 of sever data integration platforms with open source roots. It’s known for its focus on data quality and governance, and is now part of Qlik’s portfolio.
Talend offers an integrated data fabric suite that includes data integration, data profiling, data catalog, and more. Talend is used by enterprises that need end-to-end data management with a strong emphasis on trusted data for analytics, master data management, and ensuring data cleanliness across cloud and on-prem systems.
It appeals both to organizations that want the flexibility of open-source and those looking for a one-stop solution for integration. Talend supports deployment on-premises, in the cloud, or hybrid, making it suitable for companies in finance, healthcare, retail, and other sectors looking to unify data from numerous sources.
Key features
- Broad connectivity
Talend offers 1000+ pre-built connectors and components, enabling integration with a wide variety of databases (SQL, NoSQL), files, enterprise applications (SAP, CRM systems), SaaS APIs, and big data platforms. - Batch and some real-time integration
The platform is strong in batch ETL/ELT. It also supports real-time/streaming to a degree with messaging connectors, and can perform change data capture through Talend Change Data Capture. However, streaming is not Talend’s primary focus compared to its batch integration and data processing strengths. - Data quality and preparation
A differentiator for Talend is its built-in data profiling, cleansing, and enrichment tools. It includes Talend Data Quality for things like deduplication, validation, and enrichment. - Unified platform & governance
Talend’s Data Fabric tool provides a unified environment that covers integration, quality, cataloging, and self-service data prep. Metadata is shared across these, helping with data lineage and governance. Business users can participate by using Talend Data Stewardship or Pipeline Designer for simpler use cases, making it semi-self-service. - Hybrid and multi-cloud deployment
Talend jobs can be deployed on-premises or in cloud environments, including a managed cloud offering. It supports running on cloud infrastructures like AWS, Azure, or GCP, and can integrate across hybrid environments.
Strengths
Talend is recognized for comprehensive capabilities across data integration and data quality. Its strengths include a combination of coding flexibility (developers can dig into the generated Java code if needed) and a rich library of connectors/components that speed up development for common tasks. Because of its open-source roots, Talend avoids vendor lock-in (the jobs are essentially Java code that could run anywhere). Also, Talend’s hybrid nature and reasonable learning curve mean it can be adopted incrementally.
Weaknesses
While Talend is good in many areas, competitors can outperform it in specialized domains like pure-play streaming platforms or highly intuitive cloud UX. The user interface, while powerful, is not as good as newer cloud-native tools.
Performance-wise, Talend can handle large data, but the runtime might not be super optimized: complex jobs tend to be memory-intensive.
Pricing model
Talend’s open-source tools are free to use, but the enterprise edition is sold via an annual subscription license based on the number of developer seats and/or the environment scale. The exact model can vary.
In essence, entry-level is free thanks to the open source model, but enterprise features require a paid license which can be a significant investment for a full enterprise rollout.
SAP Data Intelligence
SAP Data Intelligence is a hybrid data management and integration solution designed to orchestrate data across SAP and non-SAP environments. It’s often used by organizations that are already heavy SAP customers via ERP or CRM applications.
Typical use cases include loading and transforming ERP data into data lakes/warehouses, combining SAP and non-SAP data for analytics, and machine learning data pipelines using enterprise data.
Key features
- Integration of SAP and non-SAP data
A key strength is native connectivity to SAP sources using SAP’s own protocols and libraries, which should ensure high performance and correct handling of SAP data structures. It also connects to generic sources (databases, cloud storages, streaming platforms) so it can serve as a bridge between SAP and other ecosystems. - Pipeline modeling environment
SAP Data Intelligence provides a web-based visual pipeline designer in which users can assemble operators for extracting, transforming, and loading data. It supports both batch and streaming data flows. Under the hood, it uses Kubernetes and Docker containers to run these pipelines, with support for distributed processing. - Metadata management and catalog
The platform emphasizes metadata-driven integration. It has a data catalog that can harvest metadata from connected systems, and it enables data lineage tracking and impact analysis. This metadata-centric approach aligns with SAP’s data fabric strategy, helping enterprises understand and govern their data landscape. - Machine Learning integration
SAP Data Intelligence also includes features for operationalizing machine learning. This is particularly useful if you’re already using SAP’s AI or integrating with Python/R environments for data science. - Deployment and integration suite
SAP Data Intelligence can be deployed on SAP’s cloud or on customer-managed Kubernetes clusters (either on-prem or private cloud). Additionally, SAP’s broader Integration Suite includes not just Data Intelligence but also application integration, providing a unified integration approach for both data and application integration needs within the SAP world.
Strengths
You might be seeing a pattern here, because SAP’s integration tools are strongest for companies already in the SAP ecosystem. They provide excellent integration with SAP’s own applications and databases, often at a level of depth that third-party tools struggle to.
SAP’s solution also caters to modern wishes like containerization, cloud deployment, and enabling data science.
Weaknesses
SAP is not cheap. The licensing and infrastructure costs can be significant, which might only be justifiable for large SAP-centric organizations. Also, while SAP Data Intelligence does connect to non-SAP sources, some users find it less friendly for non-SAP data or that certain adaptors are not as mature as those for SAP sources.
In other words, if your landscape is mostly non-SAP with just a bit of SAP, a third-party tool might be a better fit.
Pricing model
SAP Data Intelligence is typically licensed as a subscription or as part of SAP’s broader licensing agreements. Services are often metered by capacity units or similar metrics. For an on-prem deployment, it might be licensed per node or per throughput. SAP’s pricing is not public; it usually requires engaging SAP sales, but it’s premium-level pricing. You can get discounts if you buy multiple products.
Mulesoft
MuleSoft’s Anypoint Platform is a leading integration solution focused on API-led connectivity.
MuleSoft, owned by Salesforce, enables organizations to build APIs and integrations that connect applications, data, and devices, whether on-premises or in the cloud. It is often chosen for large-scale digital transformation projects where an org needs a strategic API architecture is desired to expose legacy systems via APIs, build microservices integrations, or create an integration layer for omni-channel experiences.
MuleSoft supports hybrid deployment (cloud or on-prem runtimes) and is utilized heavily in industries like banking (for open banking APIs), retail (connecting e-commerce and ERP), and government (integrating across agencies).
Key features
- API-led approach & ESB
MuleSoft provides an enterprise service bus (ESB) and integration runtime where users can develop flows that connect systems at the application/API level. It encourages designing integrations as APIs (Experience APIs, Process APIs, or System APIs) which can be reused. - Hundreds of connectors
The platform has a large library of connectors for databases, protocols, SaaS apps, and legacy systems. These connectors handle the annoying details of interacting with different systems, so developers can focus on transformation and orchestration. - Real-time integration & messaging
MuleSoft is built for real-time, synchronous, or asynchronous integration. It can handle high-volume API calls, event-driven architectures (via JMS or MQ connectors, or via their newer Async APIs), and streaming. - Cloud, on-prem, and hybrid deployment
MuleSoft offers a cloud iPaaS called CloudHub where integrations run in MuleSoft-managed cloud workers, as well as the ability to run on-premises or in a customer’s cloud. This flexibility allows compliance with any data residency or latency requirements. Many enterprises run MuleSoft in a hybrid model. - Full API lifecycle & management
A major part of Anypoint Platform is API Manager and Exchange. API Manager handles applying security policies to APIs (OAuth, rate limiting, etc.) and provides analytics on API usage. Anypoint Exchange is a repository for publishing APIs and integration assets for reuse within an organization (promoting a marketplace of APIs concept).
Strengths
MuleSoft is often considered the gold standard for enterprises embracing an API strategy.
Its strengths include robustness, scalability, and a rich feature set for integration. It’s highly customizable and flexible, and it handles complexity well including complex orchestration across many systems, transactions, error recovery, and more.
In addition, MuleSoft’s API management capabilities are top-notch: you can build an ecosystem of managed APIs fairly easily. The platform is also vendor-neutral in connectivity; it’s designed to integrate anything.
Also, it’s highly scalable: MuleSoft is used in mission-critical, high-transaction environments.
Weaknesses
The primary downside is cost. MuleSoft is known to be one of the more expensive integration platforms on the market. Its pricing, especially after Salesforce’s acquisition, is typically aimed at large enterprise budgets. For small or mid-sized organizations, this can be a barrier.
MuleSoft also has a learning curve. While basic integrations are straightforward, fully leveraging the platform requires skilled developers and architects. It’s a heavyweight solution, so using it for simple tasks might be overkill.
Pricing model
MuleSoft is sold via an annual subscription that typically is based on the number of cores or vCores allocated to your Mule runtimes, along with a limit on APIs or transactions in some cases. Essentially, you purchase capacity to run a certain number of Mule applications. The pricing tends to start in the six-figures (USD) annually for enterprise-level packages, and goes up from there depending on scale.
Top data integration platforms: summing up
There are plenty of top data integration platforms. 2 that we haven’t mentioned include Boomi and SnapLogic, both of which have significant solutions of their own.
Typically, these are heavy lifts: big platforms with big prices and big integration timelines. But, if that’s what you need, that’s what you get.
For simpler data movement jobs — and much lower cost — Extract is a great solution, and can work in concert with these big data integration platforms in ways that can reduce your cost.