Heroku is Ending Enterprise Sales: What’s Next for Unifying Salesforce Data?

Why modern enterprises are moving beyond legacy PaaS and how Odaseva Data Edge powers scalable AI, BI, and Salesforce data apps

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Back in 2007, Heroku helped define a new way of building applications. As one of the earliest Platform-as-a-Service (PaaS) offerings, Heroku gave developers a simple, Git-based way to deploy apps without worrying about infrastructure. When Salesforce acquired Heroku in 2010, it was investing in a developer ecosystem that could extend the value of Salesforce data through custom applications.

Now that story appears to be shifting. As of February 6, Heroku is effectively in maintenance mode, with no new enterprise contracts and is prioritizing quality and operational excellence, “rather than introducing new features.”

“Enterprise Account contracts will no longer be offered to new customers.” 

- Source: Heroku Linkedin

Meanwhile, Salesforce is prioritizing newer platforms like Data 360 (formerly Data Cloud), signaling a clear strategic transition. This leaves thousands of organizations at a crossroads: they can continue using an aging, increasingly obsolete platform, or move forward with tools that may not fully address the realities of enterprise-scale data integration and AI readiness.

The Risks of Continuing with an End Of Sale Platform

From both a technology and security standpoint, relying on any product that’s not introducing new features or acquiring new enterprise customers introduces compounding risks that grow over time.

This is the issue of stagnation. When a platform stops growing, it cannot keep pace to evolve with modern requirements such as handling large-scale data pipelines, supporting AI workloads, or integrating with newer ecosystems. Over time, gaps widen between what the customers need and what the stagnating product can deliver.

Security risks also tend to increase. Products that don’t gain new customers (and therefore new revenue sources) often lack timely updates, which makes them potentially vulnerable to security threats. This is especially problematic for organizations handling sensitive data (PII, PHI, etc.) because the risk of data breaches, compliance failures, and operational inefficiency are intolerable. Often as a result of missing functionality, teams compensate by building workarounds. These patches can add complexity, increase maintenance overhead, and reduce system reliability. 

Finally, there is also a strategic risk in investing in a stagnating platform. Every dollar spent maintaining it is a dollar not spent building future-ready capabilities like AI-driven insights, real-time analytics, or scalable data architectures.

Heroku’s Product Limitations in a Modern Data World

Heroku was built for a different era and a different purpose, and its architecture reflects that.

At its core, Heroku is optimized for stateless application deployment rather than data engineering. Features like ephemeral dynos and lightweight compute environments work well for web apps, but they introduce instability for persistent data pipelines as long-running ETL jobs can be interrupted, restarted, or slowed due to resource constraints. 

From a technical perspective, Heroku Connect was designed as a near real-time synchronization layer between Salesforce and Heroku Postgres, and while innovative at the time, data pipelines were fragile. As a largely single-threaded system, it struggles with schema changes, validation errors, and incremental data loads so a single issue could disrupt the entire pipeline. Also, it has limited scalability as it was never built for Large Data Volumes (LDV): high-throughput or multi-terabyte workloads quickly hit performance ceilings. 

In addition, despite being part of Salesforce, Heroku saw limited progress in advanced synchronization or extraction capabilities for Salesforce data over the last decade, making it hard to justify its de-facto status.

Finally, Heroku has some work to do with scalability. As scaling requires adding more dynos, which increases cost without delivering true distributed processing power, it simply isn’t adjusted to LDV transactions for modern AI/BI. 

From a business perspective, these technical constraints translate into real challenges. Costs rising due to dyno-based and row-based pricing reminds us that Heroku was built to process a subset of Salesforce data for traditional BI or tactical apps, but when it comes to sharing Salesforce data at scale (customer facing apps, GenAI, Agentic, enterprise-grade analytics), costs skyrocket and performance bottlenecks slow down complex transformations. To add to that, vendor lock-in and interoperability with broader data ecosystems such as lakehouses remains limited. 

Heroku is simply no longer aligned with how modern enterprises manage and activate data.

Why Odaseva Data Edge Is the Smart Alternative

This is where Odaseva Data Edge, built on a modern lakehouse architecture, comes in.

• Data Edge supports uncapped scale for enterprise LDV. Unlike single-process limitations, it can process hundreds of millions of records per hour. This makes it suitable for analytics, AI training, real-time processing, and cross-system integration. 

• Data Edge provides predictable and transparent pricing. Heroku’s cost model becomes expensive as data grows, while Data Edge offers a fixed approach that removes the data tax associated with scaling. It introduces a zero-copy architecture for efficiency. Instead of repeatedly extracting data from Salesforce, it repurposes existing backup streams into live, read-only datasets for modern lakehouses. This reduces system load and eliminates unnecessary duplication.

• Data Edge enables unlimited data reuse across use cases. The same dataset can power analytics, AI models, and operational workflows without repeated transformation or duplication. 

• Data Edge delivers unified data lifecycle management. Historical data can be offloaded from Salesforce while still being retained in a lakehouse. This keeps CRM systems lean while preserving the historical context needed for accurate analytics and AI. 

Data Edge is built for AI and the future of data in supporting high-quality model training, real-time enrichment, and advanced AI use cases that require scale and reliability.

The Bigger Picture? Rethinking your Salesforce Data Strategy

There’s a broader need to rethink how enterprises manage and use their Salesforce data. A positive outcome of this unfortunate update is that organizations are now asking important questions, like:

• How can we repurpose Salesforce data for AI and BI?
• Which use cases should we try to enable in our organization (analytics, automation, customer insights), and for whom?. 
• Where is our data going and how efficiently does it move across systems?
• What are our challenges related to scale, cost, and reliability?
• Who consumes this data and how critical it is to business operations?
• How can we expand these use cases, and which platforms do we need that can grow with us (avoiding tools that are no longer viable)? 

These strategic decisions determine whether an organization can compete in a data-driven and AI-powered world, and are forcing the conversation in C-suites and boardrooms.

Moving Beyond Heroku to Power the Next Generation of Salesforce data

Heroku played an important role in shaping modern application development, but its ending of enterprise sales signals an important shift in the ecosystem. As Salesforce changes focus and enterprise data needs evolve, organizations must adopt platforms that can handle scale, complexity, and power AI-driven innovation. 

Odaseva Data Edge provides that foundation by connecting Salesforce data with enterprise analytics and AI ecosystems.

If you are navigating questions about Salesforce data strategy, AI readiness, or the future of your data stack, this is the time to act. Explore Odaseva’s latest innovations announced at Dreamforce 2025, learn more about Data Edge, and begin planning your transition away from Heroku. The future of enterprise data depends on building systems that can scale, adapt, and unlock the full value of your data and Odaseva is here to help you achieve this potential. Get a demo today or if you’re attending TDX, let’s have this conversation in person. See us at Booth #2 (Level 2) or schedule a 1:1 meeting, or attend our other TDX events. Learn more here.