Hevo vs Fivetran: Which ELT Solution Will Drive Your Data Strategy Forward?

Are you trying to consolidate massive datasets from multiple sources into a single, unified cloud warehouse? Do you need a tool that automates data flows across diverse platforms while ensuring your information stays fresh with minimal manual effort? Or are you tackling complex, large‑scale data transformations that demand a highly adaptable solution?

Today, platforms like Fivetran and Hevo Data have emerged as popular choices for teams looking to centralize and streamline their data pipelines. Both are powerful, no‑code, automated ELT platforms, but when it comes to security, scalability, flexibility, and customization, they have distinct differences that can have a big impact on your organization.

So, how do you pick the right one? In this guide, we’ll compare Fivetran and Hevo Data across these critical areas and help you decide which platform is the best fit for your team, your data, and your long‑term objectives.

What is Hevo ?

Hevo Data is a fully managed, no-code ELT platform that makes data movement simple, reliable, and scalable. It helps teams connect over 150 sources to leading data warehouses in minutes, without requiring engineering effort or ongoing maintenance. 

With automated scaling, fault-tolerant pipelines, transparent pricing, and complete end-to-end visibility, Hevo ensures your data flows seamlessly and stays trustworthy at every stage.

Its near‑real‑time capabilities and growing list of connectors have earned it the trust of over 2,000 customers worldwide, including names like DoorDash, Shopify, and ThoughtSpot. 

Key Features

1. Easy to Use

Get started in minutes with a guided, no-code setup that requires no scripting or infrastructure management. Build, monitor, and scale data pipelines through a simple visual interface designed for speed and ease.

2. Scalable

Hevo automatically scales to handle growing data volumes and high-throughput workloads without downtime or manual tuning. Its performance-first design ensures consistent speed, even as pipeline complexity increases.

3. Reliable

Built for resilience, Hevo features auto-healing pipelines, intelligent retries, and a fault-tolerant architecture that keeps data flowing even when sources fail. Automatic schema handling adjusts to API or structure changes without breaking workflows.

4. Predictable Pricing

Hevo’s event-based pricing model provides complete cost transparency, allowing teams to forecast spend accurately as data scales. There are no hidden fees, usage credits, or surprise overages, only clear and consistent billing.

5. 360° Visibility

Track every pipeline in real time through unified dashboards, detailed logs, and data lineage views. Batch-level checks help detect anomalies early, keeping your data accurate, consistent, and fully trustworthy across all systems.

We did a proper evaluation between Hevo and its competitors. We realized that Hevo provided the best value out of all of them; it had all the features that we wanted at a price that we were comfortable with. It was the best option for us.

Prudhvi Vasa

Head of Data

View Customer Story

What is Fivetran?

Fivetran is a fully managed ELT platform designed to simplify data movement across hundreds of sources with minimal manual effort. Known for its extensive library of prebuilt connectors, it allows teams to centralize data quickly without managing infrastructure or pipeline maintenance.

Fivetran is widely known for its managed ELT approach and library of prebuilt connectors, making it easy to get data flowing quickly. It’s a good choice for teams that want a hands-off setup.

However, many teams soon hit roadblocks with unpredictable pricing, inconsistent support responses, and limited customization. Alternatives now offer more flexibility, transparent pricing, and control over how data flows.

Key Features

• Transformation: Fivetran uses a dbt Core integration to run transformations after data is loaded into your warehouse.
• Connector Library: Offers a wide range of prebuilt connectors, though users have limited flexibility over what data is extracted or excluded.
• Data Syncs: Runs automated batch-based ELT jobs; real-time syncs are restricted to higher enterprise plans.
• Schema handling: Detects and updates source schema changes automatically.
• Compliance: Includes SOC 2 Type II and HIPAA compliance for data protection.

Fivetran makes syncing data from multiple SaaS tools to data warehouses like BigQuery fast and effortless. With plug-and-play connectors, automatic schema management, and strong alerts, it saves our small team hours of manual work.

Dennis C.

Head of Business Operations

Both platforms can supercharge data pipelines, but which one is the right fit for your team? Let’s break down their key features, differences, and ideal use cases.

Hevo vs. Fivetran –  A Detailed Feature Comparison

Both Hevo Data and Fivetran are strong contenders for extracting, loading, and transforming data across platforms. Yet, while they have overlapping core capabilities, they appeal to different types of organizations due to their approach, pricing, and feature focus.

Real Feedback from Users

For Cost‑Conscious Teams and High Volume and Throughput Cases: 

Hevo is an excellent fit for businesses that want a robust data integration solution without unpredictable pricing. Its subscription‑based model and free tier for up to one million events give teams a clear picture of their costs, making it ideal for growing or mid‑sized businesses where cost control is a priority. Its pricing and ease of use mean you can scale data pipelines reliably while avoiding the surprises that often come with usage‑based pricing. 

Plus, Hevo excels when it comes to reliably handling large, high‑velocity datasets. Its near‑real‑time streaming, built‑in CDC capabilities, and dynamic schema mapping enable seamless replication even as data volumes grow. 

For Complex, Enterprise‑Scale Data Needs:

Fivetran shines in environments where complexity is the norm. Its expansive library of over 700 fully managed connectors allows it to integrate seamlessly with a huge range of SaaS platforms, databases, and cloud services.

Coupled with deep dbt and Census integrations, it’s ideal for enterprise‑level data engineering teams managing complex, multi‑source environments where precision, reliability, and scalability matter. It’s built for organizations that want a tool that can adapt to intricate data models and sophisticated analytics pipelines.

If you are looking to switch from Fivetran, check out our blog on Top 11 Fivetran Alternatives.

Hevo vs Fivetran : In-depth Feature & Use Case Comparison

If you’re evaluating Hevo and Fivetran, you’re likely dealing with growing data pipelines or a complex enterprise environment. The best choice depends on your priorities: speed and simplicity vs. scale and depth of enterprise features. 

Here’s how they compare across four critical areas, with a clear takeaway for each.

1. Replication Modes

Hevo supports three replication modes:

Replication Mode	How It Works	Key Benefit
Log-Based Replication	Uses WAL (Write-Ahead Logs) with TEST_DECODING	Highly efficient and low impact on production systems
Table Mode	Executes SQL on the database to extract changed rows based on a modified timestamp column	Works even if WAL is unavailable
Custom SQL	Allows writing custom queries to extract only the needed data	Ideal for selective or complex data extraction

Hevo is designed to reduce load on production databases while maintaining reliable replication. This makes it a strong choice for teams working with high-traffic, performance-sensitive systems or rapidly scaling datasets.

Fivetran offers two replication modes:

Replication Mode	How It Works	Limitation
Teleport Syncs	Captures a snapshot of the full table and compares it with the previous snapshot to detect inserts/updates/deletes	Requires primary keys and becomes resource-heavy and slow with very large or fast-growing tables
Log-Based Replication	WAL with Pgoutput, WAL with TEST_DECODING, and Xmin	Runs in batch mode, flushing logs only after replication completes, which reduces reliability during sudden data spikes or high data volume

Teleport Syncs works well only when tables are moderately sized and changes are predictable. Fivetran’s log-based replication can struggle with high data volume or unpredictable spikes due to its batch-flush mechanism.

Choose Fivetran if you have moderate data sizes, predictable batch-based replication needs, and a schema that fits Fivetran’s conditions.

2. Real-Time Streaming

Hevo is designed as a stream-first platform, built on Kafka. This enables near real-time data movement – a strong fit for teams that depend on fast updates and continuous pipelines.

Fivetran, by contrast, operates primarily in batch mode. Most customers experience hourly data transfers, which works well for predictable workloads that don’t require instant updates, but does not deliver real-time streaming for all use cases.

Users report that Hevo’s architecture offers greater flexibility and responsiveness for real-time data pipelines.

3. Schema Management

Fivetran follows a strict 1:1 schema approach. It reproduces the source schema and table structure exactly in the destination.

This is useful if you want a fully faithful replica – but it limits flexibility when working across multiple databases or namespaces.

Hevo provides a much more customizable schema experience, including the ability to:

• Push multiple databases or schema namespaces into a single destination schema
  
• Merge identical tables across different sources into one consolidated table
  
• Apply prefixes to destination tables to standardize naming across the organization
  

4. Transformation Capabilities

Fivetran supports limited pre-load transformations through its UI (e.g., dropping columns or masking fields).

Most data shaping happens after loading, via dbt, for more advanced transformation logic.

Hevo offers more robust pre-load transformation options, enabling users to shape the data before it reaches the warehouse — via drag-and-drop workflows or custom Python scripts. With Hevo, teams can:

• Clean and filter data early
  
• Convert currencies or measurement units
  
• Exclude unwanted rows
  
• Save warehouse compute cost by loading only relevant, properly formatted data

For post-load transformations, both Hevo and Fivetran integrate with dbt Core.

5. Pricing and Cost of Ownership

Hevo’s transparent, event-based tiers make it easy for teams to predict and manage monthly costs. At the same time, Fivetran’s MAR-based pricing is opaque and unpredictable for teams handling large or fast-growing datasets.

Below is a clear side-by-side comparison of how both platforms structure their pricing:

Aspect	Hevo Data	Fivetran
Pricing Model	Transparent subscription pricing with fixed event limits	MAR-based pricing; costs increase with data volume
Free Plan	Free Trial: 1M events; 50+ SaaS connectors	Free Plan: 500K MAR; limited data throughput
Entry-Level Plan	Starter: $239/month for 5M events; supports SaaS + some DBs	Starter: 1-hour syncs; does not include database connectors
Mid-Tier Plan	Professional: $679/month for 20M events; supports streaming + low latency	Standard: DB connectors + 15-min syncs; some connectors still restricted
Enterprise Plan	Business Critical: real-time streaming, RBAC, SSO, VPC Peering, advanced security	Enterprise: 1-min syncs, premium connectors; may use HVR for real-time
Cost Predictability	Highly predictable; minimal billing surprises	Harder to estimate; MAR usage can spike unexpectedly
Best For	Teams wanting clarity and predictable scaling	Teams needing enterprise connectors and fast syncs

6. Security and Compliance

Hevo supports a wide range of global regulations, including GDPR, HIPAA, SOC 2, CCPA, and DORA, making it suitable for regulated industries and European financial services. 

Fivetran provides robust compliance as well but does not include DORA and offers less flexibility in how global teams isolate or segment data environments.

Category	Hevo Data	Fivetran
Compliance	GDPR, HIPAA, SOC 2, CCPA, DORA	SOC 2 Type 2, PCI-DSS, ISO 27001, HIPAA, GDPR (no DORA)
Data Security	AES encryption at rest and in transit	PrivateLink (AWS, Azure, GCP) supported
Authentication	SAML 2.0, 2FA, SSH, reverse SSH, VPC peering	SAML 2.0, 2FA, SSH, reverse SSH, VPC peering
Workspaces	Up to five isolated workspaces per domain	Single workspace setup
Global Flexibility	Strong multi-region and multi-team setup	Limited workspace flexibility
Error Detection	Automated error correction + Slack/email alerts	Similar automated correction + alerts

Fivetran performs well for enterprise teams but may feel restrictive for organizations that operate across multiple geographies or need several isolated environments.

Hevo’s multi-workspace setup makes it especially valuable for multinational teams that need strict regional separation and isolation.

Hevo vs Fivetran – Which ELT tool should you pick?

Hevo Data is ideal for mid-market teams that want simplicity, real-time syncing, flexible schema control, and predictable pricing without heavy engineering effort. Its Kafka-based streaming and strong pre-load transformations make it easy to deliver clean, ready-to-use data at scale.

With Hevo Data, you create a connection out of 1000 objects, select 10 objects you really need, apply minimal transformations, and Hevo Data will take care of it. It will run every day, send you notifications, you can go back, you can restart the pipeline, and it will just refresh the data.

Karan Singh Khanuja

Data Scientist

View Customer Story

Fivetran is better suited for large enterprises that need extensive connector coverage, enterprise governance, and advanced integrations like dbt and Census. Its breadth and depth fit complex data stacks, though its batch-based syncs and usage-driven pricing may be less suitable for smaller or cost-sensitive teams.

Factor	Hevo Data	Fivetran
Best For	Mid-market teams, growing orgs, and fast-moving data teams	Large enterprises with complex, multi-source ecosystems
Setup Speed	4-step, minutes-long setup	More involved; requires existing data engineering maturity
Data Freshness	Near real-time streaming (Kafka-based)	Primarily batch-based; hourly syncs for most customers
Schema Flexibility	Merge databases, unify tables, add prefixes, flexible destination design	Strict 1:1 schema replication from source to destination
Transformations	Strong pre-load transformations (UI + Python)	Limited pre-load transformations; relies on dbt post-load
Pricing Model	Transparent, event-based, predictable	Volume-based; can grow expensive as data scales
Connector Coverage	Good coverage for SaaS, DBs, files	Very broad (700+ connectors) – ideal for enterprises
Security & Compliance	SOC 2, GDPR, HIPAA, CCPA, DORA; multi-workspace support	SOC 2 Type 2, PCI-DSS, HIPAA, ISO 27001; limited workspace flexibility
Ideal Use Case	Teams wanting ease-of-use, real-time data, flexibility, and cost clarity	Enterprises requiring wide connector coverage and strict governance

Frequently Asked Questions