Hevo Data

No-code ELT data pipeline for centralizing fragmented SaaS data

Analytics 4.4 / 5 Free tier / ~₹12,500/mo Updated Mar 2026
🇮🇳 The Indian challenger to Fivetran

Quick Verdict

Hevo is a globally recognized, Indian-built data pipeline platform. It syncs data from all your scattered SaaS applications (Razorpay, HubSpot, Facebook Ads) and production databases directly into your central data warehouse (Snowflake, BigQuery) in real-time, with zero coding required. It has emerged as the most dangerous and cost-effective competitor to Fivetran on the market today, democratizing ELT for mid-market teams without massive data engineering budgets.

Quick facts: Founded in 2017 by Manish Jethani & Sourabh Agarwal · Headquartered in San Francisco & Bengaluru · Backed by $43M in funding (Peak XV Partners) · Approaching $44M–$47M ARR in 2026 · Built on an event-based pricing model that scales predictably.

Ease of Use
4.8
Value for Money
4.5
Reliability
4.4
Connector Depth
4.2
Indian Support
5.0

What is Hevo Data?

As an Indian startup scales, its data naturally becomes heavily fragmented. The marketing team has acquisition cost data trapped in Google Ads. The sales team has CRM data trapped in HubSpot. The finance team has revenue data trapped in Razorpay. The product team has behavioral data trapped in Mixpanel.

If the CEO asks, "What is the ROI of our Facebook Ads based on actual finalized Razorpay settlements minus refunds?", answering that question requires a data engineer to write custom Python scripts connecting three different APIs. Those bespoke APIs break constantly, causing data outages and wasting engineering sprints on maintenance.

Hevo Data is a No-Code ELT (Extract, Load, Transform) platform that fixes this. Founded in 2017 by Manish Jethani and Sourabh Agarwal, Hevo eliminates custom scripting. You log into Hevo, authenticate your Facebook Ads account and your Razorpay account, and Hevo automatically extracts all that data every few minutes, dumping it cleanly into a central Google BigQuery or Snowflake warehouse. From there, your analytics team can run standard SQL or use a tool like Metabase to build the exact cross-platform ROI chart the CEO requested.

Key Features That Matter

150+ Plug-and-Play Connectors

Connect natively to almost every major database (PostgreSQL, MongoDB, MySQL) and SaaS application (Salesforce, Stripe, Zendesk, Jira) without writing API integration code.

Near Real-Time Syncing

While many legacy pipelines update data via batch jobs once every 24 hours, Hevo uses micro-batching, syncing data from source to destination as fast as every 5 minutes.

Auto-Schema Mapping

If an engineer adds a new column (like user_kyc_status) to your production Postgres database, Hevo detects the schema change and adds the column to your Snowflake warehouse automatically.

In-Flight Transformations

Before data lands in your warehouse, use Hevo's UI or Python snippets to clean it. For example, hashing sensitive PII (like email addresses) before it reaches the analytics warehouse.

Reverse ETL (Hevo Activate)

Hevo can push data back out. If BigQuery identifies "Users likely to churn," Hevo Activate pushes that list back into Intercom or CleverTap to trigger a retention campaign.

Alerts & Monitoring

Comprehensive Slack and email alerting if an API endpoint rate-limits you or a data sync stalls, ensuring data teams are never caught off-guard by broken pipelines.

Pricing Breakdown (2026 INR Context)

Hevo Data charges based on "Events" (a row of data synced). Their pricing is highly transparent compared to enterprise legacy tools. Note: Converted at 1 USD = ₹84. Excludes 18% GST.

Free Tier

₹0
Free forever
  • ✅ 1 Million events / month
  • ✅ Access to 50+ free connectors
  • ✅ Basic Postgres/MySQL sync
  • ⚠️ Limited frequency (12 hrs)

Business Plan

Custom
Volume discounts
  • ✅ Billions of events scalable
  • ✅ Dedicated VPC deployments
  • ✅ HIPAA & SOC2 SLAs
  • ✅ Dedicated Account Manager

Who Should Use Hevo Data?

Mid-market to Series B Indian startups that are migrating away from querying a single production database and are actively setting up a proper Modern Data Stack (e.g., pulling everything into Google BigQuery or Amazon Redshift).

It is specifically built for companies that want to empower a Data Analyst to build their own pipelines without having to beg a Backend Software Engineer for API integration tickets.

Who should NOT use it: Pre-seed startups with very simple data needs. If all your data lives in one Postgres database and you just want to visualize it, use Metabase directly. You don't need a complex ELT pipeline tool until your data is fragmented across multiple third-party SaaS platforms.

Pros and Cons

Pros

  • Significantly more affordable than Fivetran for scaling startups.
  • Auto-schema mapping prevents pipelines from breaking when production databases change.
  • Extremely fast setup time; analysts can build pipelines in 30 minutes without code.
  • Indian engineering roots mean excellent support in IST timezones.
  • Includes basic Reverse ETL capabilities, saving you from buying a separate tool like Hightouch.

Cons

  • Connector catalog (150+) is slightly smaller than Fivetran's (400+), meaning obscure SaaS apps might not be supported.
  • Event-based pricing can unexpectedly spike if you accidentally sync massive log tables.
  • In-flight Python transformations can occasionally slow down the sync latency.

Need to Centralize Your Data?

If your product, marketing, and finance teams are all reporting different numbers, your data stack is broken. Let our data architects build a clean, automated pipeline from your SaaS tools into a central Snowflake or BigQuery warehouse.

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