DataStori vs Fivetran
DataStori and Fivetran are SaaS platforms that extract, load and transform (ELT) data from cloud applications. Businesses use the transformed data to drive their AI modeling, analytics and reporting.
This article highlights key differences between DataStori and Fivetran to help users decide which one is better suited to their requirements.
DataStori vs Fivetran
The below table compares the platforms on the key dimensions cited by users when deciding which ELT platform to buy. These include agentic and automation capabilities, data security and compliance, pricing and ease of use. The information on Fivetran is taken from their pricing and deployment pages as of June 26, 2026.
| Dimension | DataStori | Fivetran |
|---|---|---|
| AI / agentic capability | Natural language pipeline creation, auto-discovery, auto-documentation, auto-transformation | Agentic flow in beta covers REST APIs that return JSON responses |
| Pipeline execution | In user's AWS, Azure or Google cloud; data never leaves the user's IT environment | In Fivetran cloud; only their Enterprise license executes in the user's IT environment |
| Compliance | SOC 2 Type 2; AES-256, column-level encryption, MFA, VNet isolation | SOC 2; PCI DSS Level 1 and customer-managed keys in the Business Critical license |
| Pricing | $250/month per application; no limits on pipelines and data volume | By monthly active rows (MAR); increases with number of rows inserted, updated or deleted |
| Trial period | 14-day free trial; no credit card needed | 14-day trial on new connections, plus free plan up to 500,000 MAR for one application |
| Connected applications | a. No limit - AI auto-discovered from source APIs, database connections and other sources to build connectors on the fly; b. Pre-built connectors include NetSuite, SAP, Salesforce, HubSpot, Dynamics 365 | Limited to 700+ pre-built connectors supported by Fivetran; agentic flow for REST APIs returning JSON responses (in beta) |
| Data transformation | Auto-generated SQL with output written to an analytics-ready gold layer | dbt Core integration; users author the models |
How does connector cost change with usage?
Fivetran's cost is based on monthly active rows (MAR), which is the count of data rows inserted, updated or deleted in a calendar month. Its pricing starts with a free plan for up to 500,000 Monthly Active Rows (MAR) for one connected application. The Standard plan starts at ~$500/mo per million MAR, growing to ~1,067/mo per million MAR in the Business Critical plan.
This pricing works well for a single application with low data volumes but rapidly gets expensive and hard to estimate as data grows. A single high-churn table can easily move users between pricing bands. A small team with three applications can expect to pay $1,500-$3,000/mo, while enterprises with over 10 applications could pay upwards of $10,000/mo. See Fivetran Pricing Guide for more details.
DataStori charges a fixed fee of $250/mo per application with unlimited pipelines and unlimited data volume. Cost is driven by the number of source applications connected, not by how much data flows through them. This makes DataStori attractive at all volumes. In the above comparison, DataStori will cost a small team with three connected applications $750/mo, while an enterprise with 10 applications will pay just $2,500/mo.
Where does user data get processed and stored? Why does it matter?
Fivetran's plans process user data on its servers before loading it to the user's warehouse; only its Hybrid Deployment option, available on Enterprise plans, processes data inside the user's network.
DataStori executes pipelines in the user's AWS, Azure or GCP cloud while orchestrating them from its AWS cloud. Data never leaves the user's cloud, eliminating the extra data hop found in Fivetran plans. DataStori can only access pipeline metadata, never the business data itself.
Data residency in-house is a big win for DataStori users because it ensures that their data can stay compliant with their security and privacy policies. For companies with data sovereignty or sector-specific compliance requirements, keeping their data in-house at all times adds a crucial layer of safety.
What do DataStori's AI agents do? How do they help ingest and transform data?
DataStori's AI agents allow users to enter business requirements in plain language, for example, 'run sales forecasting using NetSuite data'. The AI agents jump in and automate the entire process from data discovery to transformation. They auto-discover relevant source endpoints, create pipelines, generate documentation and transform the data to meet the stated business requirement. Auto-discovery reads a source's API documentation on the fly, so DataStori's coverage is not limited by a pre-built connector list.
Fivetran has a supported library of 700+ applications and a Connector SDK for building new ones in Python. There is an agentic flow (in beta) for other connectors that users can build and manage for themeselves. If the required data sources are all on Fivetran's connector list, that may provide stability to the process. If they are not, DataStori's agentic auto-discovery will support new applications with reliability and speed.
In summary, choose DataStori when you need full control over your data residency, affordable and predictable pricing, and the capability to connect to virtually any data source. Choose Fivetran for the broadest pre-built connector catalog and proven scale at very high data volumes.
When should one pick Fivetran over DataStori?
Fivetran may be the better choice when:
- Users need to connect to a few large SaaS sources with pre-built and vendor-maintained connectors. Fivetran's 700+ connector catalog is currently much bigger than DataStori's named-integration set.
- Cost of connection and usage is not an issue.
- The data team has standardized transformations on dbt Core and wants tight native integration with that workflow.
For all other situations where predictable pricing, data residency and ease of use matter more than catalog breadth, DataStori is the preferred application. These trade-offs are discussed in our iPaaS vs ELT breakdown too.
Frequently asked questions
Is DataStori cheaper than Fivetran?
Almost always. DataStori charges a flat $250 per month per application regardless of how many rows move, while Fivetran charges by monthly active rows per application. For high-volume or high-churn sources, DataStori's flat model is more predictable and cost-effective; for very low-volume data transfers from a single source, Fivetran's free plan or low usage tiers may cost less.
Does Fivetran keep my data in my cloud? What about DataStori?
Fivetran processes data on its servers before loading it to your destination, except in its Hybrid Deployment option (Enterprise plan) where your data stays in your cloud. DataStori runs all your data pipeline in your cloud, ensuring that you data never leaves your cloud.
Does Fivetran have AI pipeline creation like DataStori?
No. Fivetran relies on a library of 700+ pre-built connectors and a Connector SDK for custom Python connectors. DataStori adds an agentic layer: you describe what you need in plain language and AI agents discover endpoints, build the pipelines, generate documentation and write the final output to your cloud.
Which tool has more connectors?
Fivetran has the larger pre-built catalog, with 700+ vendor-maintained connectors. DataStori takes a different approach: AI auto-discovery reads a source's API documentation directly, so it can connect to applications that are not on a fixed connector list, besides pre-built integrations like NetSuite, SAP, Salesforce and HubSpot.