We developed a pricing model that puts the data engineer first, and rewards using data best practices: you pay only for the workloads you run, no matter how much the data volume grows.

In our experience, data teams outgrow traditional EL tools’ entire approach to data movement, including their pricing models. These have historically been based on the (monthly) data volume in rows or GBs, which may work well when an organization is just starting out, but eventually penalizes growth by increasing cost faster than the perceived value. It can even prevent new medium-to-high-volume sources like databases and event streams from being added at all.

With lots of input from our community, we have landed on a pricing model that prioritizes fairness, predictability, and transparency and sits closer to the underlying compute resources. On Meltano Cloud, you can run a pipeline built out of hundreds of connectors, Python data tools, and custom scripts, and pay only based on the resources the pipeline used.

Meltano Cloud uses “credits” to represent the resources used by your pipelines. The number of credits a pipeline run consumes depends on its frequency and how much runtime or egress it uses.

Credits can be purchased in bulk, and the price per credit decreases the more credits you purchase.

Meltano Cloud uses “credits” to represent the resources used by your pipelines. The number of credits a pipeline run consumes depends on the factors:

1. The pipeline’s frequency
2. A pipeline run’s runtime 
3. A pipeline run’s egress (data transfer out)

​​Frequent pipelines (possibly containing multiple steps) that are scheduled to run hourly or more frequently consume 0.5 credits per run and come with 5 minutes of runtime and 75MB of egress included, which is typically sufficient for pipelines like this.

Infrequent pipelines that are scheduled to run less than hourly (or are triggered externally using the CLI or API) consume 1 credit per run and come with 10 minutes of runtime and 150MB of egress included, which is typically sufficient for pipelines like this.

Extra runtime beyond the included 5 or 10 minutes consumes 0.1 credit per minute, which includes 15MB of extra egress allowance. Extra egress (data transferred out by your pipeline run) beyond the included 15MB per minute of runtime consumes 0.01 credit per 10 MB.

For the full legal details, see our Fees documentation.

Meltano Cloud offers bulk discounts on credits. The price per credit decreases as the number of credits you purchase increases. Credits need to be purchased upfront for future usage.

Bulk purchases made during the beta will receive a 20% discount on the standard pricing going into effect with the GA launch on June 27.

Here’s how the bulk pricing looks, before the Beta discount:

Number of credits	Price per credit
0 – 3,000	$0.30 per credit
3,001 – 15,000	$900 for the first 3,000 credits ($0.30 on average) + $0.25 per additional credit
15,001 – 100,000	$3,900 for the first 15,000 credits ($0.26 on average) + $0.20 per additional credit
100,001 – 1,000,000	$20,900 for the first 15,000 credits ($0.21 on average) + $0.15 per additional credit
1,000,001 and beyond	$155,900 for the first 100,000 credits ($0.16 on average) + $0.10 per additional credit

Yes! You can configure the frequency of your scheduled pipelines using a cron expression, meaning you can exactly specify your desired schedule. This can be as simple as running every day, hour, or 6 hours, or something smarter like running every 30 minutes during the workday and every 2 hours during the night. The lowest frequency Meltano Cloud currently supports is every 15 minutes.

Meltano Cloud considers a pipeline’s frequency when determining each run’s credit consumption and included runtime and egress. Infrequent pipelines are those scheduled to run less than hourly, while frequent pipelines are scheduled to run hourly or more.

A frequent pipeline is scheduled to run hourly or more frequently. Each run of a frequent pipeline consumes 0.5 credits and includes 5 minutes of runtime and 75MB of egress (15MB per minute). 

Conversely, an infrequent pipeline is scheduled to run less than hourly, or is triggered externally using the CLI or API. Each run of an infrequent pipeline consumes 1 credit and includes 10 minutes of runtime and 150MB of egress (15MB per minute).

If a pipeline run uses more than the included runtime or egress (5 or 10 minutes, depending on the pipeline’s frequency), extra credits are consumed. Extra runtime costs 0.1 credit per minute and includes an extra 15MB egress allowance per minute. Extra egress beyond the included 15MB per minute of runtime costs 0.01 credit per 10 MB.

Usually not. For the vast majority of pipelines we’ve seen on our platform, the included runtime and egress are sufficient.

​​Frequent pipelines (possibly containing multiple steps) that are scheduled to run hourly or more frequently consume 0.5 credits per run and come with 5 minutes of runtime and 75MB of egress included.

Infrequent pipelines that are scheduled to run less than hourly (or are triggered externally using the CLI or API) consume 1 credit per run and come with 10 minutes of runtime and 150MB of egress included.

To estimate the cost of a new data source, you only need to know the number of pipelines you’ll run and how frequently you’ll run them. Typically, the included runtime and egress suffice for most pipelines, so these are the main factors you’ll need to consider. 

Instead of calculating by hand, our handy Cost Calculator lets you easily estimate your credit consumption and spend, compare it with other EL tools, and unhide hidden cells to see the full calculation.

If you’ve gotten used to a volume-based model, our model may look complicated, but it makes it very easy to predict how much your pipelines will cost: the included runtime and egress are typically sufficient, so the only thing you need to know is how many pipelines you have, and how often you’d like them to run. Let’s see some examples:

• A daily sync from HubSpot into Snowflake
  • As this is an infrequent pipeline (less than hourly), each daily run will cost 1 credit and come with 10 minutes of runtime and 150MB of egress included (15MB per minute).
  • As these included amounts are typically sufficient for pipelines like this, there is no additional charge for runtime or egress.
  • The monthly consumption will thus be 30 credits, costing 30 * $0.24 = $7.20 during the Beta or 30 * $0.30 = $9 after.
• An hourly sync from a Postgres database into BigQuery
  • As this is a frequent pipeline (hourly or more), each run will cost 0.5 credits and come with 5 minutes of runtime and 75MB of egress included (15MB per minute).
  • These included amounts are typically sufficient for pipelines like this, but for the sake of the example let’s imagine each run actually takes 6 minutes and uses 100MB of egress. The extra minute costs 0.1 credit, but also increases the egress allowance by 15MB, to 90MB. The extra 10MB cost 0.01 credits.
  • The cost per run is thus 0.5 + 0.1 + 0.01 = 0.61 credits. Since these are daily runs, that’s 24 * 0.61 = 14.64 credits per day.
  • The monthly consumption will then be 30 * 14.64 = 439.2 credits, costing 439.2 * $0.24 = $105.41 ($3.51/day) during the Beta or 439.2 * $0.30 = $131.76 ($4.39/day) after.
• A more complex pipeline running every 6 hours: The Google Sheets extractor, followed by a custom Python mapper to filter out PII on the fly, followed by the Redshift loader, followed finally by dbt to run transformations on the warehouse
  • As this is an infrequent pipeline (less than hourly), each run will cost 1 credit and come with 10 minutes of runtime and 150MB of egress included (15MB per minute).
  • As the pipeline consists of multiple custom steps, the total start-to-finish runtime is actually 15 minutes and uses 250MB of egress. The extra 5 minutes cost 0.1 credits each, so 0.5 credits, but also increase the egress allowance by 5 * 15MB = 75MB, to 225MB. The extra 25MB cost 0.01 credits per 10MB, so 0.025 credits.
  • The cost per run is thus 1 + 0.5 + 0.025 = 1.525 credits. Since the pipeline runs every 6 hours, that’s 6 * 1.525 = 9.15 credits per day.
  • The monthly consumption will then be 30 * 9.15 = 274.5 credits, costing 274.5 * $0.24 = $65.88 ($2.20/day) during the Beta or 274.5 * $0.30 = $82.35 ($2.75/day) after.

Instead of calculating by hand, our handy Cost Calculator lets you easily estimate your credit consumption and spend, compare it with other EL tools, and unhide hidden cells to see the full calculation.

When your credits get close to running out, Meltano Cloud will start notifying you so you can arrange for a new credit purchase and prevent any interruption.

If you ignore the notifications, fail to arrange for a new purchase, and your credits actually run out, Meltano Cloud will stop running your pipelines (and notify you of this fact) until new credits are purchased.

Purchased credits don’t expire as long as you continue using the Meltano Cloud platform. If we don’t see any meaningful activity on your account (like pipeline run attempts or credit purchases) for one year, your credits will expire. You will be notified before and when this happens.

No, there are no hidden charges. Everything from runtime, egress, data volume, and more is covered within the credits. We aim to keep the pricing model simple and transparent.

You can get real-time insights into your credit balance and usage through the Meltano Cloud CLI and web dashboard.

We provide a 99.9% uptime guarantee for scheduled pipelines. If a previously successful scheduled pipeline starts failing, and misconfiguration and source API issues are ruled out, this counts as downtime. If uptime falls below the SLA guarantee and you report this to us, you will receive double the credits of the failing pipeline runs in compensation.

In general, pipeline runs consume credits whether they succeed or not, as Meltano Cloud pricing is based on resource usage, and your pipelines can contain arbitrary code that’s your responsibility. We recommend testing your pipelines locally prior to running them on Meltano Cloud. Meltano Cloud lets you configure alerts for failures to enable you to debug and fix issues or temporarily disable a scheduled pipeline while you’re investigating.

For the full legal details, see our Service Level Agreement (SLA).

Long-running and/or high-volume pipelines will consume credits as described above, based on their frequency, runtime, and egress.

We recommend running one-off pipelines like this outside of Meltano Cloud (e.g. on your local machine or a one-off VM) and then syncing the incremental replication state from your local Meltano to Meltano Cloud using the CLI. Meltano Cloud will then pick up where the local run left off.