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Vercel does not price an “always-on worker” as an unlimited 24/7 daemon. With Fluid Compute, estimate a workload’s monthly bill as (active CPU hours × regional CPU rate) + (provisioned memory GB-hours × regional memory rate) + invocation charges. CPU time is charged only while your code executes; waiting for a database, API, or model does not count as active CPU. Memory remains billable while the instance has in-flight work. When all requests finish, the instance pauses and CPU and memory charges stop until another invocation arrives.
Using Vercel’s published Cleveland rates of $0.128 per active CPU-hour and $0.0106 per provisioned GB-hour, a 2 GB Standard instance running at 100% active CPU would cost about $0.1492 per hour, or approximately $108.92 over 730 hours, before invocation charges. That is an arithmetic estimate, not a quote; your execution region, memory size, CPU utilization, invocation volume, plan, and billing model determine the actual amount.
What “always-on” means on Vercel
An always-on workload is a behavior you are trying to achieve, not a Vercel product that guarantees a permanently running process. Fluid Compute instances can be reused for later requests and can process concurrent work, but they pause when there are no in-flight requests. A paused instance does not continue accumulating CPU or memory charges.
This distinction matters for queue consumers, scheduled jobs, streaming handlers, and daemon-like code. A request can keep an instance active only while that request is in progress, and every invocation is subject to the function’s maximum duration. Vercel documents a 300-second default and maximum on Hobby. On Pro and Enterprise, the default is 300 seconds and the maximum is 800 seconds with Fluid Compute. Check the limit configured for the specific function before treating it as a long-running worker.
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For a genuinely continuous process, model the work as a sequence of invocations or compare a service intended for persistent processes. Do not assume that one Vercel Function request can run indefinitely, and do not budget for idle 24/7 time when no request is in flight.
The Fluid Compute pricing formula
For a current project using Fluid Compute, use this monthly estimate:
monthly cost = (active CPU hours × regional CPU rate) + (provisioned memory GB-hours × regional memory rate) + invocation charges
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Active CPU hours are the time the function is actually executing code. Vercel’s documentation states that you are billed during actual code execution, not during I/O operations. Database queries, HTTP requests, object-storage waits, and model calls can therefore make elapsed request time much longer than billable active CPU time.
Estimate active CPU hours from observed execution rather than from the wall-clock lifetime of the request. If a request takes 10 seconds but your code is executing for only 2 seconds, the active-CPU fraction is approximately 20 percent, subject to the measurement method and the amount of concurrent work.
Provisioned memory GB-hours
Provisioned memory is billed for the entire instance lifetime while it has in-flight work. Unlike active CPU, this meter continues while the function waits on I/O. Multiply the memory allocated to each instance by the hours that instances are handling requests, then apply the memory rate for the execution region.
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Invocation charges
Invocation charges are a separate term. Count every request, including failed requests, against the plan’s invocation metric. No universal invocation price is stated here, so use the current rate and allowance for your Vercel plan instead of inserting a generic number into the estimate.
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The following example uses the published Cleveland rates of $0.128 per active CPU-hour and $0.0106 per provisioned GB-hour. It assumes one 2 GB Standard instance, 100 percent active CPU, and 730 hours of operation. It excludes invocation charges and any plan allowance.
| Component | Calculation | Hourly amount | 730-hour amount |
|---|---|---|---|
| Active CPU | 1 CPU-hour × $0.128 | $0.1280 | $93.44 |
| Provisioned memory | 2 GB × $0.0106 | $0.0212 | $15.476 |
| Fluid Compute subtotal | $0.1280 + $0.0212 | $0.1492 | Approximately $108.92 |
| Invocation charges | Plan-specific; not stated here | Not stated | Not included |
The arithmetic is 1 × $0.128 = $0.128 for CPU, 2 × $0.0106 = $0.0212 for memory, and $0.128 + $0.0212 = $0.1492 per hour. Multiplying by 730 hours gives approximately $108.92. Cleveland is only the region used for this illustration; use the rates for the region where your function executes.
Collect the inputs before you calculate
1. Confirm the billing model
New Vercel projects use Fluid Compute by default, but legacy projects may still use the older Function Duration model. Identify which model applies before doing any arithmetic. Fluid Compute uses separate active-CPU and provisioned-memory meters. The legacy model charges wall-clock Function Duration in GB-hours. Mixing a legacy GB-hour rate with Fluid Compute CPU and memory rates produces a misleading result.
2. Record the memory allocation
Vercel documents a 2 GB/1 vCPU Standard configuration and a 4 GB/2 vCPU Performance configuration for Pro and Enterprise Fluid Compute. Record the actual configuration assigned to the function. A larger allocation raises the memory component even when the code is mostly waiting on I/O.
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Separate elapsed request time into code execution and waiting. The fraction spent executing code is the value that drives active CPU hours. A worker that spends most of its time waiting for a remote service can have a low CPU fraction but still incur memory charges throughout the wait.
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4. Measure the in-flight instance lifetime
For memory billing, determine how long an instance has at least one request in progress. Include I/O waits in this period. Concurrent requests can keep an instance active even when each individual request would otherwise finish quickly.
5. Count invocations
Count successful, failed, retried, scheduled, and health-check requests that reach the function. Failed requests still count against the plan’s invocation metric. If a queue or scheduler retries work, include those attempts rather than counting only completed jobs.
6. Apply the execution region
CPU and memory rates vary by region. Use the function’s actual execution region, not the region nearest your office or your database unless those are the same. If traffic is distributed across regions, calculate each region separately and add the subtotals.
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7. Verify the duration limit
Check maxDuration in the function’s code or project configuration, the vercel.json file when applicable, and the Vercel dashboard settings. A cost estimate is not useful if the invocation will be terminated before it can finish its work.
How to build an estimate step by step
- Identify the project’s compute model. Confirm that the project uses Fluid Compute rather than legacy Function Duration.
- Write down the function region. Select the corresponding active-CPU and memory rates from Vercel’s current regional pricing information.
- Record the instance configuration. Note memory in GB and the associated CPU capacity, such as Standard 2 GB/1 vCPU or Performance 4 GB/2 vCPU where those configurations are available.
- Measure request behavior. For a representative period, record invocation count, elapsed request time, active execution time, concurrency, and failed or retried requests.
- Calculate active CPU hours. Convert measured execution time to hours and multiply by the number of requests or concurrent workers represented by the measurement period.
- Calculate provisioned memory GB-hours. Multiply allocated GB by the time instances have in-flight work. Include periods spent waiting on databases, APIs, or model calls.
- Add invocation charges. Apply the plan’s current invocation allowance and rate to the complete request count, including failures.
- Check the duration ceiling. Compare the longest work item with the configured maximum duration: 300 seconds on Hobby, and 300 seconds by default or up to 800 seconds maximum on Pro and Enterprise with Fluid Compute.
- Run a sensitivity check. Recalculate with higher request volume, a lower or higher active-CPU fraction, and a different region. This shows which assumption drives the bill instead of presenting one falsely precise number.
Why waiting on I/O changes the answer
Consider two requests that each last 10 seconds. Request A executes code for 9 seconds and waits for a database for 1 second. Request B executes code for 1 second and waits for the database for 9 seconds. Under Fluid Compute, Request A contributes roughly nine times as much active CPU as Request B, while both can keep provisioned memory billable for the full 10-second in-flight period.
This is why multiplying total request duration by a CPU rate overstates CPU cost for I/O-heavy workers, while ignoring request duration understates memory cost. Measure the two meters independently.
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Fluid Compute versus legacy Function Duration
| Characteristic | Fluid Compute | Legacy Function Duration |
|---|---|---|
| Primary compute meter | Active CPU time | Wall-clock Function Duration |
| Memory meter | Provisioned memory GB-hours while the instance has in-flight work | Included in the legacy GB-hour duration model |
| Idle period after requests finish | Instance pauses; no CPU or memory charges until the next invocation | Use the legacy project’s documented duration behavior |
| Default for new projects | Yes | No |
| Estimation warning | Do not substitute wall-clock duration for active CPU | Do not apply Fluid Compute CPU/memory rates |
Legacy projects can therefore produce a materially different estimate for the same application. Confirm the project’s billing model in the dashboard and documentation before comparing historical bills with a new forecast.
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Queue consumers
A queue consumer usually waits for work, claims an item, performs processing, acknowledges the item, and repeats. On Vercel, represent that pattern as invocations that process bounded batches or individual jobs. Include polling or delivery invocations, failed attempts, retries, active execution, and memory held while external systems respond. Verify that each batch fits within the function’s maximum duration.
Scheduled jobs
A scheduled job is naturally modeled as a series of invocations rather than as one process that remains resident between schedules. Estimate the number of runs per month, the average active CPU per run, the memory-held time per run, and any failed or repeated runs. There is no Fluid Compute charge merely because no invocation is running between schedules.
Streaming handlers
A streaming request can keep memory provisioned for the entire period that the stream remains in flight. CPU billing follows actual execution, so a stream that frequently waits for upstream data may have modest active CPU but substantial memory GB-hours. Check the maximum duration before designing a stream that must remain open for longer than the documented limit.
Daemon-like services
A daemon that must maintain a continuous connection, consume work without incoming requests, or run beyond the function duration ceiling is not a straightforward single-invocation use case. Break the operation into bounded invocations if the protocol allows it, or evaluate a platform designed for persistent processes. Compare maximum duration, concurrency, region availability, memory commitment, billing metric, invocation pricing, and true process persistence.
Configuration choices that affect cost
| Configuration | Documented allocation | Cost implication | Use it when |
|---|---|---|---|
| Standard | 2 GB memory, 1 vCPU | Lower memory commitment than Performance; CPU and memory still depend on utilization and in-flight lifetime | The workload fits within the documented Standard capacity and does not need two vCPUs |
| Performance | 4 GB memory, 2 vCPU | Higher provisioned-memory component and greater CPU capacity | The workload needs additional memory or parallel CPU capacity and the plan supports this configuration |
The allocation alone does not tell you the monthly bill. A 4 GB instance that is active for a short period can cost less than a 2 GB instance held in flight continuously, while a CPU-heavy workload can reverse that relationship. Use measured lifetime and active execution for each configuration.
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Regional pricing and invocation allowances
Regional rates are not interchangeable. The Cleveland values used in the worked example are $0.128 per active CPU-hour and $0.0106 per provisioned GB-hour. Your function may execute elsewhere, and both rates can differ. If a deployment uses more than one execution region, maintain a separate row for each region in your worksheet.
Invocation pricing depends on the plan and its allowance. Include all requests in the count, including failures and retries. Keep invocation charges separate from CPU and memory in your spreadsheet so a plan change or allowance can be updated without rebuilding the compute estimate.
Common estimation mistakes and fixes
- Charging CPU for idle waits: Do not multiply all elapsed request time by the CPU rate. Estimate active execution separately from I/O waits.
- Ignoring memory during waits: Provisioned memory remains billable while work is in flight, even when CPU is paused for an external response.
- Assuming idle instances cost money: Fluid Compute instances pause after requests complete. There is no CPU or memory charge during that idle gap until another invocation arrives.
- Treating a function as an unlimited daemon: Check the maximum duration. A single request is bounded at 300 seconds on Hobby and at 300 seconds by default or 800 seconds maximum on Pro and Enterprise with Fluid Compute.
- Using the wrong billing model: Legacy projects may use wall-clock GB-hour Function Duration. Do not apply Fluid Compute rates to them.
- Leaving out failed requests: Invocation metrics include failed requests. Add retries, health checks, and scheduler attempts.
- Using a familiar region’s rate: Apply the rate for the function’s execution region, not an assumed default.
- Reporting an estimate as a quote: Label the Cleveland calculation as an example and state the assumptions, exclusions, date of the rate sheet, and region used.
A practical worksheet for your own workload
Create one row per function and region with these columns:
- Billing model: Fluid Compute or legacy Function Duration.
- Execution region and the current CPU and memory rates.
- Memory allocation in GB and CPU capacity.
- Monthly invocation count, including failed and retried requests.
- Average and peak in-flight lifetime.
- Active-CPU fraction or measured active CPU hours.
- Maximum configured duration.
- Invocation allowance and any plan-specific invocation rate.
For Fluid Compute, calculate the CPU subtotal from active CPU hours, the memory subtotal from provisioned GB-hours, and then add invocation charges. Run a low, expected, and high case. The high case should reflect peak concurrency, longer external waits, additional retries, and the most expensive region your deployment may use.
When a Vercel estimate is appropriate
Fluid Compute is a reasonable fit when work arrives through invocations, can be divided into bounded units, benefits from request concurrency, and can finish within the configured duration. The pricing model can be attractive for bursty workloads because instances pause between requests and CPU waits on I/O are not billed as active execution.
Use a different architecture when the workload requires one process to remain alive with no incoming request, holds a connection beyond the maximum function duration, needs unbounded queue consumption, or depends on a persistent local process. Compare alternatives using the same axes: active CPU versus wall-clock billing, memory commitment, duration limits, concurrency, regional availability, invocation charges, and whether a genuinely persistent process is supported.
Quick Recap
Final checklist before publishing a forecast
- Confirm Fluid Compute or legacy Function Duration.
- Confirm the execution region and use its current rates.
- Record memory allocation and CPU capacity.
- Measure active execution separately from I/O waiting.
- Measure memory-held time while requests are in flight.
- Count every invocation, including failures and retries.
- Include the plan’s invocation allowance and rate.
- Verify the 300-second Hobby limit or the applicable Pro/Enterprise limit up to 800 seconds.
- Show assumptions and label the result as an estimate rather than a quote.
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