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For most teams, the best path is to validate a workload on on-demand TPU capacity, move restartable jobs to Spot, reserve a one-to-90-day calendar window for a scheduled event, and sign a one- or three-year commitment only after utilization is predictable. On-demand starts immediately without preemption but may have no available capacity. Spot can be up to 70% cheaper, yet Google may stop or delete it with 30 seconds’ notice. Flex-start offers a defined run of up to seven days from a dedicated pool. Reservations trade flexibility for capacity certainty and lower chip-hour rates.
Your actual cost depends on TPU generation, slice topology, region, quota, data movement, checkpoint storage, and how much of a commitment you use. The prices below are Google Cloud’s 2026 examples for Trillium (v6e), quoted per chip-hour in US dollars; verify the live pricing table before ordering.
Compare the ways to rent a Google TPU
| Model | Price position | Interruption risk | Capacity certainty | Best fit | Commitment |
|---|---|---|---|---|---|
| On-demand | Baseline pay-as-you-go | No preemption, but a request can fail for lack of physical capacity | Immediate start when capacity is available | Urgent jobs, sizing tests, uncertain end dates | None |
| Spot | Highly discounted, up to 70% below standard pricing | Can be stopped or deleted with 30 seconds’ notice | Depends on excess capacity | Checkpointed training, fine-tuning, batch inference, simulations | None |
| Flex-start (Preview) | Separate preview rate; check the live regional table | Defined run, subject to the service’s preview availability terms | Requested from a dedicated pool | Experiments, fine-tuning, dynamic inference for up to seven days | Up to seven days per request |
| Calendar-mode future reservation | Up to 30% below on-demand | Reserved window rather than preemption-based use | Scheduled start and duration, subject to reservation acceptance | Known training windows, launches, seasonal peaks | One to 90 days; no CUD |
| One- to three-year future reservation with CUD | 30%–55% below on-demand | High capacity planning certainty | Designed for planned long-term use | Steady training or inference with predictable utilization | One or three years; manual Cloud Sales or account-manager process |
There is no single cheapest option. Spot has the lowest potential unit cost but can make a job more expensive if interruptions force repeated work. A reservation can have a higher nominal rate than Spot while producing a lower, more predictable total cost for a fixed launch window. On-demand is often the cheapest way to learn what your workload actually needs because it carries no unused-term obligation.
How TPU pricing is measured
Google Cloud publishes TPU prices per chip-hour, not as a universal price for every TPU VM or slice. A slice using several chips multiplies the chip-hour rate by the number of chips and the hours running. Generation, deployment model, and region all change the rate. Storage, network egress, attached services, orchestration, and idle time are additional costs.
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Use this basic estimate before adding those extras:
TPU cost = chip count × runtime hours × price per chip-hour
For example, eight Trillium chips running for 100 hours at $2.70 per chip-hour cost $2,160 before storage and networking. At $1.89 the same reservation window costs $1,512, and at $1.22 it costs $976. Those calculations describe the quoted chip rate only; they do not prove that a long-term commitment is cheaper once unused committed capacity, migration work, or data-transfer charges are included.
On-demand: the flexible starting point
On-demand is Google Cloud’s standard pay-as-you-go model. It has no preemption and no term commitment, so you can stop when the experiment ends or scale the next run to a different TPU generation. It is the right baseline for a new model, an urgent production incident, or a project whose end date and steady-state utilization are unknown.
On-demand is not a capacity guarantee. Google can reject a request when the selected region or zone does not have enough physical TPU resources. A successful quota grant also does not reserve those resources. Keep a second region, a smaller slice, or a different supported generation as a fallback if a deadline matters.
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When on-demand is economical
- A short validation run where engineering time is more valuable than the maximum discount.
- A workload still being sized, where changing chip count or topology is likely.
- A service that must not be interrupted and cannot restore quickly from a checkpoint.
- Inference with unpredictable traffic, when a long commitment would leave capacity idle.
Spot: the lowest price when restart is cheap
Spot TPUs use excess Google Cloud capacity. Google describes them as highly discounted, with discounts of up to 70% in its TPU overview. They can be preempted at any time and may be stopped or deleted with 30 seconds’ notice. Treat that notice as a scheduling event, not as enough time to finish a long training step.
Make Spot usable
- Write checkpoints to durable storage frequently enough that losing the latest interval is acceptable.
- Persist optimizer state, scheduler state, random seeds, data-shard position, and software configuration, not just model weights.
- Make startup idempotent: a replacement worker should discover the newest valid checkpoint and resume without manual edits.
- Record preemption events and retry counts so you can compare the discounted rate with the extra runtime.
- Keep a small on-demand capacity path for evaluation, final export, or a deadline-sensitive last run.
Spot is a poor choice for a one-shot run without durable checkpoints, a job that cannot be reproduced after partial loss, or a serving endpoint whose interruption would violate its availability target. For batch inference and simulation, partition work into independently retryable units so one preemption does not invalidate the whole batch.
Flex-start: a short, dedicated-pool window
Flex-start is documented as a preview option for TPU7x, v6e, and v5p. You request a defined runtime of up to seven days from a dedicated capacity pool. It sits between on-demand flexibility and a longer reservation: the request is bounded enough to improve planning, but it does not lock you into a year.
Use Flex-start when an experiment or fine-tuning run needs a continuous short window, or when dynamic inference provisioning benefits from a pool distinct from ordinary on-demand capacity. Because it is a preview, confirm current eligibility, regional availability, and operational terms in the Google Cloud console before designing a production dependency. Keep an on-demand fallback if the run has a hard external deadline.
Calendar-mode reservations for scheduled events
A calendar-mode future reservation specifies a start time and duration from one to 90 days. Google positions it for scheduled training windows, product launches, and seasonal spikes. The price can be up to 30% below on-demand, and this short reservation type does not support a committed use discount (CUD).
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Calendar mode is valuable when the cost of missing a start date exceeds the value of maximum flexibility. Request it early enough for Google to confirm the required region, generation, chip count, and topology. A reservation for the wrong slice shape may be unusable even if its chips are technically available.
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- TPU generation and supported slice topology.
- Exact chip count and the start and end timestamps, including time zone.
- Region and zone requirements driven by data residency or nearby storage.
- Expected warm-up, checkpoint, and teardown time so the window covers the complete job.
- A fallback plan if the model finishes early or needs more time than reserved.
One- to three-year reservations with a CUD
Future reservations lasting one to three years are intended for long-running training and inference with predictable use. Google lists them at 30%–55% below on-demand, supports all TPU versions, and requires a committed use discount. The request is handled manually through Cloud Sales or an account manager rather than as a simple self-service purchase.
The discount is only valuable when you consume the commitment. Estimate utilization by month, including vacations, release freezes, model migrations, maintenance, and periods when a newer TPU generation may become preferable. Compare the committed bill with an on-demand scenario that includes realistic idle time; a lower unit rate does not recover money paid for unused capacity.
Questions to answer before signing
- Will the same TPU generation and topology remain compatible with your framework for the full term?
- Can the workload run continuously enough to cover the committed quantity?
- What happens if a model is retired, a region changes, or your organization’s demand falls?
- Does the discount still exceed migration, storage, and data-movement costs?
- Who owns the manual request and renewal process inside your organization?
Regions, generations, and the 2026 Trillium examples
Google states that Cloud TPU pricing varies by product, deployment model, and region. The following figures are the published 2026 examples for Trillium (v6e), in USD per chip-hour. South Carolina and Ohio share the same values in this row; Amsterdam has a higher on-demand and long-term committed rate. Flex-start and calendar values are shown where the table lists them.
| Region | On-demand | Flex-start | Calendar mode | One-year commitment | Three-year commitment |
|---|---|---|---|---|---|
| us-east1 (South Carolina) | $2.70 | $1.35 | $1.89 | $1.89 | $1.22 |
| us-east5 (Ohio) | $2.70 | $1.35 | $1.89 | $1.89 | $1.22 |
| europe-west4 (Amsterdam) | $2.97 | $1.35 | $1.89 | $2.08 | $1.34 |
These are sticker rates, not a promise that every slice is obtainable. The cheapest chip-hour can produce the most expensive job if data must cross regions, the workload waits in a queue, or repeated retries extend runtime. Data-location rules may also require a region that is not the least expensive one.
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Check fit before comparing rates
TPU generations differ in memory, interconnect behavior, supported slice sizes, and framework support. A v6e price cannot be compared fairly with a v5p price until you know that both can hold the model, batch size, optimizer state, and communication pattern. Measure a representative step, not only a small benchmark: include input pipeline time, checkpoint writes, collective communication, compilation, and evaluation.
Quota is separate from capacity
TPU quotas are version-specific and distinguish on-demand from preemptible use. Some TPU versions require a quota request before you can create resources. Quota controls how much of a class you are allowed to request; it does not reserve chips in a region or zone.
Quota and capacity checklist
- Identify the exact TPU version, mode, region, zone, and chip count.
- Request the matching quota class, including preemptible quota if you plan to use Spot.
- Wait for the quota grant before scheduling a deadline-bound run.
- Test creation in the target zone; a quota grant can coexist with a physical-capacity shortage.
- Prepare an alternate zone, region, generation, or smaller slice that your framework supports.
Do not treat an approved quota email as proof that a reservation or on-demand instance will start. Capacity, quota, and reservation acceptance are separate gates.
A practical decision sequence
- Define the hardware requirement. Record generation, memory needs, topology, chip count, framework version, and maximum acceptable restart time.
- Select a region. Balance data residency, latency to storage, network cost, and the regions that publish the required TPU generation.
- Obtain the right quota. Request on-demand or preemptible quota for the specific version and region; do this before a deadline.
- Run a small on-demand validation. Measure throughput, compilation time, checkpoint size, and total cost per useful training step.
- Move repeatable batch work to Spot. Verify that automatic restore, logging, and retry behavior work under forced interruption.
- Use Flex-start for a bounded run. Choose it when a window of no more than seven days from its dedicated pool matches the experiment.
- Reserve a calendar window. For a known event lasting one to 90 days, reserve the exact start, duration, region, and topology.
- Evaluate a CUD-backed term. Use a one- or three-year reservation only after measured utilization shows that the discount outweighs unused commitment.
How to compare total job cost, not just chip price
Build a worksheet with one row per purchasing model and include:
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- Chip count multiplied by expected runtime and the quoted chip-hour rate.
- Expected preemption restarts and the percentage of work lost at each interruption.
- Queue or startup time during which other resources may still incur cost.
- Checkpoint storage, artifact retention, and data-transfer charges.
- Engineering time for checkpointing, failover, reservation requests, and migration.
- Unused hours under a calendar or long-term commitment.
- The cost of running in a farther region to obtain capacity or a lower rate.
For a checkpointed training job, calculate an effective rate as total spend divided by completed training steps, not requested TPU hours. For inference, use cost per successful request at the required latency and availability. This prevents a nominally cheap Spot run with frequent restarts from beating a more stable reservation.
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Common failure modes and fixes
The request fails even though quota is approved
Quota and physical capacity are different. Try another supported zone or region, reduce the slice, or switch temporarily to on-demand capacity in a region with supply. If the run is scheduled, use a calendar reservation rather than waiting until the start minute.
A Spot job loses too much progress
Shorten the checkpoint interval, write optimizer and data-loader state, validate restoration on a fresh worker, and split the workload into retryable shards. Keep final evaluation and export on non-preemptible capacity.
The reserved TPU is the wrong shape
Confirm topology and chip count with the framework before requesting. Memory or interconnect limitations can make an apparently equivalent generation unusable. Test the exact slice shape during validation.
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Recalculate monthly utilization after model, traffic, or region changes. For future purchases, commit only the stable base load and leave burst demand to on-demand, Spot, Flex-start, or calendar reservations.
The cheapest region raises the real bill
Price data transfer and storage access, check residency obligations, and measure latency to the training corpus. A slightly higher chip-hour rate nearby may reduce idle time and network spend enough to lower total cost.
Which option fits common workloads?
| Workload | Preferred starting point | Reason | Guardrail |
|---|---|---|---|
| New model or framework validation | On-demand | Flexible hardware and no term risk | Keep the run small until throughput and memory fit are known |
| Large pre-training with robust checkpoints | Spot, possibly mixed with on-demand | Maximum unit-price reduction | Automate restore and monitor lost work |
| Fine-tuning experiment lasting several days | Flex-start or Spot | Flex-start offers a bounded window; Spot lowers cost if interruptions are acceptable | Confirm preview availability or checkpoint recovery |
| Quarterly launch training window | Calendar-mode reservation | Known one-to-90-day schedule and better start certainty | Reserve enough warm-up and teardown time |
| Continuous, predictable inference | One- or three-year CUD-backed reservation | Long-term discount and planned capacity | Commit only the measured base load |
| Latency-sensitive service with uncertain traffic | On-demand baseline with burst capacity | Avoid paying for idle committed chips | Set quotas and regional fallbacks in advance |
Bottom line
Start with on-demand to establish a measured baseline and confirm hardware fit. Adopt Spot when checkpoints make interruption routine rather than catastrophic. Choose Flex-start for a preview-supported run of up to seven days, calendar mode for a scheduled one-to-90-day event, and a CUD-backed one- or three-year reservation only when utilization, region, topology, and demand are stable enough to justify the commitment.
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