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Neither platform is universally better for GPU training. Nodus is worth pricing first for longer runs when its listed whole-machine rates and prepaid-credit model fit your workload. Modal is often the more practical choice for short jobs, training tied to web endpoints, or projects that need its documented RDMA-connected cluster path. The deciding factor is your complete run cost and required features—not the advertised GPU rate alone.
This comparison covers pricing, hardware, distributed training, storage, failure recovery, and SDK trade-offs. Nodus’s October 2026 cost figures and pilot results are vendor-reported, while both companies’ capacity, rates, and beta access can change. Run a representative workload on both services before committing.
What is the practical answer: Nodus or Modal?
Choose Nodus when your training jobs run for hours, use a predictable machine shape, and you can manage a more infrastructure-oriented workflow. Choose Modal when you value per-second billing, tightly integrated preprocessing and deployment workflows, or a documented path to RDMA-connected multi-node clusters. For either service, calculate CPU, RAM, storage, region, startup, teardown, retry, and idle time alongside GPU charges.
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A short test can reverse the apparent winner. A GPU that is cheaper per hour may be attached to more expensive CPU or memory resources, or may spend longer waiting for capacity. Conversely, a higher GPU rate can be offset by per-second billing, faster setup, or fewer engineering changes.
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How do Nodus and Modal charge for training?
The billing models are different enough that a direct rate comparison is easy to get wrong. Nodus lists whole-machine prices; Modal lists GPU-task rates per second and bills other resources separately.
Nodus pricing model
Nodus’s official pricebook is version 2026.10.8, effective October 1, 2026. It says provider-supplied machine usage is charged at the provider’s cost divided by 0.875, with billing beginning when a machine is created and ending after deletion is confirmed. The listed figures are Nodus-published prices for particular machines, not a guaranteed quote for every region or availability window.
| GPU | Nodus listed price | Qualification |
|---|---|---|
| H100 SXM | $3.29 per GPU-hour | Whole-machine list price in the 2026 pricebook |
| A100 80 GB | $1.99 per GPU-hour | Whole-machine list price in the 2026 pricebook |
| L4 | $0.89 per GPU-hour | Whole-machine list price in the 2026 pricebook |
| B200 | $6.49 per GPU-hour | Whole-machine list price in the 2026 pricebook |
Nodus uses prepaid credits. Your estimate therefore needs the machine’s entire lifetime, including boot, package installation, checkpointing, export, and the time between a failed job and confirmed deletion. If a process leaves a machine running, the meter can continue even when the training process is idle.
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Modal pricing model
Modal’s pricing page lists GPU-task rates per second. Examples shown in the October 2026 materials are H100 SXM5 at $0.001097 per second, A100 80 GB at $0.000694 per second, L4 at $0.000222 per second, and B200 at $0.001736 per second. Straight multiplication by 3,600 gives approximate GPU-hour equivalents of $3.9492, $2.4984, $0.7992, and $6.2496 respectively; these are conversions of the published per-second rates, not a complete job quote.
Modal also describes CPU and memory as separately billed resources. Plan terms, region, and non-preemptible use can affect the final amount. The Starter plan is listed at $0 per month with $30 of included compute, while Team is listed at $250 per month with $100 of included compute. Treat those allowances as plan terms rather than free unlimited GPU capacity.
What must be included in a fair estimate?
- GPU count and the exact model, including whether the price is for a whole machine or only a GPU task.
- Wall-clock runtime, plus boot, data staging, checkpoint, export, and teardown time.
- CPU and RAM allocations, which Modal bills separately and which can be bundled into a Nodus machine price.
- Persistent storage, object-storage transfers, egress, and the number and size of checkpoints.
- Region, capacity delays, plan fees, and any non-preemptible or premium placement.
- Retries and failed attempts. A failed setup can still consume machine time.
Which platform is cheaper for common GPU training runs?
Nodus published illustrative calculations checked October 12, 2026. They are useful as examples of the estimation method, not as independent benchmarks or guaranteed totals.
| Illustrative workload | Modal | Nodus | How to interpret it |
|---|---|---|---|
| Six-hour, single A100 80 GB LoRA fine-tune | $14.99 | $6.54 | Nodus’s vendor-authored calculation; Modal CPU and memory charges are noted separately |
| 100 L4 GPU-hours | $79.92 | $43.00 | Published comparison calculation, not an independent invoice test |
| 288 H100 GPU-hours | $1,137.37 | $748.80 | Published comparison calculation; actual totals depend on attached resources and timing |
Those examples suggest a Nodus advantage on the listed GPU component for the stated assumptions. They do not establish that every job costs less on Nodus. For example, a short Modal task may pay only for active seconds, while a Nodus machine can incur creation-to-deletion time. A Modal run that needs substantial CPU, RAM, or storage can also cost more than its GPU-only conversion implies.
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Modal’s GPU guide lists T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, B200, and B300 families. Several families support up to eight GPUs on one machine. Asking for more GPUs can increase the wait for capacity, so the nominal maximum is not the same as immediate availability.
Nodus’s GPU guide emphasizes inspecting live machine options. You check available machines, GPU count, region class, hourly price, and how recently availability was checked. Because supply changes, record the exact machine and timestamp when you make a cost or capacity decision.
Match memory before price
Start with the model’s peak memory requirement, optimizer state, activation strategy, sequence length, and per-device batch size. A cheaper GPU that cannot hold the model forces quantization, offloading, gradient accumulation, or a different parallelism strategy. Those changes can increase runtime enough to erase an hourly-rate saving.
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Confirm the physical topology
For data or tensor parallelism, ask whether the requested GPUs share one host, what interconnect is available, and whether the framework’s collective-communication settings are supported. Eight GPUs on one machine and eight GPUs across several machines are not interchangeable from a performance or configuration standpoint.
How mature is distributed and multi-node training?
Modal documents multi-GPU training on a single node and labels multi-node training private Beta, with access requested through its support channel. Its training materials also describe RDMA-connected clusters. Nodus labels multi-node training Beta in its GPU guide. Neither label should be treated as a production guarantee without confirming access, limits, and pricing for your account.
Questions to ask before selecting a cluster
- How many nodes and GPUs can your account request, and is approval required?
- Is the network RDMA-connected for the exact machine family and region?
- Which PyTorch, NCCL, or other communication versions are supported?
- Are node allocation and networking charged while jobs are starting or waiting?
- What happens if one node fails, and can the job resume from a checkpoint?
- Is the quoted rate for each GPU, each node, or a complete machine?
If your workload is single-node fine-tuning, the beta status may not matter. If it depends on multi-node scaling, make a successful small distributed run an acceptance test rather than relying on product-page wording.
How do checkpointing, storage, and recovery differ?
Modal’s training product recommends storing checkpoints in Volumes and describes workflows that reuse persistent storage. Nodus’s comparison describes resumable state under /nodus/state and says Volumes can import data from Hugging Face, Git, URLs, and S3. These features reduce setup work, but they do not by themselves establish equal throughput, durability, egress cost, or recovery time.
A practical recovery design
- Write checkpoints at a measured interval based on the cost of repeating that interval, not merely at epoch boundaries.
- Store optimizer state as well as model weights when resuming training requires it.
- Keep a small validation artifact outside the training volume so you can verify a restored environment.
- Record the image, Python version, dependency lockfile, commit, seed, and training arguments with every checkpoint.
- Test restoration after stopping a job intentionally. A checkpoint that has never been restored is not a recovery plan.
For Nodus, include the time required to export or delete a machine in your budget. For Modal, include Volume operations and any separately billed storage or transfer. If your job is interruption-sensitive, compare the cost of a lost run with the cost of more frequent checkpoints.
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Which service fits preprocessing, fine-tuning, and deployment workflows?
Modal presents an integrated path for preprocessing, supervised fine-tuning, reinforcement learning, and cluster workflows. It is especially attractive when the same project must launch GPU jobs and serve web endpoints. Nodus’s own comparison describes a Modal-compatible API surface but also identifies unsupported SDK features and differences, so compatibility must be checked against your codebase rather than assumed.
Choose Modal’s workflow model when
- Your training pipeline already uses Modal abstractions or needs to move directly from training to an endpoint.
- You run many short experiments and benefit from per-second task billing.
- You need the product’s documented preprocessing, reinforcement-learning, or high-concurrency sweep patterns.
- You expect to use its RDMA-connected cluster offering after obtaining private-Beta access.
Choose Nodus’s workflow model when
- You prefer explicit machine selection, machine-level pricing, and prepaid credits.
- Your jobs are long enough that the listed whole-machine rate dominates startup overhead.
- You can adapt your launcher and storage layout to Nodus’s supported API and state paths.
- You want to inspect available machines, GPU counts, region class, and recent availability directly before launching.
Porting effort is part of cost. Count the engineering hours needed to change decorators, environment setup, volume mounts, secrets, logging, endpoint definitions, and retry behavior. A modest GPU saving may not justify a rewrite for a one-off experiment.
What did the published Nodus-versus-Modal pilots actually show?
Nodus reports two pilots run on October 11, 2026. Both used Qwen2.5-0.5B on one NVIDIA L4, one seed, and one accepted run per platform. Nodus says the resulting weights were byte-identical.
For supervised fine-tuning, Nodus reports 67% lower recorded charges. It says that including a failed Nodus attempt reduces the saving to 24.7%. For continued pretraining, it reports 42.5% lower recorded charges. Nodus also reports export times of 11.0 minutes on Nodus and 7.7 minutes on Modal.
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How should you run a representative pilot?
A controlled pilot is the fastest way to turn uncertain pricing and capacity into a decision. Keep the software, data, seed, GPU count, and stopping criteria identical.
- Select one production-like training slice. Include the same preprocessing and checkpoint schedule you expect in production.
- Choose a GPU shape that both services can provide and verify memory headroom before starting.
- Run at least one warm-up and one accepted run on each platform. Record queue time separately from active runtime.
- Capture GPU, CPU, RAM, storage, transfer, and plan charges. Do not compare GPU line items alone.
- Measure tokens or samples per dollar, time to first checkpoint, final export time, and recovery time after an intentional interruption.
- Repeat with the actual number of GPUs if your production job is distributed. A single-GPU result cannot validate network scaling.
- Document API changes, operational steps, and any support approval required for beta features.
Use the same accounting boundary on both sides: from the request being submitted through confirmed resource release and usable checkpoint export. That prevents a fast GPU-only estimate from hiding setup or teardown costs.
When is Modal the better choice?
Modal is the stronger candidate when the workload is short or bursty, when per-second billing reduces idle exposure, or when training is closely connected to web endpoints and other application services. Its product materials also emphasize preprocessing, supervised fine-tuning, reinforcement learning, persistent volumes, and observability. Confirm that the specific GPU family and region you need are available before relying on those advantages.
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Nodus is the stronger candidate when longer runs make its listed machine rates important, when you can use a fixed machine shape, and when inspecting live availability helps you choose capacity. Its published comparison reports lower recorded charges in the two small L4 pilots and lower illustrative prices for the listed A100, L4, and H100 scenarios. Those figures remain vendor-authored and should be validated with your own workload.
What should your final decision checklist include?
- Model fit: required VRAM, precision, batch size, optimizer state, and expected checkpoint size.
- Topology: one host or multiple nodes, GPU count, interconnect, and framework support.
- Economics: active runtime plus CPU, RAM, storage, transfer, plan, startup, teardown, and retry costs.
- Capacity: region, current supply, queue time, and any private-Beta approval.
- Reliability: checkpoint interval, interruption behavior, restore test, and deletion confirmation.
- Engineering: SDK compatibility, Python and library versions, logging, secrets, and endpoint deployment needs.
- Evidence: at least one representative run on each service, with assumptions and timestamps recorded.
If the checklist is inconclusive, choose the platform that lets you run the next experiment with less irreversible work. Hosted GPU pricing and capacity are volatile; reassess when your model size, region, or parallelism changes.
Frequently Asked Questions
Can I move a PyTorch training script from one platform to the other unchanged?
Usually not completely. The model and optimizer code may transfer, but launch definitions, environment setup, volume mounts, secrets, logging, retry behavior, and endpoint integration use platform-specific APIs. Budget a small porting exercise before comparing production economics.
Does a lower GPU-hour rate guarantee a lower training bill?
No. Runtime, CPU and RAM allocations, storage, transfer, machine startup, idle time, and failed attempts can dominate the GPU line item. Compare cost per completed training result using the same accounting boundary on both services.
How can I test whether a beta multi-node feature is suitable for production?
Request access before designing around it, then run the exact node count and communication pattern your job needs. Measure startup delay, collective-communication stability, checkpoint recovery, and the price of a failed node allocation.
Are Modal’s included monthly credits available to every account?
The pricing page lists a Starter plan with $30 of included compute and a Team plan with $100 of included compute alongside their monthly plan prices. Eligibility, renewal, and what counts toward the allowance are plan terms, so confirm them in the current account documentation.
Quick Recap
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