inference-cloud
Modal
Serverless GPU compute defined in Python, billed by the second
Starts at
Contact sales
Pricing tier: Freemium
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Independent software comparison
Modal vs Baseten
General-purpose serverless compute vs. a platform built around model serving
inference-cloud · medium search interest
inference-cloud
Baseten
Production model serving with dedicated deployments and hosted model APIs
Starts at
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Pricing tier: Freemium
Visit BasetenExpert analysis
Understanding the choice in practice
Modal runs general-purpose serverless GPU compute where inference is one of several Python workloads you define and deploy from code, while Baseten is a model-serving platform whose entire surface area is built around getting models into production endpoints. Both run on managed GPUs, both offer a free starting point, and both support fine-tuning, but the day-to-day workflow, the billing model, and the production tooling around the deployment are shaped by fundamentally different assumptions about what you are there to do.
Feature matrix
Specs at a glance
Rows are grouped by capability, and each cell shows the wording from that vendor’s own documentation. “Not documented” means we found no cited source for that capability, which is not the same as the product lacking it.
| Capability | Modal | Baseten |
|---|---|---|
| Starting price | Contact sales | Contact sales |
| Free plan | Yes | Yes |
| API available | Related platform API | Related platform API |
| Serverless GPU with scale to zero | Serverless execution billed per second | No charge for idle time |
| Dedicated or always-on instances | Not documented | Dedicated deployments on managed GPUs |
| Bring your own container or image | Images defined in Python, or an existing Docker image | Model packaging with Truss |
| Prebuilt model catalogue | Not documented | Curated set of hosted models |
| Per-token hosted model API | Not documented | Model APIs billed per token |
| Fine-tuning and training | Fine-tuning and training | Loops and Training Jobs |
| Autoscaling and concurrency control | Automatic container scale-up | Autoscaling on dedicated deployments |
| Range of GPU types offered | T4 through B300, selected in code | T4 through B200 |
| Logs, metrics, and tracing | Not documented | Logs, metrics, and request traces |
| Region and data-residency control | Not documented | Regional environments and multi-cloud capacity |
Detailed comparison
Where the differences matter
Workflow and what you are actually deploying
Modal asks you to write Python functions, decorate them with the GPU type and container image you want, and let the platform handle scale-up. The container image itself can be declared in Python beside the code that runs on it, so a Dockerfile is not required, though you can bring an existing Docker image. The unit of work is a function or a set of functions, and the documented examples span inference, batch processing, training, sandboxes, and notebooks. That breadth is the point: Modal treats inference as one shape of Python workload among several, and the same primitives that serve a model can run a fine-tuning job or a batch transform overnight. Baseten narrows the surface. The unit of work is a deployed model, packaged with Truss, served from its own endpoint, and accompanied by the operational tooling a production deployment implies. Where Modal gives you a function runner, Baseten gives you a model lifecycle. Deployments carry logs, metrics, and request traces that export to Datadog or Prometheus, autoscaling is documented as part of standard production operations, and regional environments exist for data-residency requirements. Baseten also offers a second interface entirely absent from Modal: Model APIs that expose a curated set of hosted models over OpenAI- and Anthropic-compatible endpoints, billed per token rather than by GPU time. If your workload is calling a known model, you can skip deployment altogether; if you need a custom model, you deploy it and get the serving infrastructure wrapped around it.
Implementation and the shape of the code
On Modal, the developer experience is centered on code that describes its own environment. You name the GPU in the function decorator, you declare or import the image in Python, and you deploy by running the code against the platform's interface. There is no prebuilt model catalogue or per-token API documented; the platform runs the code the customer brings. That means the team is responsible for model loading, request handling, and whatever serving logic they want, but it also means the same codebase can hold a training loop, a batch job, and an inference endpoint without changing tooling. Baseten's implementation is split between two paths. For hosted models, there is nothing to deploy: you call an OpenAI- or Anthropic-compatible endpoint and pay per million tokens. For custom models, Truss packages the model and its dependencies for deployment on dedicated infrastructure, and the platform supplies the serving, autoscaling, observability, and regional controls around it. The tradeoff is that Baseten's abstractions are optimized for the model-serving case. A team that wants to run a non-serving Python workload, or that wants inference to share a codebase with unrelated compute, will find Modal's model a more natural fit, while a team whose entire job is standing up and operating model endpoints will find Baseten's packaging and telemetry already in place.
Pricing and how cost accrues
Both platforms advertise a free starting point, but the billing models diverge in ways that matter for predictable budgeting. Modal is freemium with per-second compute billing. The Starter plan is $0 a month and includes $30 of compute credits and 3 seats. Team is $250 a month before any compute is used, with $100 of credits and unlimited seats, and Enterprise is custom. No plan charges per seat. Published per-second GPU rates run from T4 at $0.000164 per second up to B300 at $0.001972 per second, with CPU and memory billed separately and volumes at $0.09 per GiB per month with 1 TiB included. The per-second granularity and the published rates make it possible to estimate a workload's cost from the code itself. Baseten's pricing is more layered. Basic is free to start and pay-as-you-go with credits for new accounts. Pro adds priority GPU access with volume discounts, but no published price, and Enterprise is custom with self-hosting and data-residency control. Dedicated deployments bill per minute, with rates published from T4 at $0.01052 per minute up to B200 180GB at $0.16633 per minute, and CPU instances ranging from $0.00058 to $0.01382 per minute. Hosted Model APIs are billed per million tokens instead, with examples like DeepSeek-V4-Flash at $0.13 in and $0.26 out, and Kimi K3 at $3.00 in and $15.00 out. Two details deserve attention: Baseten does not bill for idle time, but billed compute covers the time a model spends deploying, not only the time it spends answering requests. Modal's per-second billing applies to the containers it runs, while Baseten's per-minute dedicated rates apply to the model's deployment lifecycle, and its per-token rates apply only to the curated hosted models. A buyer comparing the two on cost needs to know which of those meters their workload will actually hit.
Control, extensibility, and team fit
Modal's extensibility comes from being a general compute platform. The same Python codebase can hold inference, fine-tuning, training from scratch, batch jobs, and sandboxes, and the GPU is selected in code from a range spanning T4 through B300. That makes Modal attractive to teams whose work is not purely model serving and who want one platform for several shapes of GPU workload. The tradeoff is that production serving tooling, observability, and data-residency controls are not the platform's documented center of gravity; the team builds or brings what it needs beyond autoscaling. Baseten's control is narrower but deeper within its scope. Regional environments support data residency, GPU capacity is managed across clouds and regions, telemetry ships with every deployment and exports to standard observability platforms, and the platform offers both dedicated GPU time and per-token Model APIs so a workload can move between them. Fine-tuning is supported through Loops for supervised fine-tuning and reinforcement learning and Training Jobs for custom framework code on dedicated clusters. For a team whose workload is model endpoints in production, that depth is the product. For a team running mixed workloads, it is more than the job requires and less than the flexibility Modal offers.
Best use case for Modal
Teams running mixed GPU workloads from one Python codebase.
Best use case for Baseten
Teams whose workload is model endpoints in production.
Modal: pros and cons
What works
- The Starter plan is $0 a month and includes $30 of compute credits.Modal official pricing page
- No plan charges per seat, and Team and Enterprise have unlimited seats.Modal official pricing page
- The image is declared in Python beside the code that runs on it, so no Dockerfile is required.Modal official documentation
Tradeoffs
- Team costs $250 a month before any compute is used.Modal official pricing page
- No prebuilt model catalogue or per-token API is documented; the platform runs code the customer brings.Modal official documentation
Baseten: pros and cons
What works
- Logs, metrics, and request traces ship with every deployment and export to Datadog or Prometheus.Baseten official documentation
- GPU capacity is managed across clouds and regions, with regional environments for data residency.Baseten official documentation
- Dedicated GPU time and per-token Model APIs are both available, so a workload can move between them.Baseten official pricing page
Tradeoffs
- Pro pricing is not published; the page offers volume discounts rather than a rate.Baseten official pricing page
- Billed compute covers the time a model spends deploying, not only the time it spends answering requests.Baseten official pricing page
Decision framework
How to choose between Modal and Baseten
Choose Modal if inference sits alongside training, batch jobs, and other Python workloads and you want one platform defined in code to run all of them. Its per-second billing, published GPU rates, and Python-declared containers make it a good fit for teams that estimate cost from the function and that want the same tooling across serving and non-serving compute. Choose Baseten if serving models is the whole job and you want the production tooling that comes with it: Truss packaging, built-in logs, metrics, and request traces, regional environments for data residency, and the option to call curated hosted models per token without deploying anything. Teams that need OpenAI- or Anthropic-compatible endpoints for known models, or that require exportable observability and multi-cloud capacity management, will find Baseten's surface better matched. Teams that need a free tier with predictable per-second compute and no per-seat charge for small groups will find Modal's Starter plan more transparent.
Bottom line
Our verdict
Modal gives you a general-purpose serverless GPU platform where inference is one workload among several, defined in Python and billed per second; Baseten gives you a model-serving platform where deployment, observability, regional control, and per-token hosted APIs are built in. The practical separation is whether your team is running mixed GPU workloads from one codebase or operating model endpoints as a production service. For the mixed-workload case, Modal's breadth and transparent per-second pricing win. For the pure serving case, Baseten's packaging, telemetry, and curated model APIs are the better match. Neither is the right answer in isolation; the decision follows from what the team is actually deploying and how much production serving infrastructure it wants the platform to provide.
Sources and verification
Evidence and editorial reviewed
The product facts have been checked against the sources below. The AI-assisted analysis was audited against these exact evidence records and approved by a human editor.
Modal
VerifiedLast verified August 20, 2026
Baseten
VerifiedLast verified August 20, 2026
Editorial validation
Human-approvedApproved August 20, 2026 after an automated evidence audit using gemini-3.6-flash.
Read our comparison methodology and editorial policy, learn about TerraNet, or report a correction.
Common questions
Modal vs Baseten FAQ
Can I run training and fine-tuning on both platforms?
Yes. Modal documents fine-tuning an image model and training a small language model from scratch as examples, and Baseten offers Loops for supervised fine-tuning and reinforcement learning plus Training Jobs for custom framework code on dedicated clusters. The difference is that Modal treats training as one of several Python workloads in the same codebase, while Baseten frames training as part of a model lifecycle that leads to serving.
How do the billing models differ in practice?
Modal bills per second for the containers it runs, with published GPU rates from T4 through B300, and its Team plan costs $250 a month before compute. Baseten bills dedicated deployments per minute, including the time a model spends deploying, and offers per-token pricing for its curated hosted Model APIs. Baseten does not charge for idle time, but its Pro tier does not publish a rate, so high-volume dedicated usage requires a quote.
Does either platform offer hosted model endpoints I can call without deploying anything?
Baseten does. Its Model APIs expose a curated set of models over OpenAI- and Anthropic-compatible endpoints, billed per million tokens. Modal does not document a prebuilt model catalogue or per-token API; it runs code and containers the customer supplies.
Which platform is better for data residency requirements?
Baseten documents regional environments for data-residency needs and manages GPU capacity across clouds and regions. Modal does not foreground regional or data-residency controls in its documented feature set, so teams with those requirements should confirm current capabilities before committing.
AI-assisted draft audited against the cited product evidence and approved by a human editor. Vendor pricing and capabilities can change after the recorded verification date.
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Related comparisons and alternatives
The best Baseten alternatives
Baseten's dedicated deployments bill per minute for the time a model spends deploying as well as the time it spends answering requests, and its Pro tier with priority GPU access carries no published rate, which leaves teams with predictable, high-volume inference workloads needing to model their costs from published per-minute GPU rates alone. The platform pairs those dedicated deployments with a curated set of hosted Model APIs billed per token, packages everything through Truss, and ships logs, metrics, and request traces that export to Datadog or Prometheus. That combination is well suited to teams that want a managed production serving layer with regional environments for data residency. A buyer might look beyond Baseten when the curated model set does not include a specific open model they need, when per-minute billing granularity is too coarse for bursty or short-lived workloads, when they prefer to define infrastructure in Python rather than package through Truss, or when they want a published price for a higher service tier before committing. The criteria below frame the four decisions that most directly separate these alternatives: how compute is billed and what that billing covers, whether the platform provides hosted models or expects you to bring your own, how packaging and deployment work, and what operational controls exist for production traffic.
Read guideThe best Modal alternatives
Modal's platform runs code the customer supplies, with no prebuilt model catalogue or per-token API documented, which means a team that wants to call a hosted model from a single endpoint rather than package and deploy its own inference code has to look elsewhere. The per-second billing model and Python-defined container images make Modal a strong fit for engineers who want fine-grained control over what runs on the GPU, but that same design pushes buyers who need ready-to-run models, per-token pricing, or a curated model library toward alternatives. The Team plan at $250 per month before any compute is used adds a fixed floor that can matter for organizations whose usage is intermittent or still ramping. These factors do not diminish Modal's value for teams that want to bring their own training, fine-tuning, or batch workloads and pay only for the seconds of GPU time consumed. They simply define the boundary where a different product may serve a given workflow better.
Read guideReplicate vs Modal
Replicate gives you a catalogue of ready-to-call models accessible via API, while Modal gives you serverless compute to run your own Python code and containers on the GPU. A buyer must decide whether they want to consume a pre-packaged model or build and deploy custom logic. Replicate is the right tool for product teams adding a model feature without infrastructure overhead. Modal is the right tool for engineers who need custom code, specific dependencies, or training capabilities, and who want to leverage free monthly credits before committing to paid compute.
Read guideRunPod vs Baseten
RunPod gives you direct control of GPU workers, your own Docker containers, and the widest range of published silicon from L4 to B300. Baseten gives you managed model serving with logs, metrics, request traces, and regional environments for data residency built into the product. The separation is not subtle: RunPod is capacity you operate, Baseten is serving the vendor manages. Choose RunPod if you want to own the container, tune autoscaling settings, and match workloads to a broad GPU range under one account that spans serverless and dedicated pods. Choose Baseten if you want observability and regional control as part of the platform, or if per-token Model APIs for a curated set of hosted models fit your workload better than managing GPU time. Neither tool is the better choice in isolation; the decision turns on whether your team wants to operate inference infrastructure or consume it as a managed service.
Read guideRunPod vs Modal
RunPod and Modal separate on what you bring to the platform and what you get back. RunPod takes a Docker image and gives you serverless endpoints, dedicated pods, queue-based routing, SSH access, and active worker controls on one account. Modal takes Python code and gives you per-second serverless compute with the container generated for you, plus a free tier to start. Choose RunPod if your team already builds containers and needs the operational control of dedicated pods alongside bursty serverless capacity. Choose Modal if your team writes Python, wants to skip the Dockerfile, and values a free tier for experimentation. Neither platform offers a hosted model catalogue or per-token API, so both assume you are bringing the inference code yourself. The decision comes down to whether the container is an artifact you want to own or one you want the platform to generate.
Read guideRunPod vs Replicate
RunPod gives you a Docker image and a GPU bill; Replicate gives you a model ID and an API call. That is the difference a buyer feels first, and it determines everything downstream. RunPod is the better choice for a team with a custom model or pipeline to run, because the container is the unit of deployment, the worker count is a knob the team can turn, and the same account holds both serverless endpoints and dedicated pods. Replicate is the better choice for a developer who wants a published open model working today, because the library removes the packaging step and the per-second or per-token billing maps directly onto application usage. Neither platform publishes a free tier, so the decision rests on workflow fit and cost shape rather than on trial access. For bursty custom inference, RunPod's serverless-to-zero model wins. For sporadic calls to a known model, Replicate's library wins. For a private, always-on custom deployment, the buyer should model idle cost carefully on both sides before committing.
Read guide