inference-cloud
Replicate
Run published open models from a cloud API, or deploy your own
Starts at
Contact sales
Pricing tier: Usage-based
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Independent software comparison
Replicate vs Modal
A catalogue of ready-to-call models vs. compute for code you write
inference-cloud · medium search interest
inference-cloud
Modal
Serverless GPU compute defined in Python, billed by the second
Starts at
Contact sales
Pricing tier: Freemium
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Understanding the choice in practice
Replicate and Modal serve teams that need GPU compute for machine learning workloads, but they approach the problem from opposite directions. Replicate provides a large public library of models that can be called via an API without packaging or deploying anything, while Modal provides serverless GPU compute where you write and deploy your own Python code. The decision between them falls to product teams looking to add an AI feature without managing infrastructure versus engineers who need custom code, specific dependencies, or training capabilities on the GPU.
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 | Replicate | Modal |
|---|---|---|
| Starting price | Contact sales | Contact sales |
| Free plan | Under review | Yes |
| API available | Related platform API | Related platform API |
| Serverless GPU with scale to zero | Not documented | Serverless execution billed per second |
| Dedicated or always-on instances | Private models on always-on instances | Not documented |
| Bring your own container or image | Deploy your own custom model | Images defined in Python, or an existing Docker image |
| Prebuilt model catalogue | Public library of published models | Not documented |
| Per-token hosted model API | Per-token billing on some models | Not documented |
| Fine-tuning and training | Fine-tuning on your own data | Fine-tuning and training |
| Autoscaling and concurrency control | Not documented | Automatic container scale-up |
| Range of GPU types offered | T4, L40S, A100, and H100 | T4 through B300, selected in code |
Detailed comparison
Where the differences matter
Workflow and Implementation
The most immediate difference a developer notices is how workloads are defined and launched. Replicate treats models as ready-to-call endpoints. A team can select a model from a public library, pass inputs through a Python or Node.js client library, and receive outputs. Webhooks report prediction lifecycle events, removing the need to poll for status. This workflow is optimized for teams that want to integrate an existing model into an application without understanding the underlying machine learning infrastructure. Modal requires a different approach. Instead of calling a pre-existing model endpoint, developers define their container images and write the Python code that runs on the GPU. The platform allows images to be declared in Python code alongside the execution logic, meaning no Dockerfile is strictly required, though bringing an existing Docker image is also supported. Workloads scale automatically as traffic demands. This implementation path suits engineers who need to run custom code, manage specific dependencies, or execute training and fine-tuning scripts from scratch.
Control and Extensibility
Control over the execution environment dictates which tool fits a given project. Replicate abstracts the environment away for public models, but it does offer hardware selection, allowing users to choose between T4, L40S, A100 80GB, and H100 GPUs per model version. For teams that need to go beyond the public library, Replicate allows deploying custom models as private, always-on instances and provides fine-tuning capabilities on customer-provided training data. Modal provides a deeper level of control over the compute environment. Because the developer writes the code that runs on the GPU, they dictate the dependencies and the execution flow. Hardware selection is also granular, with published rates covering T4, L4, L40S, A100 40GB and 80GB, H100 SXM5, H200 SXM, B200, and B300 GPUs, selected directly in the function decorator. Modal supports fine-tuning and training, with documented examples including fine-tuning an image model and training a small language model from scratch. This level of control is necessary when a project requires custom dependencies or training loops that cannot be accommodated by a pre-packaged model API.
Pricing and Value
Both platforms bill compute usage by the second, but their pricing structures reflect their different orientations. Replicate charges based on the time a model takes to run, with per-second rates varying by hardware. For example, a T4 costs $0.000225 per second, while an H100 costs $0.001525 per second. Some models are billed per token or per output instead of by time. However, private models deployed as always-on instances are charged for all the time they are online, including setup and idle time, which can increase costs for deployments with sporadic traffic. Replicate does not mention a free tier or trial credits on its pricing page. Modal operates on a freemium model. The Starter plan costs $0 per month and includes $30 of compute credits, allowing developers to test workloads without an upfront financial commitment. Compute is billed per second across a wide range of GPUs, with a T4 costing $0.000164 per second and a B300 costing $0.001972 per second. CPU and memory are also billed per second. For teams, the Team plan costs $250 per month before any compute is used, but includes $100 of monthly credits and unlimited seats. Modal's pricing structure favors teams that want to experiment with free credits and scale based on actual compute usage rather than instance uptime.
Best use case for Replicate
Product teams adding a model feature without ML infrastructure work.
Best use case for Modal
Engineers who need custom code, custom dependencies, or training.
Replicate: pros and cons
What works
- A published model can be run from the API without packaging or deploying anything.Replicate official documentation
- Client libraries cover Node.js, Python, and Colab.Replicate official documentation
- Webhooks report prediction lifecycle events rather than requiring polling.Replicate official documentation
Tradeoffs
- Private models bill for setup and idle time as well as processing, so an always-on deployment costs more than its request time.Replicate official pricing page
- No free tier or trial credit is mentioned on the pricing page.Replicate official pricing page
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
Decision framework
How to choose between Replicate and Modal
Choose Replicate if your primary goal is to integrate an existing open-source model into your application quickly. It is the right choice for product teams that want to avoid ML infrastructure work and need a simple API to call published models. Choose Modal if you need to run your own code on the GPU, require custom dependencies, or need to perform training and fine-tuning from scratch. It is the better fit for engineers who want granular control over their container environment and benefit from per-second billing with free monthly credits.
Bottom line
Our verdict
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.
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.
Replicate
VerifiedLast verified August 20, 2026
Modal
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
Replicate vs Modal FAQ
Can I run my own custom code on Replicate?
Yes, Replicate allows you to deploy custom models as private, always-on instances, though you are billed for the time the instance is online, including idle time.
Does Modal offer a free tier?
Yes, Modal offers a Starter plan for $0 per month that includes $30 of compute credits.
Can I fine-tune models on both platforms?
Yes, both platforms support fine-tuning. Replicate allows fine-tuning on your own training data, and Modal provides documented examples of fine-tuning and training models from scratch.
Do both platforms bill by the second?
Yes, both Replicate and Modal bill compute usage by the second, though Replicate also offers per-token billing for some models.
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.
Continue researching
Related comparisons and alternatives
The 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 guideThe best Replicate alternatives
Replicate's per-second billing on private, always-on instances charges for setup and idle time as well as active processing, so a custom model deployment that sits waiting for traffic costs more than the requests it actually serves. That cost structure is reasonable for a platform whose value is a large public library of models runnable from a cloud API without packaging, but it pushes teams with sustained or bursty custom-model workloads toward alternatives that separate serverless scaling from dedicated GPU time. The candidates below differ in whether they offer a model catalogue, how they handle idle billing, and how much infrastructure the buyer must manage.
Read guideModal vs Baseten
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.
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