Production inference for teams that care about cost, latency, and control.
Serve open and frontier models through an OpenAI-compatible API. Scale from your first request to production workloads without managing GPU infrastructure yourself.
Bring us your current inference bill. We'll benchmark your workload against Pipe and show you:
Pipe gives your application a simple interface to production inference while we handle the infrastructure underneath.
Deploy your model and start sending requests.
Run production workloads on the models your application actually needs.
Give agents reliable, high-volume inference without paying for idle capacity.
Build latency-sensitive applications without managing GPU fleets yourself.
Deploy the models behind your image, video, and multimodal applications.
Keep your existing client and change the endpoint.
Traffic changes. Your infrastructure should too.
Scale inference capacity with demand instead of paying for GPUs that sit idle.
Have a steady workload?
Reserve dedicated capacity for predictable performance and production SLAs.
Talk to salesStart with prepaid credits and pay for the tokens you actually consume.
No complicated cloud infrastructure bill.
View models & pricingInference is only one part of the stack. Pipe also gives you the storage and delivery infrastructure needed to move AI workloads from data → model → inference → user.
S3-compatible storage built for large datasets, model artifacts, and AI applications.
AI applications move a lot of data. Models. Datasets. Context. Documents. Images. Audio. Video.
Pipe's distributed infrastructure caches and delivers frequently accessed data closer to the workloads consuming it.
Your application sees an API. Pipe handles the infrastructure underneath.
Build the entire model lifecycle on one infrastructure layer.
Put datasets, model weights, checkpoints, and application data into Pipe Storage.
Fine-tune an open model against your data with managed training infrastructure.
Deploy the resulting model directly to a production inference endpoint.
Use infrastructure optimized around AI workloads instead of paying for a general-purpose cloud stack.
Run the models that make sense for your application.
If you're already using OpenAI-compatible APIs or S3-compatible tooling, getting started is straightforward.
Distribute compute, storage, and cached data closer to your users.
Start with shared inference. Move to dedicated capacity when your traffic demands it.
Don't take our word for it. Enter your current monthly AI infrastructure spend.
Enter your current spend and we'll give you a workload-specific estimate.
Want us to verify the number? Send us your workload.
Get a benchmarkEstimates are based on typical workload migrations and your provider mix — not a quote. A benchmark against your real traffic is the number that counts.
You don't need to move your entire stack.
No. Pipe's inference API is OpenAI-compatible, so existing OpenAI SDK integrations can generally be pointed at Pipe by changing the base URL and credentials.
Pipe supports a growing catalog of open and frontier models. See the current model catalog for availability and pricing.
Yes. For production workloads, Pipe supports dedicated model deployments and custom infrastructure.
Yes. Dedicated endpoints are available for workloads that need predictable capacity and performance.
Pipe Storage provides an S3-compatible API for datasets, model artifacts, application files, and other objects.
Storage is currently priced at $0.02/GB-month with $0.001/GB egress and no per-request fees.
Pipe's infrastructure is designed around distributed compute, storage, and edge capacity. You don't need to manage the network yourself. You simply use Pipe's APIs.
Yes. Start with a small workload and benchmark the results before moving production traffic.
Run it on Pipe.