Route AI requests across any inference provider. Deliver high-bandwidth objects from the edge. Control performance, reliability, and cost without rebuilding your stack.
Two products built on the same global infrastructure. Start with one. Scale into both as your AI workloads grow.
Route AI requests across hosted models, open-source inference, and private endpoints with automatic fallback, cost controls, observability, and policy-based routing.
Explore AI RouterPut Pipe in front of S3 and serve repeat-heavy objects from the edge. Keep S3 as your source of truth while reducing egress costs and improving delivery performance.
Explore S3Point your existing OpenAI client at Pipe and route across any provider with one parameter. No SDK migration. No rewrites. No vendor lock-in.
model="auto" for cost-aware routingfrom openai import OpenAI client = OpenAI( api_key="pipe_sk_...", base_url="https://api.pipe.network/v1" ) # Route across providers with one parameter response = client.chat.completions.create( model="auto", messages=[{"role": "user", "content": "Summarize Q3 earnings"}], routing={"policy": "cost_aware", "fallback": "auto"} )
import OpenAI from "openai"; const client = new OpenAI({ apiKey: "pipe_sk_...", baseURL: "https://api.pipe.network/v1", }); // Route across providers with one parameter const response = await client.chat.completions.create({ model: "auto", messages: [{ role: "user", content: "Summarize Q3 earnings" }], routing: { policy: "cost_aware", fallback: "auto" }, });
curl https://api.pipe.network/v1/chat/completions \ -H "Authorization: Bearer pipe_sk_..." \ -H "Content-Type: application/json" \ -d '{ "model": "auto", "messages": [ {"role": "user", "content": "Summarize Q3 earnings"} ], "routing": { "policy": "cost_aware", "fallback": "auto" } }'
Two cloud layers are becoming one. Pipe is the inference layer where data and compute converge.
Modern AI applications depend on reliable inference and fast access to large data. Teams need to route requests across providers, serve model files and datasets closer to users, reduce egress costs, and keep applications online when providers degrade.
Pipe brings inference routing and edge data delivery into one cloud layer.
Connect any hosted model, open-source inference provider, or private endpoint through one API.
Serve model files, datasets, media, software assets, and S3 objects from a global edge network.
Reduce egress, manage usage, route by policy, and optimize infrastructure spend across providers.
Most teams start with one model provider. Then they add another. Then fallback logic, usage tracking, cost controls, evals, rate limits, private endpoints, and provider-specific SDKs. Eventually, they're maintaining their own inference platform.
Pipe gives teams a managed routing layer for inference, plus the edge delivery layer needed for AI data — so engineering stays focused on the application, not the plumbing.
Route user requests across providers based on cost, latency, availability, or model quality.
Keep agents online with fallback routing, provider controls, and request-level observability.
Serve model files, weights, datasets, and inference traffic from one infrastructure layer.
Deliver large files, video assets, generated media, and model outputs from the edge.
Centralize inference access, team usage, budgets, provider policies, and private endpoint routing.
Use Pipe AI Router to control inference across providers. Use Pipe for S3 to reduce object delivery costs. Bring them together as your AI infrastructure scales.