Intelligent routing across every single AI model (GPT, Claude, Gemini, Deepseek, Hugging Face, etc). Deterministic, secure, enterprise-ready.
Joined January 2026
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Missed today's Gatewayz AMA? 🎙️
We've got you covered!
Catch the full recording below to hear the latest updates, what we've been working on, and where Gatewayz is heading next.
What does "the inference layer for the agentic internet" actually mean?
When an AI agent asks a model to interpret information or decide its next step, that's inference.
Gatewayz connects those requests to the model providers.
Your application manages the task, the tools and the actions.
Gatewayz handles the model-access side: authenticating requests, checking limits, forwarding calls and recording usage.
Clear responsibilities make the stack easier to reason about.
We want developers choosing models because they fit the job.
Gatewayz brings multiple providers behind one inference layer, without a model of our own to push.
Your application. Your model choice. Infrastructure built around both.
When agents do the work, you still need to know what the work costs.
Gatewayz lets you tag inference requests and track calls, tokens and cost by tag.
Connect the model usage to the task behind it. That's how you start understanding an agent's economics.
We're building Gatewayz as the inference layer for the agentic internet.
One place for agents to access models across providers, with the controls developers need to run them.
Build the agent. Connect the intelligence.
DeepSeek has opened its Search and Fetch APIs to developers.
Search finds relevant web pages. Fetch retrieves their contents.
For builders, that's another option for giving an AI app current information instead of relying only on what its model already knows.
Weekend build idea: a release-note assistant that turns merged pull requests into a draft your team can review.
Start with one repository and one release window. Give every proposed change a link back to its pull request.
Have it separate user-facing changes from internal maintenance, then draft a plain-English summary for each.
If a pull request doesn't explain the impact, flag it for the author. Keep those questions beside the draft.
AI had a busy week (Oct 7–15). The big stories:
1) Google released Gemini 4 Argon for developers and businesses, targeting complex coding, research and multi-step tasks.
2) Meta launched Muse Wonder and Wonder Full, its new frontier models for agents.
5) Amazon introduced Bee Frontier and Eclipse, personal AI devices that turn conversations into reminders, summaries and tasks.
6) Character.AI filed for Chapter 11. Disney is bidding $20M for a license to some of its IP, highlighting the brutal economics of AI apps.
New models are competing. The tools and economics around them are evolving just as fast.
Build across providers → gatewayz.ai
If you could add one AI feature to the product you're building, what would it do?
Not a whole new app. Just one task you'd love to make easier.
Your backend doesn't need a new language just because you're adding an agent.
Google's latest ADK guide covers Python, Go, TypeScript, Java and Kotlin.
Build where your application already lives. Spend the effort on the workflow.
You chose a model for a reason. Your gateway should respect that choice.
Gatewayz resolves model names explicitly and refuses unknown or ambiguous requests instead of silently picking a replacement.
For version stability, pin an available dated snapshot.
Keep model selection in your hands. Connect your app to Gatewayz and choose the model you want to run.
beta.gatewayz.ai/
