Multi-cloud model serving/orchestration platforms (AWS + GCP + on‑prem) that avoid vendor lock-in

Most teams don’t start out wanting “multi‑cloud model orchestration.” They just want their LLMs and vision models to be fast, cheap, and reliable. Then reality hits: some workloads need to run in AWS, others in GCP, a few on an on‑prem Kubernetes cluster, and suddenly you’re fighting three different deployment stacks and one giant fear—vendor lock‑in.

This is where a true multi‑cloud model serving/orchestration platform matters: a single control plane that can deploy and route traffic across AWS, GCP, and on‑prem without forcing you into one provider’s ecosystem or a bespoke migration every time you want to switch hardware.

Quick Answer: Multi‑cloud model serving/orchestration platforms give you a unified control plane to deploy and manage models across AWS, GCP, and on‑prem/air‑gapped environments. Clarifai’s Compute Orchestration, Local Runners, and Control Center are built specifically to avoid vendor lock‑in while optimizing GPU cost and latency at scale.

The Quick Overview

What It Is: A unified, model-agnostic control plane that deploys and orchestrates any model (LLMs, multimodal, vision, custom) across cloud providers and on‑prem compute from one place.
Who It Is For: ML infra teams, platform engineers, and GenAI product owners who have workloads split across AWS, GCP, and private clusters—and who can’t afford a rewrite every time they change providers or hardware.
Core Problem Solved: Slow, expensive inference and fragmented deployments across multiple clouds that trap you in vendor‑specific tooling and duplicated infrastructure.

How It Works

At a high level, a multi‑cloud model serving/orchestration platform gives you:

A single control plane to define models, workflows, and deployments
Multiple compute planes—Clarifai SaaS, your AWS/GCP VPC, on‑prem, or air‑gapped
A runtime/orchestrator that handles autoscaling, GPU fractioning, batching, and routing
A unified API surface (including OpenAI‑compatible endpoints) so clients don’t change when the underlying hardware or cloud does

Clarifai does this with a few key building blocks:

Compute Orchestration (the control plane):
You register models (open, third‑party, or your own) and define how they should be deployed: instance type, autoscaling behavior, batching limits, and environments (Clarifai SaaS, BYO AWS/GCP, on‑prem). It then orchestrates workloads across those compute targets and optimizes GPU utilization automatically.
Armada + Local Runners (the compute plane):
Armada handles serverless and dedicated inference in Clarifai’s cloud. Local Runners let you bridge your own hardware—AWS, GCP, on‑prem K8s, bare metal, even air‑gapped—into that same control plane via a lightweight runner (think “ngrok for AI models”). No inbound ports or complex networking required.
Control Center (observability and governance):
A single pane of glass for latency, tokens/sec, cost, and usage across all environments. You get RBAC/Teams and enterprise‑grade governance so you’re not debugging three separate stacks or re‑implementing guardrails in each cloud.

Because the API surface stays consistent—including OpenAI‑compatible endpoints—your migration path is mostly base_url + API key swaps, not a full client rewrite.

How Clarifai’s Multi‑Cloud Orchestration Works Step‑by‑Step

Register and package your model (or pick an existing one):
- Choose from hosted models (Kimi K2.5, DeepSeek, Llama, GPT‑OSS‑120B, Claude, vision models, etc.), or
- Upload your own checkpoint/container and let Clarifai package it for deployment.
  Models become versioned entities in the control plane, not tied to a single cloud.
Attach deployments to the environments you care about:
For each model version, you can define multiple deployments:
- Clarifai SaaS:
  - Serverless for bursty workloads
  - Dedicated nodes for consistent throughput
- Your AWS or GCP VPC:
  - Connect via a simple Helm install on your K8s cluster
  - No inbound ports, VPC peering, or custom IAM gymnastics
- On‑prem / air‑gapped:
  - Install Local Runners in your K8s/bare‑metal cluster
  - The runner initiates outbound connections to Clarifai’s control plane
Compute Orchestration manages clusters and nodepools so you can centrally manage all compute, regardless of where it lives.
Route traffic intelligently and optimize cost/performance:
Once deployments exist, you choose how requests are routed:
- Route sensitive workloads to on‑prem/air‑gapped runners
- Route latency‑sensitive workloads to the nearest region or fastest model
- Route cost‑sensitive workloads to the cheapest GPU shapes or spot instances
Under the hood, Clarifai uses GPU fractioning, batching, autoscaling, and fast cold starts to reduce compute by up to 70% and support 1.6M+ inference requests/sec. Third‑party benchmarks (e.g., Artificial Analysis) have verified Clarifai as the #1 fastest provider for models like Kimi K2.5, with TTFA in sub‑millisecond ranges and >400 tokens/sec.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Compute Orchestration	Central control plane for deploying any model across SaaS, AWS, GCP, on‑prem, and air‑gapped environments	Avoids vendor lock‑in and multi‑stack chaos; one place to manage all model serving
Local Runners & BYO Compute	Connect your own K8s clusters and hardware (cloud or on‑prem) without opening inbound ports or setting up VPC peering	Use your existing GPUs/CPUs while keeping a unified platform; meet regulatory and network constraints
GPU Fractioning, Batching, Autoscaling	Right‑size GPU allocation, batch requests, and scale dynamically based on load	Cut GPU spend by 70%+ while maintaining ultra‑low latency under concurrency
OpenAI‑Compatible Endpoints	Present a drop‑in compatible API surface for LLMs	Migrate providers with a base_url/API key change instead of rewriting your application clients
Control Center & Governance	Unified telemetry, cost/usage views, RBAC/Teams, and policy controls across all environments	Prevent AI sprawl, enforce guardrails, and enable multi‑team collaboration without losing control
Model‑Agnostic Workflows (Mesh)	Chain models, tools, and agents into workflows that execute as one API call	Abstract away individual deployment locations; workflows keep working even if you move models across clouds
AI Lake™ & Spacetime Search	Centralized storage for inputs, annotations, embeddings, and versioned datasets	Durable assets you can reuse across clouds and models, instead of bespoke data silos per provider

Ideal Use Cases

Best for “AWS + GCP + on‑prem” enterprises:
Because you can register models once and deploy them to each environment without re‑platforming. You control where each workload runs (SaaS, AWS VPC, GCP VPC, on‑prem, air‑gapped) while maintaining one API and one governance layer.
Best for GenAI teams avoiding lock‑in to a single LLM provider:
Because Clarifai is model‑agnostic, you can run multiple foundation models and switch between them based on cost, speed, or quality—without changing clients. If a new open model beats your current vendor, you deploy it via Compute Orchestration and update routing rules; your application code stays the same.
Best for regulated workloads that can’t leave a private network:
Because Local Runners let you run inference inside your own K8s clusters (including air‑gapped) while still using Clarifai’s control plane and API. No inbound ports, no VPC peering, no fragile IAM integration.
Best for teams hitting GPU and cost ceilings:
Because GPU fractioning, batching, and autoscaling are implemented across environments, not re‑implemented per cloud. You get measurable improvements like 65% faster TTFA and 40% faster response times without having to become GPU runtime experts.

Limitations & Considerations

You still need basic Kubernetes and cloud chops:
Clarifai simplifies orchestration, but connecting your own AWS/GCP VPC or on‑prem cluster still assumes you can manage a K8s environment and run a Helm install. For fully managed scenarios, use Clarifai SaaS compute first, then expand to BYO compute.
Not a data warehouse replacement:
AI Lake™ and Spacetime Search handle AI‑native assets (inputs, embeddings, labels, model artifacts) extremely well, but they don’t replace your enterprise data warehouse or lakehouse. Plan integrations accordingly.
Vendor neutrality doesn’t eliminate tradeoffs:
Multi‑cloud flexibility doesn’t mean everything should run everywhere. You still need to decide which workloads justify duplication across AWS/GCP/on‑prem and where latency, compliance, or egress costs demand local execution.

Pricing & Plans

Clarifai’s pricing is structured around inference usage, hosted models, and deployment type, not tying you to a specific cloud provider:

Serverless inference on shared GPUs for bursty workloads
Dedicated nodes for high, predictable traffic
BYO compute via Local Runners where you pay for Clarifai’s control plane and orchestration while using your own hardware

Common patterns:

Run latency‑sensitive or public‑facing workloads on Clarifai’s high‑throughput SaaS GPUs.
Run sensitive or regulated traffic on Local Runners in your AWS/GCP VPC or on‑prem clusters.
Mix both under the same account, API, and governance model.

For exact pricing, see the Clarifai pricing page or start a free account and inspect the cost calculator from within the dashboard.

Starter / Free Tier: Best for developers and small teams needing to test multi‑cloud orchestration patterns and OpenAI‑compatible endpoints without upfront commitment.
Enterprise Plan: Best for organizations needing BYO cloud/on‑prem/air‑gapped deployments, advanced RBAC/Teams, 99.99% SLAs, and deep cost controls across multiple environments.

Frequently Asked Questions

How is this different from just using SageMaker on AWS and Vertex AI on GCP?

Short Answer: Clarifai gives you one control plane, one API, and one governance layer across AWS, GCP, and on‑prem—without rewriting for each provider’s stack.

Details:
If you adopt SageMaker + Vertex + an on‑prem stack, you’re essentially committing to:

Three deployment pipelines
Three monitoring stacks
Different security models and IAM setups
Different SDKs and APIs

Every time you switch model providers or hardware, you’re doing multi‑provider plumbing again.

Clarifai instead:

Lets you define models and workflows once in the control plane.
Deploys to Clarifai SaaS, AWS, GCP, and on‑prem via Compute Orchestration and Local Runners.
Presents a consistent API (including OpenAI‑compatible) to your applications.
Centralizes monitoring, cost visibility, RBAC, and guardrails in Control Center.

You still use AWS/GCP as raw compute, but you’re not locked into their AI platforms or SDKs.

Can I move models between clouds without impacting my application clients?

Short Answer: Yes. With Clarifai, switching clouds or hardware is usually a deployment change, not an application change.

Details:
Because Clarifai separates control plane from compute plane:

Your model is a logical entity with versions and metadata in Clarifai.
You can attach multiple deployments to that model: e.g., Clarifai SaaS, AWS VPC, GCP VPC, on‑prem.
Routing rules determine which deployment handles traffic.

If you decide to shift a workload from AWS to GCP (or add GCP as a failover/secondary):

You deploy the model to your GCP cluster via Local Runner.
You update routing rules in the control plane.
Your client keeps calling the same Clarifai endpoint.

If your app uses OpenAI‑compatible endpoints, migration can be as simple as:

# Before: direct to OpenAI
export OPENAI_BASE_URL="https://api.openai.com/v1"
export OPENAI_API_KEY="<openai_key>"

# After: point to Clarifai gateway
export OPENAI_BASE_URL="https://api.clarifai.com/v1/openai"
export OPENAI_API_KEY="<clarifai_pat>"

No SDK change. No per‑cloud logic in your application.

Summary

Multi‑cloud model serving/orchestration isn’t about ticking a “supports AWS and GCP” checkbox. It’s about getting one control plane that can:

Deploy any model to any environment—Clarifai SaaS, AWS, GCP, on‑prem, or air‑gapped
Optimize GPU usage with fractioning, batching, and autoscaling to cut compute by up to 70%
Maintain ultra‑low latency and high throughput (410+ tokens/sec, sub‑ms TTFA) under load
Keep governance, observability, and guardrails centralized so AI sprawl doesn’t eat your budget

Clarifai’s Compute Orchestration, Armada, Local Runners, AI Lake™, Spacetime Search, Mesh, and Control Center work together to give you multi‑cloud flexibility without vendor lock‑in and without re‑implementing the stack per cloud.

If you’re serious about running AI across AWS, GCP, and on‑prem, the fastest path is to standardize on a unified control plane and treat clouds as interchangeable compute backends.

Next Step

Get Started