Together AI Alternative
The Together AI alternative —
full GPU platform, not just an API.
Pods, VMs, Baremetal, Fractional GPU. Persistent storage. SDK and CLI. Run your own vLLM, your own models, your own runtime. Sovereign India. And — uniquely — licensable.
Why teams move from Together to PodStack
Pods, VMs, Baremetal, Fractional GPU, persistent storage. Together is API-first; PodStack is platform-first.
Launch vLLM with your own weights. Control batching, quantisation, and routing.
Indian DCs, DPDP-ready, no forex/GST stack. Together is US-based with USD billing.
License the same stack and run it inside your own DCs. Together does not offer this.
PodStack vs Together AI — feature by feature
| Feature | PodStack | Together AI |
|---|---|---|
| Platform stack | Proprietary, full IaaS for GPU | Open-source-based inference / fine-tuning API |
| Platform license | Available — license + run in your own DC | No — managed only |
| Compute model | Pods, VMs, Baremetal, Fractional GPU | Inference API + GPU cluster rental |
| Persistent storage | S3-compatible bucket + NFS | API-scoped storage |
| Fractional GPU | 12.5% – 100% via PodVirt | Per-token / full GPU rental |
| Data residency | India (Bengaluru DCs) | US |
| Billing currency | INR — incl. taxes | USD |
| Compliance | ISO 27001, DPDP-ready | SOC 2 (US) |
Pricing side-by-side
Together AI cluster-rental prices converted at ₹83.5/USD. Per-token API pricing not directly comparable to dedicated GPU.
| GPU / SKU | PodStack | Together AI |
|---|---|---|
| NVIDIA A100 80GB | ₹292/hr (dedicated) | ~$2.40/hr (~₹200/hr) cluster rental |
| NVIDIA H100 80GB | ₹333/hr (dedicated) | ~$3.36/hr (~₹281/hr) cluster rental |
| NVIDIA H100 fractional (25%) | ~₹84/hr | Not offered |
| Inference (per-token) | Run your own — pay GPU time | Per-token API pricing |
Migrating from Together AI
- Step 1Package your serving image (or use the vLLM template)
docker tag my-vllm:latest registry.podstack.ai/<org>/my-vllm:latest docker push registry.podstack.ai/<org>/my-vllm:latest - Step 2Sync model weights to PodStack S3
aws s3 sync ./model/ s3://my-bucket/model/ \ --endpoint-url https://s3.podstack.ai - Step 3Launch and expose an inference endpoint
podstack pod create -f podstack.yaml
Frequently asked questions
Why look for a Together AI alternative?+
Together AI is excellent for serverless inference on shared models and short-lived fine-tuning jobs. If you need the full GPU platform — Pods, VMs, Baremetal, persistent storage, fractional GPU, your own Docker images, and control over the runtime — PodStack is purpose-built for that. PodStack is also India sovereign, INR billed, and licensable.
Is PodStack built on open-source?+
No. PodStack is a proprietary, purpose-built platform — our own control plane, scheduler, and virtualisation layer (PodVirt) designed specifically for fractional GPU sharing. Together AI is built largely around open-source inference stacks.
Can we license the PodStack platform to run our own GPU cloud?+
Yes. PodStack is sold both as a managed cloud and as a licensable platform. Enterprises, government departments, and operators can license the full PodStack stack and deploy it in their own data centres. Together AI does not offer this.
Does PodStack have an inference API like Together?+
PodStack does not currently sell per-token shared-model inference. We give you the GPU platform underneath — launch vLLM in a Pod with your model, expose an endpoint, and you control the runtime, the model, and the pricing. Lower per-call cost at scale; more control.
Does PodStack support vLLM, Unsloth, ComfyUI?+
Yes. One-click templates for vLLM, Unsloth, ComfyUI, PyTorch, and TensorFlow. BYO Docker also supported.
Is PodStack DPDP and government-empanelment ready?+
Yes. PodStack is ISO 27001 certified, DPDP-compliant, and government-empanelment ready. All compute and storage stay inside Indian data centres.
How do I migrate from Together AI to PodStack?+
For fine-tuning / training jobs: package the script in a Docker image, sync weights to a PodStack S3 bucket, launch a Pod with `podstack pod create -f podstack.yaml`. For inference: launch vLLM in a Pod with your model and route traffic to the Pod's endpoint.
Own your GPU stack.
Launch a Pod in 60 seconds — or talk to us about platform licensing.
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