RTX 3050 - Order Now

Together.ai Alternative

Dedicated GPU Servers — Fixed Monthly Pricing, No Per-Token Fees

Why Switch from Together.ai to a Dedicated GPU Server?

Together.ai offers a serverless inference API for open-source models — you send requests, they handle the infrastructure, and you pay per token. It’s a fast way to get started, but costs compound quickly at production volumes, you share GPU capacity with other tenants, and your data passes through a third-party pipeline with every request.

A dedicated GPU server from GigaGPU gives you the same open-source models — LLaMA, Mistral, DeepSeek, Qwen, and more — running on your own bare-metal hardware at a flat monthly rate. No per-token billing, no noisy neighbours, no data leaving your server.

£0
Per-Token Fees
100%
Dedicated Resources
UK
Data Centre
Full
Root Access

Together.ai vs Dedicated GPU Hosting

A side-by-side look at the key differences between serverless inference APIs and self-hosted GPU infrastructure.

Together.ai (Serverless API)

Pricing modelPer token
Cost at scaleGrows with usage
GPU resourcesShared / multi-tenant
Data privacySent to third party
Model selectionPlatform catalogue only
Fine-tuningLimited / platform-managed
LatencyShared queue
Infrastructure controlNone

Dedicated GPU (GigaGPU)

Pricing modelFixed monthly rate
Cost at scaleSame flat rate
GPU resources100% dedicated
Data privacyNever leaves your server
Model selectionAny model you want
Fine-tuningFull access
LatencyDedicated hardware
Infrastructure controlFull root access

Cost Example: LLaMA 3 70B at Scale

Together.ai: At $0.90 per million tokens, processing 50 million tokens per month costs roughly $45/month — and that’s just one model at moderate volume. Add multiple models, higher concurrency, or fine-tuning, and the bill scales quickly.
Dedicated GPU: An RTX 5090 with 32 GB VRAM handles the same workload at a fixed monthly cost — unlimited tokens, unlimited requests, no surprises. Run multiple models simultaneously on the same hardware.

Why Teams Move from Together.ai to Dedicated GPUs

The benefits of owning your inference infrastructure instead of renting it per token.

Predictable Monthly Cost

Together.ai charges per token — costs fluctuate with traffic spikes, prompt length, and model choice. A dedicated GPU server is a single flat monthly fee regardless of how many tokens you process.

Complete Data Privacy

Every API call to Together.ai sends your prompts and data through their infrastructure. On a dedicated server, your data never leaves your machine — critical for regulated industries, proprietary data, and GDPR compliance.

No Noisy Neighbours

Serverless APIs share GPU capacity across tenants. During peak demand, your inference latency suffers. A dedicated GPU means 100% of the compute is yours — consistent performance around the clock.

Full Infrastructure Control

Install any model from Hugging Face, fine-tune with your own data, run custom quantisations, deploy vLLM or Ollama, and build compound AI pipelines — all with full root SSH access.

Run Any Open-Source Model

Together.ai limits you to their supported model catalogue. On your own server, deploy any model — LLaMA, Mistral, DeepSeek, Qwen, Phi, Command R, Gemma, or your own fine-tuned weights.

No Rate Limits or Queues

Together.ai enforces rate limits and request queues during high demand. Your dedicated GPU serves only your workloads — send as many concurrent requests as the hardware can handle.

What Can You Run on a Dedicated GPU Instead of Together.ai?

Everything you run on Together.ai’s serverless API — and more — on infrastructure you control.

LLM Inference & Chatbots

Deploy open-source LLMs behind vLLM or Ollama with an OpenAI-compatible API. Run private ChatGPT-like chatbots with no usage caps.

Code Generation

Self-host code models like DeepSeek Coder, Qwen2.5 Coder, or StarCoder for private code completion, review, and generation without sending your source code to a third party.

RAG Pipelines

Build retrieval-augmented generation systems with LangChain or LlamaIndex backed by your own embedding model and vector store — all on a single server.

Image Generation

Run Stable Diffusion, SDXL, or Flux on your own GPU without per-image API costs. Generate unlimited images for products, marketing, and creative workflows.

Speech & Audio AI

Deploy speech models like Whisper, XTTS-v2, or Kokoro TTS at a flat rate — no per-minute or per-character billing.

Multimodal AI

Host multimodal models like LLaVA, Qwen-VL, or InternVL for vision-language tasks, document understanding, and image analysis on private infrastructure.

Dedicated GPU Servers — Fixed Monthly Pricing

No per-token fees. No rate limits. Full root access. Every server includes a Ryzen CPU, DDR4/5 RAM, NVMe storage, and a 1 Gbps port.

RTX 3050

6 GB GDDR6
  • Architecture: Ampere
  • CUDA Cores: 2,304
  • FP32: 6.8 TFLOPS
  • Bandwidth: 168 GB/s
From /mo
Configure

RTX 4060

8 GB GDDR6
  • Architecture: Ada Lovelace
  • CUDA Cores: 3,072
  • FP32: 15.1 TFLOPS
  • Bandwidth: 272 GB/s
From /mo
Configure

RTX 5060

8 GB GDDR7
  • Architecture: Blackwell 2.0
  • CUDA Cores: 3,840
  • FP32: 19.2 TFLOPS
  • Bandwidth: 448 GB/s
From /mo
Configure

RTX 4060 Ti 16GB

16 GB GDDR6
  • Architecture: Ada Lovelace
  • CUDA Cores: 4,352
  • FP32: 22.1 TFLOPS
  • Bandwidth: 288 GB/s
From /mo
Configure

RTX 3090

24 GB GDDR6X
  • Architecture: Ampere
  • CUDA Cores: 10,496
  • FP32: 35.6 TFLOPS
  • Bandwidth: 936 GB/s
From /mo
Configure

RTX 5080

16 GB GDDR7
  • Architecture: Blackwell 2.0
  • CUDA Cores: 10,752
  • FP32: 56.3 TFLOPS
  • Bandwidth: 960 GB/s
From /mo
Configure

RTX 5090

32 GB GDDR7
  • Architecture: Blackwell 2.0
  • CUDA Cores: 21,760
  • FP32: 104.8 TFLOPS
  • Bandwidth: 1.79 TB/s
From /mo
Configure

RTX 6000 PRO

96 GB GDDR7
  • Architecture: Blackwell 2.0
  • CUDA Cores: 24,064
  • FP32: 126.0 TFLOPS
  • Bandwidth: 1.79 TB/s
From /mo
Configure

All servers include a Ryzen CPU, DDR4/5 RAM, NVMe storage, 1 Gbps port, 99.9% uptime, and any OS. View all GPU plans →

Frequently Asked Questions

Common questions about switching from Together.ai to a dedicated GPU server.

Yes. Together.ai primarily serves open-source models like LLaMA, Mistral, DeepSeek, Qwen, and others. All of these are freely available on Hugging Face and can be self-hosted on a dedicated GPU using inference engines like vLLM or Ollama. You can also run models that Together.ai doesn’t offer.
At sustained usage, typically yes — and often by a significant margin. Together.ai charges per token, so costs scale linearly with traffic. A dedicated GPU processes unlimited tokens at a flat monthly rate. The break-even point depends on your volume and model size, but most production workloads become cheaper on dedicated hardware within the first month of sustained use.
The typical path is: order a dedicated GPU server, install vLLM or Ollama, download your model from Hugging Face, and start the inference server. Both vLLM and Ollama expose an OpenAI-compatible REST API, so you usually just change the base URL in your application code. Most teams complete the migration in under an hour.
It depends on the model size. For 7B–13B parameter models (Mistral 7B, LLaMA 3 8B), an RTX 4060 Ti 16GB or RTX 3090 works well. For 30B–70B models, an RTX 5090 (32 GB) or RTX 6000 PRO (96 GB) provides the VRAM needed. For smaller models or embeddings, even an RTX 4060 is sufficient. Contact us if you’re unsure.
Yes. Both vLLM and Ollama expose a drop-in OpenAI-compatible REST API out of the box. Your existing code that calls Together.ai’s API can typically be switched over by changing a single base URL — no code refactoring required.
Yes. With full root access, you can run LoRA, QLoRA, or full fine-tuning using frameworks like PyTorch, Hugging Face Transformers, and Axolotl. Together.ai offers managed fine-tuning for a limited set of models — on your own hardware, you can fine-tune any model with any dataset and any method.
All servers are located in the UK. This makes them well-suited for teams with GDPR requirements or data residency needs. For latency-sensitive workloads, a UK-based server also provides strong connectivity to European users.
Our team can help with GPU selection, model deployment, and configuration. Contact sales or browse the knowledgebase for setup guides on vLLM, Ollama, and popular open-source models.

Replace Together.ai with Your Own GPU Server

Flat monthly pricing. Unlimited tokens. Full root access. UK data centre. Deploy any open-source model in under an hour.

View All GPU Plans Talk to Sales LLM Hosting Guide

Have a question? Need help?

  • GPU VPS
  • Dedicated GPU Hosting
  • Multi GPU
  • Affiliate
  • Client Area
  • Announcements
  • Knowledgebase
  • Contact us
  • Network Status
  • Terms of Service

    • 65 Victory Avenue
    PE7 1XU, England

    GPUAIR © All Rights Reserved.