Together AI Model Cloud

🚀 Quick Verdict

The speed of the serverless API surprised us during high-traffic tests. We ran Llama 3 70B and saw tokens per second that beat most managed providers. It’s a clear choice for founders who want to cut their API bills without losing performance.

Overall Score	9.2/10
Best For	Founders scaling AI apps
Tested Plan	Pay-per-token (Serverless)
Testing Period	21 days
Biggest Strength	Inference speed on open models
Biggest Weakness	Niche model availability delays
Best Alternative	Replicate

🤔 What Is Together AI?

Together AI is a cloud platform built specifically for open-source artificial intelligence. It provides the hardware and software stack to run, train, and fine-tune models like Llama, Mistral, and Qwen. It solves the problem of high costs and lack of control found in closed-source ecosystems.

The company focuses on research-driven optimization, meaning they write custom code to make GPUs run faster. This results in lower prices for founders who need to process millions of tokens daily. It’s built for teams that have moved past the prototyping phase and need production-grade reliability.

⚙️ How We Tested Together AI

We used the serverless API on a pay-per-token basis for three weeks. We focused on three specific founder tasks: deploying a Llama 3.1 70B model for a customer chatbot, running 50,000 rows of data through the Batch Inference API, and setting up a fine-tuning job using a custom brand voice dataset.

✨ Key Features (What Actually Stood Out)

The platform stands out by making complex coding infrastructure accessible through a simple API. You don’t need to manage individual GPUs unless you choose to move to dedicated clusters.

• Serverless Inference — We connected this to our app in under five minutes. It handles the scaling automatically, so we didn’t have to worry about cold starts during traffic spikes.
• Batch Inference API — This was a major cost saver for non-urgent tasks. We processed large datasets at roughly half the price of standard real-time requests.
• Together Kernel Collection — These are custom optimizations that make models run faster. We noticed a visible difference in latency compared to running the same models on generic cloud providers.
• Fine-Tuning Platform — The interface for uploading data and starting a training job is clean. It now supports tool calling and vision models, which we tested with a small image-to-text dataset.
• GPU Clusters — For very large projects, you can rent dedicated H100 or H200 clusters. We didn’t need this for our small app, but the self-service setup looked straightforward.

💰 Together AI Pricing — Is It Worth It?

Pricing is transparent but usage-based, which can be tricky for budgeting. There are no heavy monthly fees for the basic tier, but you pay for what you use. This is ideal for automation workflows where volume fluctuates. Watch out for the Gold support tier, which costs 10% of your total contract.

Plan	Price	Best For	Watch Out For
Serverless	Pay-per-token	Early startups	Rate limits on free tier
Batch	50% discount	Data processing	Not for real-time apps
GPU Clusters	Custom	Large enterprises	Long-term commitments

Our pick: Serverless — It’s the most flexible way to start without an upfront investment or hardware management.

🧪 What We Found During Testing

Setting up our first API call took less time than creating the account itself. We were impressed that the playground allowed us to compare different models side-by-side before writing any code. When we ran a heavy load of 100 concurrent requests, the latency stayed consistent, which is usually a failure point for smaller providers.

A founder in our community who runs a customer support startup told us they switched from OpenAI to Together AI to cut costs. They found the Llama 3 latency was lower for their specific use case and liked that they weren’t locked into one vendor.

⚠️ Limitations We Found

• Model Selection — While they have the big names, very new or niche research models sometimes take a few days to appear on the serverless list.
• Support Response — On the standard tier, support is best-effort. As of late 2024, if you have a production emergency on a weekend, you might be waiting until Monday without a paid support plan.
• Documentation — Some of the advanced fine-tuning guides are written for researchers rather than business owners, making the learning curve steep for non-technical founders.

⚔️ Together AI vs Competitors

Most founders compare this tool against the big cloud giants or specialized model hosts.

Competitor	Pick it instead of Together AI if…
Replicate	You need a massive library of niche image and video models.
Anyscale	You are already heavily invested in the Ray ecosystem for scaling.
AWS Bedrock	You need all your data to stay within your existing AWS VPC.

👍 Pros & Cons

Easy to switch between open models

✅ Pros	❌ Cons
Extremely fast inference speeds	Usage-based billing can be unpredictable
Advanced features require technical skill
Significant cost savings over GPT-4	Standard support is limited to business hours
Clean developer playground for testing	Fine-tuning docs can be overly technical

🎯 Who Should Use Together AI (And Who Shouldn’t)

✅ Use it if you:

• Need to run Llama 3 or Mistral at high volumes for a web app.
• Want to fine-tune a model on your own proprietary business data.
• Are looking for an alternative to OpenAI to avoid vendor lock-in.

❌ Skip it if you:

• Have zero technical knowledge — try a no-code tool like Bubble instead.
• Only need to use AI for occasional, low-volume tasks.
• Require a specific closed-source model that isn’t available here.

❓ Frequently Asked Questions

Is Together AI cheaper than OpenAI?
For most high-volume tasks using open-source models, yes. We found savings of up to 70% for similar performance levels.

Which models are available on Together AI?
They host hundreds of models including Llama 3, Mistral, Qwen, and specialized vision or code models.

Does Together AI offer H100 GPUs?
Yes, you can rent dedicated clusters of NVIDIA H100 and H200 GPUs for large-scale training or inference.

What is the Batch Inference API?
It is a way to process large amounts of data at a lower cost by allowing the system to run the job when demand is lower.

Can I fine-tune models on Together AI?
Yes, they have a dedicated platform for fine-tuning that supports tool calling and vision-language models.

Is Together AI safe for private data?
They offer enterprise-grade security and you own the weights of any model you fine-tune on their platform.

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