Together AI Review: Pricing & Comparison

Together AI: a leading player in open-source model aggregation

In the open-source track of AI inference services, Together AI (api.together.xyz) is one of the largest and best-funded players — it closed an $800M Series C in early 2026 at an $8.3B valuation, a rare mega-scale unicorn in this space.

But funding numbers aren’t a reason for developers to pick a platform. What actually matters about Together AI is what it offers: a single API endpoint giving access to 100+ mainstream open-source models — from Meta’s Llama 4 to DeepSeek V3, from Google’s Gemma to Alibaba’s Qwen — all callable in a unified OpenAI SDK format, with no need to apply for a separate API key per model.

What 100+ model coverage actually means in practice

“100+ models” sounds like a marketing number, but for research teams and engineering teams that need to A/B test different models, this coverage has real practical value:

Model comparison experiments: sending the same prompt to both Llama 4 Maverick and DeepSeek V3 to compare output quality — Together AI minimizes the cost of this kind of comparison testing: one account, one bill, one SDK.

Fast-following new models: Together AI typically integrates a new open-source model within days of its release, so you don’t have to wait for the official API to open up or deploy it yourself.

Fault tolerance and fallback: if your primary model (say, DeepSeek R1) runs into trouble, you can quickly switch to a backup model (say, Llama 3.3 70B) without changing your API endpoint or SDK code.

Model families Together AI supports (as of July 2026):

Family	Representative model	Price range (input/output, per M tokens)
Meta Llama 4	Scout 109B, Maverick 400B	$0.18/$0.59
Meta Llama 3.3	70B Instruct	$0.88/$0.88
DeepSeek	V3, R1	$0.14/$0.27
Alibaba Qwen	2.5 72B	$0.12/$0.18
Google Gemma	3 27B	$0.10/$0.10
Mistral	7B/8x7B	$0.10/$0.10

Actual prices follow Together AI’s live pricing on its official site; the numbers above are for reference only.

Serverless vs. Dedicated: pick based on need

Together AI offers two inference modes suited to different scales:

Serverless mode (recommended for new users and small-to-medium scale):

• No resource provisioning needed — GPUs are scheduled automatically as requests come in
• Billed by actual token usage
• May have cold-start latency (on first request or infrequent calls)
• Suited to prototyping, test environments, and personal projects

Dedicated mode (suited to high-concurrency production environments):

• Configure dedicated GPU instances (A100/H100 available)
• Billed hourly, with throughput and latency SLA guarantees
• No cold start, suited to real-time inference applications
• Suited to production scenarios with high daily call volume and latency sensitivity

For most developers, starting with Serverless is the right call — confirm your technical approach first, then evaluate whether you need Dedicated.

Integration example

from openai import OpenAI

client = OpenAI(
    api_key="your Together API key",
    base_url="https://api.together.xyz/v1"
)

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[{"role": "user", "content": "Write a quicksort in Python"}]
)

Together AI’s API is fully compatible with the OpenAI SDK — just swap in your api_key and base_url; no other code changes needed.

Where Together AI fits

Best fit:

• AI research teams that need to compare results across multiple open-source models
• Open-source-model-driven SaaS products that need to flexibly switch model versions
• Deploying fine-tuned models (Together AI offers integrated training-and-deployment services)
• Budget-constrained startups that still need production-grade inference capability

Poor fit:

• Scenarios that require closed-source models like Claude/GPT/Gemini
• Mainland China production environments (stable access requires a proxy)
• Teams that need mainland direct connect and Alipay/WeChat Pay support

If you need closed-source models plus mainland direct connect, consider SiliconFlow’s domestic open-source model inference service or Yunwu’s Claude/GPT relay. Together AI has gone further than anyone else on open-source model aggregation — the $800M in funding reflects real infrastructure investment, and its platform stability and model-update speed are top-tier among comparable products.

Information verified 2026-07-04. Pricing and model coverage change as the platform updates — check api.together.xyz for the latest figures.

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