Together AI is the Swiss Army knife for the open-source ecosystem. While providers like Groq chase raw speed and DeepInfra races to the bottom on price, Together builds a "safe" middle ground: enterprise-grade reliability, compliance (SOC2/HIPAA), and a massive model hub that includes everything from Llama 3.3 to the latest DeepSeek R1.
The pricing is competitive but not the absolute floor. Running Llama 3.3 70B costs $0.88 per million tokens (input and output). For a RAG application processing 1,000 queries a day with 4k context each, you're looking at roughly $100/month. That’s significantly cheaper than GPT-4o but about 2x the cost of running the same model on bare-bones providers like DeepInfra. You pay the premium for stability and features like "Zero Data Retention" (ZDR), which guarantees your inputs aren't stored for training—a critical requirement for fintech and healthcare apps.
Technically, the platform is solid. The inference engine, built on their own FlashAttention research, delivers sub-100ms time-to-first-token (TTFT) for most models. It’s not the instant flash of Groq’s LPU, but it’s fast enough for real-time chat. The real value, however, lies beyond simple inference. Together is one of the few platforms that makes fine-tuning accessible. You can fine-tune a Llama 8B model with LoRA for under $10 in compute credits, hosted on the same infrastructure you use for inference. This unifies the lifecycle: prototype with the generic API, fine-tune on your data, and deploy without managing a single GPU.
The platform does have rough edges. The sheer number of models (200+) means some older ones feel neglected or have slower cold starts. Rate limits on the lower tiers can be surprisingly tight, forcing you to upgrade or commit to monthly spend earlier than you might like. And while they support the OpenAI SDK, minor parameter differences (like stop sequences) can occasionally trip up drop-in replacements.
Skip Together AI if you are a hobbyist just wanting the absolute cheapest tokens; the budget providers will save you 50%. Use it if you are building a product that requires the flexibility of open-source models, the safety of enterprise compliance, and the option to fine-tune later without switching platforms.
Pricing
The free tier gives you $5 in credits, which is enough to test Llama 3.3 70B for about 5M tokens—generous for testing but not for production. The real cost cliff is the lack of a middle tier; you go from pay-as-you-go with restrictive rate limits (often 60 requests/min on popular models) to needing 'Scale' plans for higher throughput. While $0.88/1M for Llama 3.3 70B is fair, note that DeepSeek R1 is priced at a premium ($3 input/$7 output), which is significantly higher than competitors like DeepInfra ($0.55/$2.19). You are paying for the reliability wrapper, not just the raw compute.
Technical Verdict
The API is fully OpenAI-compatible, meaning migration is usually just a URL and key change. The Python SDK (together) is decent but largely redundant if you already use openai. Latency is reliable (~0.5s TTFT for 70B models), though not instant. The standout technical feature is the fine-tuning API, which abstracts away the complexity of GPU orchestration for LoRA jobs.
Quick Start
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.together.xyz/v1",
api_key="YOUR_TOGETHER_API_KEY"
)
response = client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
messages=[{"role": "user", "content": "Explain quantum entanglement in one sentence."}]
)
print(response.choices[0].message.content)Watch Out
- Rate limits on the pay-as-you-go tier are per-minute and can be surprisingly low for bursty workloads.
- DeepSeek R1 pricing is significantly higher than budget providers, charging a premium for the hosted experience.
- The 'free' endpoints for specific models have very aggressive rate limits (often 1 request/second) and shouldn't be relied on for apps.
- Zero Data Retention (ZDR) must be explicitly enabled in settings; it is not on by default.