Published: February 5, 2026
Category: AI Infrastructure
Reading Time: 8 minutes
Quick Verdict#
Together.ai is the provider you want when you’re serious about open-source models. Not the cheapest. Not trying to be. But reliable, fast, and actually optimized for the models they host.
If you’re building production workloads on Llama, DeepSeek, or Mistral, they’re worth the premium over budget providers.
On a tighter budget? I also tested NanoGPT — cheaper option for side projects.
Pricing: The Real Numbers#
Together isn’t trying to win on price alone. They’re middle of the pack cost-wise, but the performance optimizations often make them cheaper in practice.
Text models (per 1M tokens):
| Model | Input | Output |
|---|---|---|
| Llama 4 Maverick | $0.27 | $0.85 |
| Llama 4 Scout | $0.18 | $0.59 |
| Llama 3.3 70B | $0.88 | $0.88 |
| DeepSeek-R1 | $3.00 | $7.00 |
| DeepSeek-V3.1 | $0.60 | $1.70 |
| Kimi K2.5 | $0.50 | $2.80 |
| Qwen3-Coder | $0.50 | $1.20 |
Images, video, GPU rentals too. Full catalog on their site.
What You’re Actually Paying For#
Together doesn’t just host models. They optimize them.
That “11x cheaper than GPT-4o” claim? It’s real, but with caveats. They’re comparing Llama 3.3 70B to GPT-4o. Different models. But the optimization work is legitimate. Custom speculative decoding. Optimized kernels. You get better throughput than you’d manage self-hosting on the same hardware.
The model selection is huge.
Llama family from 3B to 405B. DeepSeek variants. Qwen. Mistral. Kimi for long context. Plus vision models, image generation (FLUX, Imagen), video (Veo, Kling, Sora 2). If it’s open-source and worth using, it’s probably here.
Developer Experience#
API is OpenAI-compatible.
Drop-in replacement. Change the base URL. Swap the model name. Done. Here’s the Python:
from together import Together
client = Together(api_key="your-key")
response = client.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
messages=[{"role": "user", "content": "Explain quantum computing"}],
)
Works with LangChain, Vercel AI SDK, LlamaIndex. All of it.
Documentation exists. It’s fine. Not amazing. Not terrible. Gets you started. API reference is there. Examples work.
Reliability is solid. Claims they handle trillions of tokens in hours for some customers. Anecdotally, uptime has been good in my testing. Better than the budget providers. On par with major clouds.
vs. The Competition#
vs. OpenAI:
| Factor | Together | OpenAI |
|---|---|---|
| Price | 3-11x cheaper for equiv models | Premium |
| Model control | Open weights, self-hostable | Black box |
| Latency | Competitive, optimized | Fastest for their models |
| Best for | Cost-sensitive, open-source | Cutting-edge capabilities |
Bottom line: Unless you specifically need GPT-4o’s unique abilities, Together’s Llama/DeepSeek options save serious money.
vs. OpenRouter:
OpenRouter aggregates everything. Together is a direct provider.
| Factor | Together | OpenRouter |
|---|---|---|
| Pricing | Direct, no markup | Adds 5-10% |
| Variety | Curated (100+) | Everything (300+) |
| Reliability | Consistent | Depends on upstream |
| Best for | Production | Experimentation |
Use OpenRouter to test new models. Use Together for production workloads.
vs. Self-hosting:
Unless you’re processing billions of tokens monthly with predictable patterns, Together’s convenience wins. Their optimization usually beats naive self-hosting unless you have dedicated ML ops engineers.
The Budget Alternative: NanoGPT#
For side projects and prototypes, NanoGPT is worth a look — I tested it as a cheaper option.
Typical pricing is 20-50% cheaper than Together on smaller models. Tradeoffs exist: less reliable uptime, smaller catalog, slower inference. But for prototypes where every dollar matters, it adds up.
Use NanoGPT for experimentation. Use Together for production. That’s my setup.
When to Choose Together#
✅ Use them for:
- Production teams on open-source models
- Startups wanting OpenAI reliability without lock-in
- Enterprises needing dedicated capacity and SLAs
- Anyone prioritizing optimization over rock-bottom price
❌ Skip them if:
- You’re on an extreme hobby budget (try NanoGPT)
- You need every possible model (OpenRouter wins)
- You have massive scale and dedicated ML infrastructure
- You require proprietary models (GPT-4o, Claude)
Bottom Line#
Together.ai earns its place in your stack if you’re running production workloads on open-source models. Not the cheapest, but the most reliable way to do it at scale. The optimization work matters. The developer experience is polished. The model selection is comprehensive.
Rating: 8.5/10
Would be higher with better documentation and published SLAs. But for what it does, it’s excellent.
Related reviews:
- NanoGPT — my budget pick for side projects
- OpenRouter — for testing 300+ models
- DeepSeek-V3 — the cheapest way to run a frontier model
- LLM API pricing — full cost comparison
Disclosure: Referral link to NanoGPT included. Opinions based on technical analysis of public pricing and features.