Save up to 70% on LLM inference vs Together AI
Fleek costs 30-70% less than Together AI for most LLM workloads. The biggest savings come on frontier MoE models like DeepSeek R1 (70% cheaper) and Llama 4 Maverick (70% cheaper). Together AI has a larger model catalog and more enterprise integrations, but if cost is a factor, Fleek wins.
Together AI has been a solid choice for developers running open-source LLMs since 2022. They've built a reputation for reliability and have a good selection of models. But their per-token pricing model can get expensive at scale.
Fleek takes a different approach. Instead of charging per token, we charge per GPU-second at $0.0025. When inference runs faster—through better optimization, newer hardware, or more efficient models—your costs drop automatically. No surprises.
This comparison breaks down the real numbers across popular models, explores the architectural differences between platforms, and helps you figure out which one makes sense for your workload.
| Model | Fleek | Together AI | Savings |
|---|---|---|---|
| DeepSeek R1 (671B)MoE model, 37B active params | ~$0.67/M tokens | $2.36/M tokens | 70% |
| Llama 4 Maverick (400B)MoE model, 17B active | ~$0.25/M tokens | $0.85/M tokens | 70% |
| Llama 3.3 70B | ~$0.20/M tokens | $0.42/M tokens | 52% |
| Mixtral 8x22B | ~$0.25/M tokens | $0.90/M tokens | 70% |
| Qwen 2.5 72B | ~$0.18/M tokens | $0.45/M tokens | 60% |
| Mistral 7B | ~$0.02/M tokens | $0.05/M tokens | 60% |
Prices based on blended input/output rates. Fleek prices estimated from GPU-second costs at typical throughput. Together AI prices from their public pricing page as of January 2026.
Fleek charges $0.0025 per GPU-second. Your actual cost per token depends on how fast the model runs. We optimize inference with custom precision tuning (FP4/FP8 on Blackwell GPUs), efficient batching, and speculative decoding. Faster inference = lower cost per token.
For a model like DeepSeek R1, our optimizations squeeze out significantly more tokens per second than standard deployments. That throughput improvement directly translates to cost savings for you.
Together AI uses traditional per-token pricing with separate rates for input and output tokens. Output tokens typically cost 2-4x more than input tokens. They also offer batched inference at lower rates for non-latency-sensitive workloads.
Their infrastructure runs on a mix of cloud GPUs, and they've built solid tooling around fine-tuning and model customization. Enterprise plans include dedicated capacity and SLAs.
Both platforms support OpenAI-compatible APIs. Migration typically involves changing the base URL and API key. Most SDK code works without modification. Test with a small workload first to validate output quality.
Most teams see 30-70% savings depending on model choice. The biggest savings are on MoE models like DeepSeek R1 (70% cheaper) and Llama 4 (70% cheaper). Smaller models like Mistral 7B still save around 60%.
Yes, both platforms support OpenAI-compatible APIs. You can typically switch by changing the base URL and API key. Most existing code works without modification.
Together AI has a longer track record with published SLAs for enterprise customers. Fleek is newer but runs on enterprise-grade Blackwell infrastructure. Both target 99.9%+ uptime for production workloads.
Together AI has more mature fine-tuning tooling. Fleek supports custom model deployment at the same $0.0025/GPU-sec rate, but the fine-tuning workflow is less polished. If fine-tuning is critical, Together has the edge.
Yes, many teams use Fleek for high-volume inference on supported models and Together AI for specialized models or fine-tuning. The APIs are compatible enough to route traffic between them.
Coming soon. We're building support for any model—not just the open-source ones we showcase. Upload your fine-tuned weights or proprietary model, and we'll apply the same optimization. Same $0.0025/GPU-sec pricing, no custom model premium. Launching in the coming weeks.
For most teams running open-source LLMs, Fleek will save real money—30-70% depending on the model. The savings are most dramatic on frontier MoE models where our optimization stack really shines.
Together AI makes sense if you need enterprise compliance, fine-tuning workflows, or models we don't support yet. They've been around longer and have more enterprise features.
The practical advice: run your actual workload through both platforms and compare the bills. The numbers don't lie.
Note: Fleek is actively expanding model support with new models added regularly. Features where competitors currently have an edge may become available on Fleek over time. Our goal is universal model optimization—supporting any model from any source at the lowest possible cost.
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