Together AI Review 2026

Quick Verdict

Together AI has carved out the most compelling inference niche in the GPU cloud market. Where RunPod gives you cheap raw compute and Lambda Labs gives you clean H100 access, Together AI gives you optimised inference stacks — FlashAttention, speculative decoding, continuous batching — pre-configured and ready to serve. If you are building a production LLM API on Llama 3, Mistral, or Mixtral, Together AI's H100 dedicated endpoints at $1.49/h with their inference engines will serve more tokens per dollar than any comparable cloud. The managed fine-tuning pipeline is also genuinely good. Where Together AI is not the right fit: raw multi-node training, consumer GPU experiments, or EU-sovereign requirements.

Best For

• High-throughput inference
• Open-source LLM serving
• Llama / Mistral fine-tuning
• Production AI APIs

Together AI vs RunPod Serverless — Inference Performance

RunPod Serverless is the most common alternative for teams that want serverless inference without paying per GPU-hour. You deploy a Docker container, RunPod scales it to zero when idle, and you pay per compute unit consumed. This works well and the cold-start overhead (5-15 seconds for large models) is manageable for many use cases. The economics at low traffic are excellent — zero idle cost.

Together AI's advantage is throughput efficiency. Their custom inference stack (built on FlashAttention-3, speculative decoding, and continuous batching) extracts significantly more tokens per GPU-second than a standard vLLM container running on RunPod Serverless. In benchmarks, Together AI's Llama 3.1 70B serving achieves roughly 3× higher throughput than equivalent RunPod vLLM deployments. At high steady-state request volumes, this difference directly translates to per-token cost savings.

The choice comes down to traffic pattern. Low-volume, bursty inference: RunPod Serverless or Together AI's own per-token API will both serve you well at near-zero idle cost. High-volume steady-state inference: Together AI's dedicated H100 endpoint at $1.49/h is likely cheaper per token than RunPod Serverless at equivalent request rate, due to throughput efficiency. For custom models or unusual frameworks, RunPod's Docker flexibility wins.

Together AI vs Replicate — Model Marketplace

Replicate is often cited alongside Together AI in conversations about managed inference platforms. Both offer hosted open-source model endpoints with a pay-per-use model, and both have curated model libraries. Replicate's model marketplace is broader — any developer can publish a model on Replicate using Cog — which makes it excellent for discovery and prototyping. You can run Stable Diffusion, Whisper, ControlNet, and thousands of niche models via a single API.

Together AI's focus is narrower and deeper: text-generation LLMs at high performance. Together AI does not try to serve every model type; it focuses on running Llama, Mistral, Mixtral, DBRX, and similar models faster than anyone else. For a production text API, Together AI's throughput and pricing are hard to beat. For diverse model types or prototyping with a wide model catalogue, Replicate's marketplace is more useful.

Price comparison at scale: Together AI's per-token API for Llama 3.1 70B is roughly $0.88/M input tokens and $0.88/M output tokens (May 2026). Replicate's equivalent is comparable but with more variability by model and GPU. For large-volume production inference on standard LLMs, Together AI typically wins on price at >1B tokens/month. Below that, the difference is minor and developer experience should drive the choice.

Detailed Feature Tour

Inference engines: Together AI runs a proprietary inference stack with FlashAttention-3, speculative decoding, and tensor parallelism tuned for each model. The result is typically 2-4× higher token/s throughput versus vanilla vLLM on the same hardware. This is the platform's core differentiator and the reason dedicated endpoints at $1.49/h often produce more value than cheaper bare-GPU alternatives.

Model library: 100+ open-source models available on the serverless API including Llama 3/3.1/3.2, Mistral, Mixtral, Command-R, DBRX, and various fine-tuned derivatives. All models are served from Together AI's optimised runtime — you do not configure vLLM parameters yourself.

Fine-tuning: Together AI's managed fine-tuning accepts JSONL datasets, supports LoRA and full fine-tuning, and integrates with common ML frameworks. Training runs are charged at GPU-hours; costs depend on model size and dataset size. Fine-tuned models deploy immediately to dedicated endpoints.

Dedicated endpoints: H100 SXM at $1.49/h, H200 SXM at $2.49/h, A100 80 GB at $1.05/h. Dedicated endpoints guarantee GPU allocation — no cold starts, consistent latency. Suitable for production APIs with SLA requirements.

Regions: US and EU availability. EU inference is useful for latency-sensitive European applications; sovereignty guarantees are less strong than Nebius for GDPR-strict requirements.

Who Should Use Together AI?

Together AI is ideal for engineering teams building production AI APIs on open-source LLMs, data teams doing high-volume inference for document processing or classification, and ML practitioners who want managed fine-tuning without configuring training infrastructure. If your core workload is "serve Llama 3.1 70B at scale," Together AI is the strongest specialist option available.

Who Should NOT Use Together AI?

Together AI is not the right choice for teams that need raw multi-node training clusters (use Crusoe or Lambda Labs), hobbyists experimenting with consumer GPUs (use RunPod or TensorDock), EU-sovereign GDPR-bound workloads (use Nebius), or diverse model types beyond LLMs (use Replicate or RunPod Serverless with custom Docker).

Final Verdict

Together AI earns a 4.4/5.0. The inference performance advantage is real and measurable, the managed fine-tuning pipeline is polished, and the pay-per-token serverless option provides zero-idle-cost serving for low-traffic endpoints. The main limitations are the narrower GPU variety and the focus on inference over training. For teams whose primary GPU workload is LLM inference at scale, Together AI is our top recommendation.