Together AI vs Baseten (2026): Serverless Catalog vs Owned Deployment

Who Wins Per Workload

The verdict changes with the job. This is the fast lookup.

Quick Comparison

Together AI optimizes serverless breadth and portability. Baseten optimizes dedicated control in your own infra.

Serverless vs Dedicated: The Core Split

The two providers sit on opposite sides of the same tradeoff: provisioning.

Together AI is serverless-first. You send a request to a shared, already-warm pool and pay per token. There is no GPU to reserve, no autoscaling to configure, and no idle cost. The catalog is broad: Llama 3.x, Qwen3.5, Gemma, gpt-oss, DeepSeek, plus image, audio, and embedding models. Together AI also offers on-demand dedicated endpoints and monthly reserved capacity when you outgrow shared serverless.

Baseten is dedicated-first. You deploy a model (often packaged with its Truss framework) onto your own autoscaling replicas. Baseten Model APIs do offer pay-per-token access to a curated set like DeepSeek and Llama 4 Maverick, but the platform is built around running your model with full control over hardware, scaling, and placement. Its Multi-cloud Capacity Management (MCM) treats GPUs across regions and clouds as one fungible pool, bin-packing your replicas wherever capacity exists.

The practical read: Together AI gets you to first token in seconds with zero setup. Baseten gives you a dedicated, tunable home for a model you plan to run at scale, including inside your own cloud.

Pricing: Per-Token vs Per-Hour

Together AI prices per token. Baseten prices per GPU-hour. The cheaper option flips with your traffic shape.

Together AI's serverless model is the cheapest path for bursty or low-volume traffic. You pay $1.04 per million tokens on Llama 3.3 70B and nothing when idle, and the batch API cuts that roughly in half for non-interactive jobs. Smaller models run far cheaper: gpt-oss-20B is $0.05 in / $0.20 out per million.

Baseten's dedicated H100 at about $6.50 per hour wins once a single model serves steady traffic. Billing is per minute, and scale-to-zero drops idle replicas to no cost. At 50 to 70 percent sustained utilization, dedicated typically beats per-token economics somewhere past 80 to 100 million output tokens per month for one model.

Cost on a Real Workload

Take one concrete scenario and compute it from the list prices already on this page (computed from list prices, June 2026). Serve Llama 3.3 70B at 50M output tokens per day, every day.

Speed: ATLAS vs TensorRT-LLM

Both providers ship production speculative decoding, but Together AI's is the more novel design.

Together AI's ATLAS (AdapTive-LeArning Speculator System) is a runtime-learning accelerator. Instead of a fixed draft model, it adapts the speculator to your live traffic and claims up to a 400% inference speedup over a vLLM baseline. It runs on dedicated endpoints at no extra cost, alongside FlashAttention-4 kernels. The catch is that the headline number is workload-dependent: speculative decoding pays off most on predictable, repetitive output.

Baseten builds speculative decoding on TensorRT-LLM with output-preserving guarantees, meaning the speculated tokens produce identical results to standard decoding. It also ships Baseten Embeddings Inference (BEI), which it reports as the fastest embeddings and reranker stack available, at 2x throughput and 10% lower latency versus prior solutions. For embedding-heavy retrieval pipelines, BEI is a real differentiator.

Neither speedup is code-specific. Both accelerate general token generation. Neither is built for the coding-agent apply loop; if applying model-generated code edits is the bottleneck, that is a different tool (Morph Fast Apply, ~10,500 tok/s, with published benchmarks).

Cold Starts & Autoscaling

Serverless hides cold starts; dedicated exposes them, then optimizes them.

On Together AI serverless, you call a pre-warmed shared pool, so cold starts are not your problem. The provider absorbs scaling. The tradeoff is less control over the exact hardware and placement of your requests.

On Baseten dedicated, scale-to-zero means a model can fully shut down when idle, which saves money but introduces a cold start on the next request. For large models, that wake-up can take from seconds to minutes, and you are billed per minute during it. Baseten has invested heavily in fast cold starts and autoscaling controls (including Chains for compound, multi-model workflows that report 6x better GPU usage), but the cold-start tradeoff is inherent to scale-to-zero. You tune min-replica counts to trade idle cost against latency.

Fine-Tuning

Together AI exposes fine-tuning as a priced API. Baseten treats it as infrastructure.

Together AI publishes a fine-tuning price list: LoRA up to 16B at $0.48 per 1M tokens, 17B to 69B at $1.50, and 70B to 100B at $2.90, with full fine-tuning at $1.20, $3.75, and $7.25 respectively. You fine-tune through a simple API call and deploy the result as a customer-specific model. This is the cleaner path if you want managed fine-tuning with predictable per-token cost.

Baseten runs fine-tuning and training on dedicated GPU instances rather than a fixed per-token menu, which fits teams that already own a training pipeline and want full control over hardware and process. It is more flexible and less prescriptive, but you manage more of the loop yourself.

Compliance & Deployment Modes

Both clear enterprise compliance bars; Baseten goes further on where the model runs.

Together AI is SOC 2 Type II compliant with HIPAA-aligned options and business associate agreements, which satisfies most procurement. But it is a managed cloud: your inference runs on Together's infrastructure.

Baseten Cloud is SOC 2 Type II, HIPAA, and GDPR compliant, and it splits into three modes on one inference stack: fully managed cloud, self-hosted inside your own VPC, and hybrid that runs core workloads in your cloud and bursts to Baseten on demand. For data-residency-sensitive or air-gapped deployments, Baseten is the clearer fit.

When to Use Together AI

• Serverless, multi-model traffic. One endpoint, broad catalog, $1.04/1M on Llama 3.3 70B and far less on small models. Zero provisioning, zero idle cost.
• Bursty or unpredictable load. Pay-per-token means you never over-provision GPUs for a spike that may not come.
• Managed fine-tuning. A clean per-token price list for LoRA and full fine-tuning, deployed behind an API.
• Batch jobs. The batch API cuts cost roughly in half for non-interactive workloads like evals or bulk generation.
• Cutting-edge serving research. ATLAS adaptive speculative decoding and FlashAttention-4 ship free on dedicated endpoints.

When to Use Baseten

• Steady, high-volume single model. Dedicated H100s at ~$6.50/hr with per-minute billing and scale-to-zero amortize better than per-token at scale.
• In-VPC or hybrid deployment. Self-hosted and hybrid modes run the full inference stack inside your own cloud for strict data residency.
• Multi-cloud capacity. MCM pools GPUs across clouds and regions, useful when single-region capacity is tight.
• Embedding and reranker pipelines. BEI reports the fastest embeddings inference, 2x throughput and 10% lower latency, for retrieval-heavy systems.
• Compound, multi-model workflows. Chains gives granular per-step hardware and autoscaling, reporting 6x better GPU usage.

Frequently Asked Questions

Is Together AI or Baseten cheaper?

Cheaper depends on volume and steadiness. Together AI's serverless pricing wins for variable or low traffic: Llama 3.3 70B is $1.04 per million input and output tokens with no idle cost. Baseten wins for steady high traffic because dedicated H100s at about $6.50 per hour amortize across millions of tokens, and scale-to-zero means $0 when idle. The crossover sits roughly where one model serves 80 to 100 million output tokens per month at 50 to 70 percent utilization. Below that, Together serverless is cheaper; above it, Baseten dedicated is.

Does Together AI or Baseten have faster speculative decoding?

Both ship production speculative decoding. Together AI's ATLAS is an adaptive speculator that learns from live traffic and claims up to 400% inference speedup over a vLLM baseline, free on dedicated endpoints. Baseten builds speculative decoding on TensorRT-LLM with output-preserving guarantees. Together AI's runtime-learning approach is the more novel design, but real gains depend on your traffic pattern.

Can I run inference in my own VPC?

Baseten has the stronger story. It offers three modes on one stack: managed Baseten Cloud, self-hosted inside your VPC, and Hybrid that bursts from your cloud to Baseten on demand. Together AI focuses on its managed cloud with serverless, on-demand dedicated, and monthly reserved endpoints. For strict data residency or in-VPC requirements, Baseten fits better.

Are Together AI and Baseten SOC 2 and HIPAA compliant?

Yes, both. Together AI is SOC 2 Type II compliant with HIPAA-aligned options and BAAs. Baseten Cloud is SOC 2 Type II, HIPAA, and GDPR compliant, which opens regulated healthcare and financial-services use. Either clears the bar for most enterprise procurement.

Can I export my fine-tuned weights from Together AI and Baseten?

Together AI is the more portable option. After a fine-tuning job on Together AI you can download the resulting weights and run them elsewhere, so you are not locked to the platform. Baseten is built around deploying and serving models on its stack, including inside your own VPC, rather than handing you a downloadable artifact from a managed fine-tune. If exportable, portable weights matter, Together AI fits better; if you want the trained model served under your own infrastructure controls, Baseten fits better.