Together vs Fireworks vs Replicate Fine-Tuning (2026): The Open-Weight Comparison

These platforms started from very different problems and the product decisions reflect those starting points.

**Together AI** (https://together.ai/) positions itself as the open-weight model platform — broadest catalog, cheapest training, cheapest inference, and the deepest method support. Together built its own training infrastructure on H100 clusters and serves on a custom inference engine that consistently lands in the top tier of throughput and latency benchmarks for open-weight models. The fine-tune offering includes LoRA, full fine-tuning, DPO, and continued pre-training — the broadest method support of the three. If you want to do something unusual (DPO on Llama 4, continued pre-training on a base model with your domain corpus), Together is often the only hosted platform that supports it.

**Fireworks AI** (https://fireworks.ai/) positions itself as the production-serving platform that happens to offer fine-tuning. Its core product is the Fireworks inference stack — a custom GPU serving infrastructure optimized for low latency at high concurrency — and FireOptimizer fine-tuning is built to plug directly into that serving stack. The differentiator is multi-LoRA serving: Fireworks can serve hundreds of different LoRA adapters on the same base model with sub-millisecond switching, which is the right architecture for SaaS products that want per-customer fine-tuned models without per-customer GPU costs.

**Replicate** (https://replicate.com/) positions itself as the creator and ML researcher's friendliest platform. Its product surface — a clean REST API, typed Python client, a public catalog of community-built models — is optimized for developer flow. Fine-tuning on Replicate works the same way as everything else: you call a training endpoint, you get back a model URL, you call that URL like any other Replicate model. The pricing is per-second-of-GPU-runtime rather than per-token, which makes it the right pick for unusual training shapes (image LoRAs, audio fine-tunes, anything that isn't standard text LLM SFT) but more expensive at scale for standard text fine-tunes.

Per-token training pricing varies meaningfully across the three platforms, and the variance is large enough to drive vendor choice for high-volume training programs.

**Together AI Llama 4 70B LoRA** is approximately $1.20 per 1M training tokens as of June 2026. A typical 5,000-example × 1,500-token × 3-epoch run (22.5M tokens) costs approximately $27.

**Fireworks AI Llama 4 70B LoRA** is approximately $1.80 per 1M training tokens. Same run: approximately $41.

**Replicate Llama 4 70B LoRA** is billed by GPU-hour, typically running on a single A100 80GB or H100 80GB at $1.80-2.50/hour. The same 22.5M-token job takes ~12-18 hours on a single GPU, costing approximately $24-45 — comparable to Together at the low end but with higher variance.

**Smaller models follow similar ratios**: Llama 4 8B LoRA on Together is approximately $0.40/1M tokens (so a 22.5M-token job is ~$9), Fireworks is ~$0.60, Replicate is GPU-hour billed at $5-15 total. Larger models (Llama 4 405B) move proportionally — Together ~$6/1M, others scale up correspondingly.

**Full fine-tuning** on Together (where supported) is approximately 3-5x the LoRA price. Fireworks supports full fine-tuning on select smaller models at roughly the same multiplier. Replicate full fine-tuning is GPU-hour billed and depends on the cluster size used.

Training cost is often a one-time investment; serving cost is forever. The three platforms differ substantially in serving model.

**Together AI serverless inference** charges per token at published rates. For a Llama 4 70B fine-tune, it is approximately $0.90 input and $0.90 output per 1M tokens — close to the base model rate. There is no per-hour serving floor; you pay only for the tokens you generate. Multi-LoRA serving is available on dedicated endpoints if you need adapter hot-swapping at low latency.

**Fireworks AI serverless inference** is similarly priced (~$0.90 in/out per 1M tokens for Llama 4 70B) and also has no per-hour floor on serverless. The differentiator is multi-LoRA: dozens or hundreds of adapters served on shared base-model GPUs with sub-millisecond switch time. For SaaS products with many fine-tuned variants, this can be a 10-100x serving cost reduction versus deploying each adapter as a separate endpoint.

**Replicate inference** is billed per second of GPU runtime, not per token. A request to a Llama 4 70B fine-tune typically takes 0.5-2 seconds on the assigned GPU, billed at the per-second rate of that GPU (~$0.001-0.002 per second for an A100). Cold starts on Replicate add 30-60 seconds of GPU time if the model is not warm — this is the major cost trap on Replicate inference for low-traffic deployments. Always-on warm pools are available but reintroduce a per-hour serving floor.

**The honest TCO**: for high-traffic production deployments of standard text fine-tunes, Together and Fireworks come out clearly ahead on serving. For low-traffic or batch-style deployments where occasional 30-60-second cold starts are acceptable, Replicate can be cheaper. For multi-tenant SaaS with many fine-tunes per customer, Fireworks multi-LoRA is the architectural win.

What you can train differs significantly across the three platforms.

**Together AI** supports the broadest catalog: Llama 4 (8B, 70B, 405B), Mistral 7B and 8x22B, Qwen 2.5 and Qwen 3 family, DeepSeek-V3, DeepSeek-R1 (with caveats — the long reasoning traces are expensive to train against), Yi 1.5, Falcon 3, and dozens more. Method support is the deepest: LoRA, full fine-tuning, DPO, continued pre-training (rare and expensive but available), and KTO are all GA.

**Fireworks AI** supports a tighter catalog focused on production-grade models: Llama 4 (8B, 70B), Mistral 7B and 8x22B, Qwen 2.5 family, DeepSeek-V3. Method support is LoRA (FireOptimizer is the proprietary LoRA-and-distillation method) and full fine-tuning on select smaller models. No DPO or continued pre-training as of June 2026.

**Replicate** supports Llama 4 (8B, 70B), Mistral 7B, Qwen 2.5 family on the text side, plus a wide catalog of image models (SDXL, Flux, etc.) and audio models (XTTS, MusicGen, Whisper variants) that can be fine-tuned. Method support is LoRA primarily for text; full fine-tuning on smaller models is available but priced per GPU-hour. The image and audio fine-tuning is a real differentiator — neither Together nor Fireworks offers comparable depth in multimodal training.

Workflow ergonomics shape day-to-day productivity. The three platforms differ in API style.

**Together AI** offers an OpenAI-compatible REST API for both training and inference. You point your existing OpenAI SDK code at the Together base URL with a Together API key, and most workflows just work. Fine-tune submission is a straightforward POST with a jsonl file reference and method config. Job status, model deployment, and adapter download are all REST endpoints. The CLI (`together`) provides ergonomic wrappers for common workflows.

**Fireworks AI** also offers an OpenAI-compatible inference API and a similar fine-tune submission flow via REST. The Fireworks dashboard provides more visualization than Together's (training loss curves, eval metrics, deployment status) and the multi-LoRA serving setup has dedicated UX. Python client (`fireworks-ai`) is well-typed and tracks the API closely.

**Replicate** uses its native API style: training is a job submission with typed inputs, and the resulting model gets a URL slug that becomes part of the Replicate model catalog (private or public). Inference is the standard Replicate prediction API. The Python client (`replicate`) is the smoothest of the three for one-off scripts and notebooks; the typed inputs feel more like calling a normal function than an HTTP API. The catch: switching from OpenAI to Replicate inference requires more code changes than switching to Together or Fireworks.

Mapping common situations to the right platform.

**You want the lowest training and inference cost on a standard text LLM fine-tune** → Together AI. Lowest per-token training, lowest per-token inference, broadest model catalog, deepest method support.

**You are building a SaaS with many customer-specific fine-tunes** → Fireworks AI. Multi-LoRA serving with sub-millisecond adapter switch is the architectural fit; serving cost per fine-tune approaches zero for the marginal adapter.

**You need DPO, continued pre-training, or any uncommon method** → Together AI. Method support is the deepest of the three.

**You are fine-tuning image, audio, or multimodal models** → Replicate. The catalog and per-second GPU billing model are the right fit.

**You want the fastest dev iteration for one-off training experiments** → Replicate. Typed Python client and simple REST API have the smoothest one-script workflow.

**You want OpenAI SDK compatibility with no code changes** → Together AI or Fireworks AI. Both expose OpenAI-compatible APIs; pick by training/serving cost or multi-LoRA needs.

**You are price-sensitive on serving but okay with cold starts** → Replicate. The per-second-of-GPU-runtime billing means very low-traffic deployments pay almost nothing; high-traffic deployments lose to Together and Fireworks.

Three pitfalls show up repeatedly when teams pick the wrong platform.

**Pitfall 1: Choosing Replicate for high-traffic text production.** Per-second GPU billing on Replicate is generous for low-traffic and one-off use cases but becomes expensive at 10+ requests per second of sustained load. At that point, Together or Fireworks' per-token serverless pricing wins on TCO by 2-5x. Switching from Replicate to Together after launch is real engineering effort because the inference API surface differs; pick the right platform up front.

**Pitfall 2: Choosing Together when you actually need multi-LoRA.** If your product serves many different fine-tunes (per-tenant, per-vertical, per-customer), serving each as a dedicated Together endpoint costs more than necessary. Fireworks' multi-LoRA architecture serves dozens or hundreds of adapters on shared base-model GPUs. Together does support multi-LoRA on dedicated endpoints, but Fireworks' implementation is more mature in 2026.

**Pitfall 3: Underestimating data format conversion cost.** All three platforms accept jsonl with chat-style messages but the exact schema differs in small ways (system prompt placement, tool-call format, role names). Maintain a neutral internal format and run per-platform translation at submission — switching platforms after launch should not require a data-pipeline rewrite.

For most teams in 2026, the decision boils down to a few questions: what is your model? what is your method? what is your serving shape?

If your model is in the Llama 4 / Mistral / Qwen catalog (the vast majority of open-weight fine-tunes), method is standard LoRA SFT (also the vast majority), and serving shape is a single fine-tune behind a chat or completion API, **Together AI wins on price and method depth**.

If your serving shape is multi-tenant with many fine-tunes per customer, **Fireworks AI wins on architecture** — multi-LoRA serving is the right pattern and Fireworks' implementation is the deepest.

If your model is multimodal (image/audio/video) or your serving shape is low-traffic / batch / experimental, **Replicate wins on workflow** — per-second GPU billing and the typed-input API are the right fit for those patterns.

Use the fine-tuning cost by model calculator to model the specific numbers for your dataset size and serving QPS. The 2-3x cost differences between platforms compound quickly at production scale.