Together AI Alternatives for Fine-Tuned LLM Inference
Connor Blier
Founding GTM
Together AI Alternatives for Fine-Tuned LLM Inference
The best Together AI alternatives for fine-tuned LLM inference fall into three archetypes: model APIs that serve fine-tunes (Fireworks, Predibase), managed serverless platforms (Baseten, Modal, Replicate, Cerebrium), and self-managed GPU (Anyscale). Choose by how you need to serve LoRA adapters, your billing unit, and deployment control.
What you're actually replacing in Together AI
Together AI's appeal is "tune-and-serve in one place": you fine-tune a model and serve it from the same account. Together AI supports two fine-tuning approaches - LoRA (the default, because it is "faster, cheaper, and the right choice for most use cases") and full fine-tuning - and it bills training per token processed, with rates set by model size bracket, method, and training type. On the serving side, Together AI notes that "most teams start with serverless inference and move to dedicated endpoints at scale."
So when a VP of Engineering shops for alternatives, the real question is a fork: do you want fine-tuning as a service (someone else owns the training loop and the serving endpoint) or fine-tuning as a capability (you own the training and just need somewhere to serve the adapter efficiently)? That fork decides which of the platforms below is actually a replacement versus a partial one. If you only need the serving half, see our deeper write-up on serving fine-tuned LLMs on serverless GPU.
Four decision criteria
Serverless fine-tuned inference. Can you serve a fine-tune without reserving a dedicated endpoint? Predibase's serverless fine-tuned endpoints let you "query their fine-tuned LLMs at the same exact per-token price as base models." Fireworks serves fine-tunes serverless with "no additional costs".
Multi-LoRA serving. If you run many adapters, you want dynamic loading rather than one endpoint per variant. Fireworks offers live merge vs multi-LoRA; Predibase's LoRAX does dynamic adapter loading, tiered caching, and continuous multi-adapter batching; Baseten uses LoRA swapping with TensorRT-LLM. We cover the pattern in running multiple models on one GPU.
Pricing model. Per-token (model APIs) hides the GPU; per-second or per-GPU-hour (managed serverless) exposes it but rewards efficiency. See LLM inference cost at scale.
Deployment control. Managed endpoint you cannot touch, versus your own container and framework. This is the axis where serverless GPU platforms win.
At-a-glance comparison
| Platform | Archetype | Fine-tune training | Multi-LoRA | Billing unit |
|---|---|---|---|---|
| Together AI | Model API + tuning | Yes (LoRA/full) | Limited | Per token |
| Fireworks | Model API | Yes | Live merge / multi-LoRA | Per token |
| Predibase | Model API | Yes | LoRAX | Per token |
| Baseten | Managed serverless | Serve only | LoRA swap (TRT-LLM) | Per GPU-minute |
| Modal | Managed serverless | Yes (GPU attach) | Your code | Per second |
| Replicate | Managed serverless | Yes | Your code | Per second |
| Cerebrium | Managed serverless | Serve + train | Your code | Per second |
| Anyscale | Self-managed GPU | Yes (Ray) | Your code | Per GPU-hour |
Model APIs that serve fine-tunes (Fireworks, Predibase)
These are the closest like-for-like swap for the tune-and-serve experience. Fireworks offers LoRA fine-tuning and prices supervised and preference tuning per 1M training tokens - $0.50 for LoRA SFT up to 16B, scaling with model size - then serves the result serverless. Predibase leans hardest into serverless fine-tuned inference at base-model per-token pricing. If open-source catalog breadth matters more than tuning, our Fireworks AI alternatives guide breaks down the catalog angle.
The trade-off: you give up deployment control. You cannot pin a serving framework, bring a custom kernel, or co-locate pre/post-processing on the same box.
Managed deployment / serverless (Baseten, Modal, Replicate, Cerebrium)
This archetype replaces only the serving half - you own training - in exchange for control and efficiency. Baseten serves thousands of fine-tunes from one GPU via LoRA swapping, which is "compatible with in-flight batching" and "does not meaningfully affect latency." Modal attaches pay-as-you-go GPUs in a couple of lines and notes a fine-tuned Llama 7B can be "around 50 times" cheaper per token than GPT-3.5. Replicate serves custom models per-second.
Cerebrium sits here too, and here is where we can speak from first-hand data. In our benchmarks on 1xH100 at 256 input tokens, we measured a lowest TTFT of 123ms with vLLM and peak throughput of 460 tokens/sec with SGLang at batch size 64. For TensorRT-LLM, our Llama 3 8B deploy showed a ~10-15s cold start to load the model into GPU memory. And production economics hold up: DistilLabs cut inference costs 50% while raising accuracy from 83% to 92%. For the broader serverless comparison see our Modal alternatives, Baseten alternatives and Replicate alternatives guides.
Self-managed GPU (Anyscale)
If you already run Ray and want maximum control over both training and serving, Anyscale gives you the cluster and the primitives, billed per GPU-hour. It is the most work and the most control - the right call when your team treats infrastructure as a core competency rather than something to outsource. If you also train on the same platform, our serverless training overview is a useful contrast.
Pricing by billing unit
Per token (Together, Fireworks, Predibase): simplest to forecast, but you pay a margin on top of GPU cost and cannot optimize the hardware. Best for spiky or low-volume traffic.
Per second (Modal, Replicate, Cerebrium): you pay for exactly the compute you use, so batching and framework choice directly lower your bill.
Per GPU-hour (Anyscale, dedicated endpoints): cheapest per unit at steady high utilization, most expensive when idle.
Decision matrix by workload
Many adapters, low volume each → Predibase or Fireworks multi-LoRA: per-token serverless keeps idle cost near zero.
One or two fine-tunes, high steady volume → managed serverless on per-second billing (Cerebrium, Modal) or a dedicated endpoint; efficiency pays back.
Latency-critical → benchmark TTFT yourself; we measured 123ms on vLLM, but your tokens and network path differ.
Full control / custom kernels → Anyscale or a serverless GPU platform where you bring your own container.
Tune-and-serve in one place, minimal ops → Fireworks or Together itself if you are only leaving on price.
Frequently asked questions
- Is there a Together AI alternative that serves fine-tuned models at base-model prices?
- Yes. Predibase's serverless fine-tuned endpoints let you query fine-tuned LLMs "at the same exact per-token price as base models" using LoRAX for dynamic adapter loading, and Fireworks serves fine-tunes serverless with no additional serving cost beyond base-model rates.
- Which alternative is best for serving many LoRA adapters?
- For high adapter counts, look at multi-LoRA serving: Fireworks multi-LoRA, Predibase LoRAX (dynamic loading, tiered caching, continuous multi-adapter batching), or Baseten's LoRA swapping with TensorRT-LLM, which Baseten reports can serve thousands of fine-tunes from a single GPU without meaningfully affecting latency.
- Do I keep training on these alternatives or just serve?
- It depends on the fork. Model APIs (Fireworks, Predibase) replace both training and serving. Managed serverless and self-managed GPU platforms let you own training and focus the platform on efficient serving, which gives you more deployment control.
- How do the pricing models differ?
- Model APIs bill per token and hide the GPU. Managed serverless platforms like Modal and Cerebrium bill per second, so batching and framework choice lower your bill directly. Self-managed GPU bills per GPU-hour, cheapest at steady high utilization and most expensive when idle.
- What latency should I expect from a serverless fine-tuned endpoint?
- Benchmark it on your own tokens. In Cerebrium's own testing on 1xH100 we measured a lowest TTFT of 123ms with vLLM and peak throughput of 460 tokens/sec with SGLang at batch 64; a TensorRT-LLM Llama 3 8B engine took roughly 10-15s to load on cold start.
Get started with Cerebrium
Deploy AI models on serverless GPUs in minutes, with no infrastructure to manage. Start for free and pay only for the compute you use.
Sources
- docs.together.ai
“Together AI currently supports two fine-tuning approaches: Low-rank adapter (LoRA): Trains a small set of adapter weights on top of the frozen base model. This is the default training mode, as it's faster, cheaper, and the right choice for most use cases. Full fine-tuning: Updates every weight in the base model.”
Establishes Together AI's tune-and-serve capability and its LoRA default.
- docs.together.ai
“Together AI bills fine-tuning by the total number of tokens processed across training and validation. The per-token rate depends on three factors: the model size bracket, the training method (supervised or DPO), and the training type (LoRA or full fine-tuning).”
Together AI's per-token training billing model.
- together.ai
“Most teams start with serverless inference and move to dedicated endpoints at scale.”
Together AI's serverless-to-dedicated serving path.
- fireworks.ai
“Your fine-tuned models are served serverless on the Fireworks inference platform with no additional costs and with fast speeds like our base models.”
Fireworks serves fine-tunes serverless at no extra serving cost.
- fireworks.ai
“Supervised and preference fine tuning is priced per 1M training tokens. | Base Model | LoRA SFT | LoRA DPO | Full Param SFT | Full Param DPO | | Models up to 16B parameters | $0.50 | $1.00 | $1.00 | $2.00 | | Models 16.1B - 80B | $3.00 | $6.00 | $6.00 | $12.00 |”
Fireworks per-token fine-tuning pricing by model size.
- docs.fireworks.ai
“Fireworks offers two ways to deploy LoRA trained models. The right choice depends on how many trained variants you need to serve and your performance requirements. | | **Live merge** | **Multi-LoRA** |”
Fireworks live-merge vs multi-LoRA deployment options.
- rubrik.com
“Serverless Fine-tuned Endpoints allow users to query their fine-tuned LLMs at the same exact per-token price as base models.”
Predibase serverless fine-tuned endpoints at base-model pricing.
- rubrik.com
“LoRAX consists of three novel components: Dynamic Adapter Loading, allowing each set of fine-tuned LoRA weights to be loaded from storage just-in-time as requests come in at runtime, without blocking concurrent requests. Tiered Weight Caching, to support fast exchanging of LoRA adapters between requests, and offloading of adapter weights to CPU and disk to avoid out-of-memory errors. Continuous Multi-Adapter Batching, a fair scheduling policy for optimizing aggregate throughput of the system that extends the popular continuous batching strategy to work across multiple sets of LoRA adapters in parallel.”
Predibase LoRAX multi-LoRA serving internals.
- baseten.co
“LoRA swapping is compatible with in-flight batching. Every request in a batch can use a different LoRA. LoRA swapping does not meaningfully affect latency. Loading LoRA weights from CPU memory takes 1-2 milliseconds and only happens once per request at the start of generation.”
Baseten multi-LoRA serving via TensorRT-LLM LoRA swapping.
- modal.com
“Modal lets you attach on-demand, pay-as-you-go GPUs with just a couple lines of code.”
Modal serverless pay-as-you-go GPU model.
- modal.com
“a fine-tuned Llama 7B model can be astronomically more cost-effective (around 50 times) on a per-token basis compared to an off-the-shelf model like GPT-3.5, with comparable performance.”
Cost case for serving your own fine-tune vs a proprietary API.
- cerebrium.ai
“From our tests, it seems that vLLM is the best framework for use cases looking for the lowest TTFT of 123ms which is extremely quick if you take into consideration network latency.”
Cerebrium first-hand TTFT measurement.
- cerebrium.ai
“As for throughput use cases, it seems SGLang was a clear winner achieving a throughput of 460 tokens per second on a batch size of 64.”
Cerebrium first-hand peak throughput measurement.
- cerebrium.ai
“This code will run on every cold start and takes roughly ~10-15s to load the model into GPU memory.”
Cerebrium first-hand cold-start model-load figure.
- cerebrium.ai
“With Cerebrium, we reduced their inference costs by 50%, and increased accuracy from 83% to 92%, while keeping latency and reliability consistent at production scale.”
Cerebrium first-hand production cost case study.
