Together AI vs Fireworks AI vs Modal vs Predibase: LLM Fine-Tuning Platforms for Production in 2026

Last quarter, I spent three weeks fine-tuning Llama 3.1 8B for our SmartExam AI Generator — the product builds multiple-choice exams from teacher-uploaded PDFs, and the base model kept producing distractors that were too obviously wrong (the kind of thing where the right answer is the only sentence with proper grammar). Fine-tuning was the fix. Picking the platform to do it on, though, ate more time than the actual training run.

I narrowed it to four: Together AI, Fireworks AI, Modal, and Predibase (now part of Rubrik after the June 2025 acquisition). I ran a real LoRA fine-tune on three of them with the same dataset — roughly 12,000 cleaned exam-question pairs — and shipped a production endpoint on the winner. This is the comparison I wish I'd had at the start of that month.

Why fine-tune in 2026 when RAG and prompt engineering are easier?

Honest answer: most teams shouldn't fine-tune yet. Across the six AI products I've built at Warung Digital Teknologi — SmartExam, DiabeCheck (food scanning), BizChat, DocSumm, ServiceBot, and ContentForge — only two needed fine-tuning. The rest were solved with prompt caching, structured outputs, and a decent retrieval layer.

The signal you actually need a fine-tune, from my notebook:

• Your prompt is over 4,000 tokens and you're still getting style drift.
• You need a format that JSON mode and Pydantic schema enforcement keep mangling.
• You're paying for GPT-5.4 or Claude Opus calls and your task is narrow enough that an 8B model would do it if it knew the pattern.
• Latency requirements are sub-400ms and you're stuck at 1.2s with a long system prompt.

For SmartExam, every generation request shipped a 6,200-token system prompt with style guides and example questions. Fine-tuning collapsed that to a 280-token prompt on Llama 3.1 8B. The blended cost dropped from $0.018 per exam to $0.0021, and p95 latency went from 2.4 seconds to 680ms. That's the kind of math that justifies the four-week project.

The four platforms at a glance

Together AI: the default for first-time fine-tuners

Together is where I started. It supports nearly every open model worth fine-tuning — Llama 3.1 family (8B, 70B, 405B), Mistral, Qwen 2.5, Gemma 2 — and the SDK ergonomics are the closest to the OpenAI fine-tuning API. If you've ever fine-tuned a GPT-3.5 model the old way, you'll feel at home in about 20 minutes.

My actual run: 12,400 exam pairs, average 480 tokens per pair, three epochs of LoRA on Llama 3.1 8B Instruct. Together quoted me $14.40 and the actual bill came in at $13.86. Training took 47 minutes. That's the cheapest run I did across all four platforms.

What I liked:

• One CLI command to kick off a job, with sane defaults.
• Hugging Face dataset import works without converting to their format first.
• Full fine-tuning is available if you outgrow LoRA, which Fireworks doesn't offer cleanly.

What I didn't like:

• Cold starts on the dedicated inference endpoint are 5–7 seconds on a fresh model. Fine for batch jobs, painful for user-facing endpoints unless you keep one warm at $0.85/hour idle.
• No native multi-adapter serving. If you want three SKUs of one base model, you pay for three deployments.
• The training logs are sparse — you get loss curves but not much in the way of gradient norms or learning-rate diagnostics.

Verdict: Together is the right starting point if you've never fine-tuned an open model before and want the lowest dollar risk on your first three runs. I'd budget $20–$50 for your first project including the inevitable re-runs.

Fireworks AI: pick this when inference latency is the actual bottleneck

Fireworks markets itself on speed, and in our testing the marketing was honest. After the fine-tune lands, the served model on Fireworks consistently hit p50 latency under 350ms for 200-token completions on the 8B class, which is roughly 2x what we measured on Together AI's dedicated endpoint with the same model.

The catch: fewer base models support fine-tuning. As of April 2026, Fireworks' fine-tuning menu is Llama 3.1 (8B, 70B), Mistral, and Qwen 2.5 7B/32B. If you wanted to fine-tune Gemma 2 or a more exotic base, you're out of luck on this platform.

My SmartExam re-run on Fireworks: same dataset, same hyperparameters, $19.20 actual bill, training took 38 minutes (faster than Together, probably because their H100 fleet has shorter queue times). Inference for the resulting model: p50 of 320ms, p95 of 510ms. On Together the same workload was p50 of 740ms.

The Fireworks gotcha I hit: their LoRA adapter export is locked. You can't easily download your adapter weights and serve them somewhere else. That's a vendor-lock signal worth caring about if you're a startup that might want to switch providers when funding rounds shift the math. Together does let you download adapter weights. So does Modal (you own them).

Verdict: Use Fireworks when your product is latency-sensitive and you can live with the smaller base-model menu. Avoid it if portability matters or you want to deploy the same adapter across multiple clouds.

Modal: maximum control, maximum work

Modal isn't a fine-tuning platform — it's a compute platform with great Python ergonomics. The "platform" part of fine-tuning is something you build yourself with their primitives. I went into Modal thinking the lower per-hour GPU rate would win me money. I came out thinking I'd undercount the engineering hours and not save anything on the first run.

What you actually do on Modal: you write a Python function decorated with @app.function(gpu="A100"), you mount your dataset from S3 or Modal's volume primitive, you call peft or axolotl directly, you save the adapter back to a volume, and then you write a second function that loads the adapter into vLLM and exposes it as a web endpoint.

Total wall-clock time to get my first Modal fine-tune running, starting from scratch: 11 hours, spread across a Saturday and the following Monday. By contrast, my first Together run took 90 minutes. That said, the second Modal run took me 35 minutes because I'd already built the harness.

Modal's economic case becomes real after run number five or six. The per-hour GPU rate (A100 80GB at $2.10/hour, H100 at $4.20/hour) significantly undercuts the per-token pricing of managed platforms once you're doing high-volume training or serving. For a team running 50+ fine-tunes per quarter, Modal can be 40–60% cheaper than Together. For a team running their first three, it's more expensive in total cost when you count engineering time.

The other reason to pick Modal: you want to fine-tune something exotic — vision-language models, audio models, custom architectures. The managed platforms only cover mainstream transformer LLMs. Modal will run whatever Python can run.

Predibase: the multi-adapter platform that punches above its weight

Predibase is the dark horse, and it's the platform I eventually shipped SmartExam on. Their open-source LoRAX server is the underrated piece of infrastructure of 2025 — it lets you load hundreds of LoRA adapters onto a single base-model GPU deployment and route requests to the right adapter at runtime.

For us, that mattered because SmartExam has six question styles (multiple choice, short answer, essay, fill-in-the-blank, matching, true/false) and each style benefits from its own adapter. On Together AI, that meant six dedicated endpoints at roughly $0.85/hour each — about $3,600/month just to keep them all warm. On Predibase's LoRAX deployment, all six adapters route through one base-model GPU at roughly $1.40/hour. That's $1,000/month for the same capacity. Same latency, one-third the cost.

Training cost was roughly in the middle of the pack — my SmartExam run came to $16.40 on Predibase vs. $13.86 on Together. But the post-training serving economics shift the calculation hard if you have more than two adapters. Three adapters or more, Predibase wins on monthly cost. Six adapters, it's not even close.

The Rubrik acquisition closed in June 2025 and the platform has been re-positioned as "agentic AI governance with fine-tuning underneath." In practice, the fine-tuning workflow hasn't changed much — they've added enterprise SSO, audit logs, and tighter network isolation, all of which I haven't needed but a Fortune 500 buyer would. The free tier is still generous enough to do a real evaluation.

What I didn't like:

• Their UI was clearly built for ML researchers, not application engineers. There are hyperparameter knobs I had no business touching.
• The fine-tuning queue can be slow on the shared tier — one of my runs sat for 14 minutes before starting.
• Documentation lags behind features — I had to read source on GitHub to figure out the right adapter-merge incantation.

Pricing breakdown for a real workload

Here's what I paid in practice for the SmartExam 12,400-pair LoRA fine-tune, three epochs, Llama 3.1 8B Instruct. Same dataset across all platforms, same hyperparameters within their respective constraints.

One thing the numbers don't show: my engineering time. The Modal run took me 11 hours of setup time for the first project. At Indonesian senior-engineer rates, that's about $440 of labor — almost wiping out the per-token training savings. By the third Modal run, the harness was reusable and the math flipped. Pricing for a single experiment understates Modal's cost; pricing for an ongoing program understates Together's cost.

My decision matrix

If I had to summarize the choice for somebody starting today:

• You've never fine-tuned an open model and want one production endpoint: Start on Together AI. Cheapest first run, widest base model menu, easiest SDK.
• Your bottleneck is inference latency and you don't need adapter portability: Fireworks AI. Their served-model latency is a real differentiator.
• You're running 3+ adapters on the same base model: Predibase. The LoRAX serving math destroys everyone else once you're past two adapters.
• You have an MLOps team and run 50+ training jobs a quarter: Modal. The per-hour compute is the cheapest, but you pay for the engineering harness up front.
• You need to fine-tune a non-LLM (vision, audio, multimodal) or an architecture the managed platforms don't list: Modal is your only realistic option of the four.

Production gotchas I learned the hard way

1. Evaluate before you celebrate. The training loss curves on all four platforms look indistinguishable for a successful run. The thing that differentiates a good fine-tune from a regression isn't training loss — it's a held-out evaluation set you wrote before the fine-tune started. I lost two days on SmartExam debugging "why does the model produce worse answers than the base model" before realizing my eval set was contaminated with training data.

2. The first epoch usually wins. Across the runs I logged, the second and third epochs gave marginal gains and occasionally introduced overfitting on style. Start with one epoch and add more only if eval scores keep improving.

3. Watch the chat template. Llama 3.1's chat template is not the same as Llama 3.0's, and Together's documentation example silently used the older template format for about three weeks in March 2026. If your fine-tuned model produces garbage on the first inference call, check the template before you blame the training.

4. Adapter sizes matter for serving cost. A rank-16 LoRA adapter is maybe 30 MB. A rank-128 adapter is 250 MB. On LoRAX, you can fit dozens of small adapters in GPU memory but only a handful of big ones. We standardized on rank-16 for adapter-heavy workloads, rank-64 for our most demanding format (essay generation).

5. Cold starts kill demo days. The first request to a fine-tuned endpoint after idle time will be 5–10x slower than the warm latency. Either keep a small ping job hitting your endpoint every two minutes, or build cold-start handling into your product UX. I now ship a "warming up the AI" state in the front end for the first three seconds of any new session.

FAQ

How much data do you actually need for a good fine-tune? For style and format adaptation, 500–2,000 high-quality examples is usually enough. For learning new factual content, you almost certainly want RAG instead of fine-tuning. SmartExam's 12,400-pair dataset was overkill for the style adaptation we needed — I re-ran with 2,000 samples and the eval scores were within 1.5%.

Should I fine-tune GPT-5.4 instead of an open model? OpenAI's fine-tuning is roughly 8–15x more expensive per million tokens than Together for similar tasks, and you can't host the resulting weights anywhere else. For a workload that needs hosted closed-model fine-tuning (mostly because of safety or compliance needs), it's defensible. For most production use cases I've shipped, an open 8B model fine-tuned on Together or Predibase beats the cost-quality curve.

What about Hugging Face's fine-tuning service? I didn't include it in this comparison because the managed-fine-tuning offering is still in early access as of April 2026 and pricing is opaque. I'll re-evaluate it in Q3 2026.

Does Predibase still make sense after the Rubrik acquisition? Yes, with one caveat — the enterprise tier pricing has moved up, and the free tier limits feel tighter than they were in 2024. For startups, the math still works. For Fortune 500 buyers, you'll likely be on a custom contract anyway.

Can I fine-tune for tool calling and structured outputs? All four platforms support it, but Predibase's LoRAX serving handles structured-output schema enforcement at the gateway level, which simplified our SmartExam JSON validation considerably. Together and Fireworks expect you to validate downstream.

The verdict, six months into running fine-tuned models in production

I shipped SmartExam on Predibase because the multi-adapter math saved us $2,600 a month on serving and that bought back the slightly higher training cost in the first week. If we'd been a single-adapter shop, I would have shipped on Fireworks for the latency. If I were running the first fine-tune for a different product tomorrow with a single adapter and no urgency, I'd start on Together AI again — it's the lowest-friction path to your first working fine-tune.

The pattern I keep seeing across teams I advise: people pick a platform based on training cost (because it's the visible number on the pricing page) and discover six months later that the serving economics dominate the total bill 10:1. Pick for serving, not training. Your fine-tune is going to run inference for thousands of hours and train for less than an hour.

For aggregate context: we now run four fine-tuned 8B models in production across SmartExam, DocSumm, and ServiceBot. The combined monthly bill across training and serving is under $4,200, and these three products generate around 2.3 million LLM-mediated requests a month. If I'd built the same capacity on GPT-5.4, the monthly bill would be roughly $38,000. Fine-tuning, done right, is still one of the highest-leverage cost optimizations available in 2026 — and the platform choice matters more than most teams realize.