Limitations of Together and Fireworks finetuning (and why autonomous finetuning can win)

Managed finetuning platforms are a great way to get started: you upload data, kick off a job, and get a model endpoint back. But as soon as you care about iteration speed, portability, or total cost (train + serve), you start running into architectural constraints.

We’ll use Together and Fireworks as examples of a common pattern: managed, black‑box finetuning paired with strong inference infrastructure.

Note: Platforms change quickly. If any of these capabilities have improved, that’s a good thing. The goal here is to describe the failure modes teams repeatedly hit when scaling finetuning beyond “one-off experiments.”

The core limitation: experimentation is the work

Finetuning isn’t “run one job and you’re done.” In practice, teams iterate on:

Data selection and formatting
Eval definitions and pass/fail thresholds
Training recipe (LoRA vs full, rank/alpha, LR schedule, epochs)
Safety, refusal behavior, and regression control
Deployment targets and latency budgets

If the platform you use makes iteration expensive or slow, you end up paying for it twice: once in training spend, and again in inference costs because you ship an over-sized model or a suboptimal checkpoint.

Limitations you tend to hit with managed finetuning

1) Limited portability and “serve elsewhere” paths

Many managed finetuning setups are “endpoint-first”: you get a hosted model ID that runs on their infrastructure. That’s convenient—until you want:

Your own inference stack (vLLM/TGI/TensorRT-LLM) for cost or latency reasons
Multi-provider routing or fallback
On-prem / VPC constraints
To switch providers without retraining

Portability generally requires either exportable adapters/weights or a standard checkpoint format that another serving stack can load. If exports are not available, the finetune is effectively pinned to that provider’s runtime.

If you want a deeper portability checklist, see The finetuning platform landscape.

2) Coarse controls around recipes and hyperparameters

Managed finetuning often exposes a narrow interface: dataset in, model out. That’s good for onboarding, but it can become a bottleneck when you need to systematically improve quality (or avoid regressions) through:

Different LoRA ranks / target modules
Curriculum or dataset mixing
Stronger eval-driven selection of checkpoints
Reproducibility of runs and comparisons

3) Evals are external, not first-class

Even when a platform provides “finetune + deploy,” teams still need:

Offline eval suites (held-out tasks)
Regression tests across versions
Monitoring in production

When evals are bolted on after training, teams end up guessing which run is best or relying on anecdotal spot checks. That’s the opposite of how production ML should work.

Maniac’s approach is to treat evals as the optimization objective (like CI), not as an afterthought. See Docs: Creating evaluations.

4) Iteration cost is dominated by “one job per GPU” thinking

This is the big one.

Most teams don’t need an entire GPU (or an entire GPU fleet) to run a single fine-tune at a time—especially for LoRA-style training where utilization is bursty and the bottleneck may be I/O, CPU preprocessing, or small-batch kernel launch overhead.

When every experiment is treated as a heavyweight job with dedicated hardware and human-in-the-loop tuning, you get:

Long queues (or higher costs to avoid queues)
Too few experiments → slower convergence on the best model
“Ship the first acceptable checkpoint” behavior

Why Maniac’s autonomous finetuning can be structurally cheaper

Maniac is built around the idea that iteration should be automated and that hardware should be used efficiently.

1) Autonomous finetuning reduces human-in-the-loop experimentation

Instead of asking your team to hand-tune every run, Maniac can treat finetuning as a repeated optimization process: run candidates, score them against evals, and keep the best-performing model.

This is the only way to reliably improve quality without scaling headcount or accepting slow iteration.

2) Packing finetuning jobs onto the same GPU increases utilization

At a systems level, we schedule and pack compatible jobs to avoid “one job owns one GPU” waste. Higher utilization means the same training budget buys more iteration.

More iteration → better model selection → fewer retries in production → smaller inference bill.

3) Better models often beat “optimized inference” on total cost

Inference platforms like Fireworks and Together invest heavily in serving infrastructure. That matters.

But total cost isn’t just “tokens/sec.” If autonomous finetuning helps you ship:

A smaller model that matches or beats a larger baseline
A model with higher task success rate (fewer retries / tool loops)
A model with shorter generations (less token bloat)

…then you can end up with lower effective cost per successful task, even when running on an already-optimized inference stack.

For teams evaluating inference stacks directly, see: Inference stacks compared.

Practical checklist: what to ask any finetuning provider

If you’re evaluating Together, Fireworks, or any managed finetuning provider, ask these questions:

Can I export adapters/weights? If yes, in what format?
Can I reproduce runs? Are configs and artifacts versioned?
How do evals integrate? Can I use my own eval suite to select winners?
How fast can I iterate? What’s the real queue time and cost for 20–50 runs?
Where can I serve the result? Can I move to my own inference stack later?

References and further reading

Together docs: Together AI Docs
Fireworks: Fireworks
Maniac docs: Creating evaluations
Maniac blog: The finetuning platform landscape
Maniac blog: Inference stacks compared