Freesolo
PaidAgent-native post-training for production-ready small models.
By Tanmay Verma, Founder · Last verified 03 Jul 2026
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Added 7d ago
77/100Safe BetIn short
Freesolo — Agent-native post-training for production-ready small models. Best for Developers who want to fine-tune small models on their own data for specific tasks, Teams that need custom classification, extraction, routing, or reranking models, Engineers comfortable with CLI and simple configs who want agent-driven workflows. Paid pricing.
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Editorial Verdict
Best for
Developers who want to fine-tune small models on their own data for specific tasksTeams that need custom classification, extraction, routing, or reranking modelsEngineers comfortable with CLI and simple configs who want agent-driven workflowsOrganizations that want full ownership of model weights without lock-inCost-sensitive teams that prefer fixed pricing over metered token costs
Not ideal for
Beginners who prefer a GUI over CLI and config filesUsers needing frontier-level model capabilities for general reasoningTeams that cannot define a reward function or prepare example dataThose requiring large-scale pre-training rather than fine-tuningApplications demanding real-time, low-latency inference without model ownership
Freesolo’s fixed-price, agent-native approach to fine-tuning is a breath of fresh air for engineers tired of GPU roulette. It’s fast, transparent, and hands you weights you actually own. If you need a task-specific SLM and can define a reward function or provide example data, this is worth a serious look.
Last verified: July 2026
What's new in Freesolo
Checked 7 days agoAcross the latest 3 updates: 3 feature updates.
FeatureBlog·Feb 17Newest
DSPy Is All You Need
Blog post discussing DSPy for post-training agent models.
FeatureBlog·Nov 3
How We Train Models at Freesolo
Overview of training methodology used at Freesolo.
FeatureBlog·Oct 29
Building SOTA People Search
Blog on building state-of-the-art people search capability.
Viability Score
77/100
Safe BetHow likely is Freesolo to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Agent-native post-training via Claude Code, Cursor, Codex
- Supports SFT (supervised fine-tuning) from example data
- Supports GRPO reinforcement learning with custom reward functions
- Fixed-price quoting per run, not metered by tokens or GPU-hours
- Sub-10B model specialization beats frontier models on tasks
- Own weights exported in standard formats after every run
- Data encrypted in transit and at rest, never used for other training
- Reproducible runs with pinned configs and seeds, end-to-end checkpointing
- Custom kernel optimization for H100, A100, and other GPUs
- Flash CLI for local configuration and submission
- OpenAI-compatible API for deployed adapters
- Pre-flight cost estimation with --cost flag
- Training environments for defining task and reward
- Managed serving for deployed adapters
- Dashboard with training runs, environments, and models overview
About Freesolo
PaidIntermediateAPI availableCLI · API
Freesolo is a managed post-training service that lets developers fine-tune small language models via a simple agent-driven workflow. Instead of managing GPU clusters or paying per token, users describe a run in natural language to an agent like Claude Code or Cursor, and Freesolo returns a deployable model with weights owned outright. It supports supervised fine-tuning (SFT) and reinforcement learning with GRPO, emphasizing fixed pricing (one quote per run, up to 8x cheaper than metered equivalents) and fast turnaround—typically 5 hours from agent to deployable model. The platform is built for engineers who need task-specific models for classification, extraction, routing, or reranking, where a sub-10B model tuned on proprietary data often outperforms frontier API calls on specific tasks. Freesolo also optimizes custom kernels for GPUs like H100 and A100 to maximize training throughput, and offers an OpenAI-compatible API for serving. Compared to metered services like Fireworks or Prime, Flash eliminates cost uncertainty and provides full weight ownership, making it a strong alternative for production fine-tuning without vendor lock-in.
Behind the Verdict
Freesolo cuts right to the chase: describe what you want in natural language, get a fixed quote, and minutes later you have a deployable model. No GPU queues, no surprise bills. The focus on small models (sub-10B) is deliberate—these tuned SLMs can beat frontier models on narrow tasks like classification or extraction, and they’re cheap enough to retrain on the fly. The fixed pricing model is a standout: a single quote covers the whole run, unlike competitors that meter by token or GPU-hour. That means you can estimate costs upfront without complex calculations. Freesolo also owns the kernel optimization, continuously tweaking for H100 and A100 to wring out maximum throughput. The natural language interface through agents like Claude Code or Cursor is a nice touch—your favorite tool becomes the control plane. But it’s not for everyone. If you need a GUI or can’t define a reward function, you’re better off with a full-platform solution like Together AI. And if you need frontier-scale reasoning, you’ll still call GPT-4 or Claude. Freesolo’s value peaks when you have many routine queries that need a custom, owned model. For a team that lives in the CLI and values cost transparency over abstraction, it’s a sharp tool.
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Use Cases
- Fine-tune a small language model on your own dataset to classify customer support tickets with higher accuracy than zero-shot frontier models
- Train a routing model that directs queries to the appropriate backend service, reducing reliance on expensive general-purpose APIs
- Use GRPO to optimize a model for extractive QA on internal documents, scoring completions with a custom relevance metric
- Deploy a tuned adapter behind an OpenAI-compatible endpoint and integrate it into existing chat applications with minimal code changes
- Rapidly iterate on a specialized autocomplete model by pointing your agent at a dataset and getting a deployable model back in under 5 hours
Models Under the Hood
Qwen3.5-4BQwen3MistralLLaMA 3
Limitations
- Freesolo's Flash service is limited to LoRA fine-tuning; full model training or pretraining is not offered.
- Supported base models are currently a curated set (Qwen3.5, Mistral, LLaMA 3, etc.) and may not include every popular architecture.
- The platform is CLI-first, so users who prefer graphical interfaces may find the onboarding steeper.
12-month cost
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