The Market Gap
The training-as-a-service landscape can be organized into five buckets. The gap is obvious once you look at who actually provides end-to-end customization versus who expects you to do it yourself.
| Bucket | Description | Cost | Gap |
|---|---|---|---|
| A - Do nothing | No AI adoption; use existing workflows | $0 | Missed competitive opportunity as peers adopt AI |
| B - Self-serve APIs | OpenAI GPT-4.1, Together AI, Fireworks AI | $0.48–$25/1M training tokens | Requires ML skills; limited customization depth; no compounding value |
| C - Fine-tune yourself | Internal ML team using managed platforms | $200k–$500k/yr per engineer | Talent gap; 6–18 month lead time; high management overhead |
| D - Hire consultants | Accenture, Deloitte, IBM | ~$280/hr (Accenture Systems Engineer Level III) | Expensive; every engagement starts from scratch; no compound value |
| E - Connito | Full-service, decentralized, compounding library | Above B, well below D | The gap Connito fills |
The key insight: smaller and mid-size companies need real model customization, but the platforms that are affordable expect ML skills most companies don't have, and the vendors who will do it end-to-end charge like enterprise consulting firms. Nobody is delivering managed, done-for-you customization at B-style pricing.
Connito's position: deliver the outcomes customers expect from C/D-style solutions, but price it closer to B economics by replacing expensive consulting labor with the Bittensor talent pool. The compute is already transparently metered in the market. Our pricing sits above the self-serve floor while staying well below the consulting ceiling — and the spread is our margin.