ML Platforms
Ollama vs Together AI.
Both sit in ML Platforms, scored on the same six pillars from the same published methodology. Here is where they actually differ.
The short answer
Ollama scores higher — 83.5 against 80.6, a margin of 2.9 points. That is the overall answer, not the whole one: the pillar breakdown below is where the decision usually actually gets made.
Ollama leads on value for money, security & compliance and integrations & ecosystem; Together AI leads on capability.
Ollama, Inc.
83.5
Excellent
Run open LLMs locally with one command.
- From
- Free
- Pricing
- Open Source
- Maturity
- Established
- Founded
- 2023
Together AI
80.6
Strong
Fast inference and fine-tuning for open-source models.
- From
- Pay-as-you-go per token / per GPU-hour
- Pricing
- Usage-based
- Maturity
- Established
- Founded
- 2022
Pillar by pillar
The same six pillars and fixed weights used for every tool on the site. A lead of fewer than 5 points is not called for either side — these are evidence-backed judgements, not measurements. Read the methodology.
Capability
Together AI by 6
Value for Money
Ollama by 12
Security & Compliance
Ollama by 12
Integrations & Ecosystem
Ollama by 5
Maturity & Reliability
Level
Momentum
Level
Which one, and when
Pick Ollama if
- →cost per unit of output is the binding constraint — it leads Value for Money by 12 points.
- →compliance and data control decide it — it leads Security & Compliance by 12 points.
- →it has to fit the stack you already run — it leads Integrations & Ecosystem by 5 points.
- →Private/offline development
- →Air-gapped environments
- →Learning and experimentation
The catch
- Local hardware caps model size and speed
- No managed scaling story for production traffic
Pick Together AI if
- →the hardest end of the work is what you are buying for — it leads Capability by 6 points.
- →Shipping open models to production
- →Fine-tuning at lower cost
- →Teams avoiding vendor lock-in
The catch
- You own more of the model-selection and eval work than with a closed API
- Enterprise compliance surface still maturing vs hyperscalers
What each is good at
Ollama
- ✓Zero-cost, fully private inference on local hardware
- ✓Dead-simple UX: `ollama run` and you're chatting
- ✓OpenAI-compatible API drops into existing code
Together AI
- ✓Fast, cost-effective inference for a huge range of open models
- ✓OpenAI-compatible API makes switching low-friction
- ✓Fine-tuning and dedicated GPU capacity in one place
Other comparisons in ML Platforms
Comparing something else? Build your own side-by-side across any tools in the catalog.