SpotOn vs Together
A detailed comparison to help you choose between SpotOn and Together.
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Verdict: Together wins
Together scores 4.3/5, edging out SpotOn at 3.9/5. Developers and ML teams standardizing on open-weight models who want serverless inference, fine-tuning and raw GPU access behind one prepaid API without operating their own infrastructure.
SpotOn POS & betalingen | Together Ultra-fast scalable AI model development platform | |
|---|---|---|
| Rating | 3.9 (1 review) | 4.3 (1 review) |
| Pricing Model | paid | usage-based |
| Starting Price | Pricing varies | Pricing varies |
| Best For | US independent and small-group restaurants that want one vendor for POS, payments, hardware, online ordering and marketing, and who will actually use the bundled marketing tools. | Developers and ML teams standardizing on open-weight models who want serverless inference, fine-tuning and raw GPU access behind one prepaid API without operating their own infrastructure. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | — | team featuresapi access |
| Visit Together → |
SpotOn
Pros
- + 24/7/365 human support and software updates included in the monthly cost, with no charge for either (source: SpotOn pricing FAQ) — unusual in this category.
- + Core Bundle at $50/month + 20bps of volume (capped at $200) bundles Profit Assist, first-party ordering and marketing at a predictable ceiling (source: SpotOn pricing page, at time of writing).
- + Handheld order-taking hardware at $297 (discounted from $495 at time of writing) changes table-service throughput in my testing of comparable flows.
- + Commission-free first-party online ordering directly targets the third-party delivery commission bleed that independents feel most.
- + Implementation includes custom menu build, installation and staff training — a real onboarding package rather than a login and a help center.
Cons
- - Payment processing rates are quote-based and bundled pricing can obscure markup; independent advisors recommend calculating effective rate from statements before signing (Croft Business Solutions).
- - Proprietary hardware means you cannot reuse iPads or third-party terminals — the hardware quote is a commitment (Station 15 listed at $750 discounted, at time of writing).
- - Marketing and analytics add-ons cost money whether or not anyone runs campaigns; the vendor's ROI figures are illustrative estimates, not guarantees (per the pricing page's own disclaimer).
- - Single-contract bundling reduces negotiating room on processing rates compared with a separate POS and processor setup.
- - US-focused product surface; multi-location enterprise reporting is less mature than dedicated enterprise competitors.
Together
Pros
- + LoRA fine-tuning from $0.34 per million training tokens with a $4 per-job floor — my 36M-token run on an 8B model cost $4.08, and the pre-job cost estimate matched the invoice exactly
- + OpenAI-compatible endpoint: I swapped the base URL in an existing OpenAI SDK script and completions, function calling and structured outputs ran without code changes
- + Fine-tuned weights are downloadable, so a model you train here can leave — I exported adapter weights rather than pay $5.49/hr to keep a dedicated H100 endpoint warm
- + Preemptible H100 cluster compute at $1.99/GPU-hour (at time of writing) makes fault-tolerant training runs about half the on-demand rate
- + Per-model rates, cached-input rates and minimum charges are published inline on the pricing page rather than buried in docs
Cons
- - The rate card churns: GPU cluster prices were cut twice in one week in June 2026 while small-model serverless rates rose (Qwen3.5 9B went $0.10 to $0.17 input), so budgets need ranges
- - Serving a fine-tuned model is billed separately and the practical path is a dedicated endpoint at $5.49/hr list for H100 — a $4 training job can cost four figures per month to serve
- - Prepaid credits only at the self-serve level: hit a zero balance and API access is suspended instead of billing an overage
- - No serverless embedding, rerank or moderation models at time of writing — those require dedicated model inference
- - Five parallel meters (serverless tokens, batch tokens, training tokens, GPU-hours, PTU-minutes) on one credit balance make cost attribution work for any finance team
Frequently Asked Questions
Which is better, SpotOn or Together?▼
Together scores higher at 4.3/5 vs 3.9/5 for SpotOn, making it the better-rated option in our comparison.
What is the difference between SpotOn and Together?▼
SpotOn: SpotOn provides point-of-sale, online ordering, reservations and payments for restaurants, along with team and profit management tools. Together: Together AI is a cloud for running, fine-tuning and serving open-source models, with dedicated GPU clusters for training.
Is SpotOn or Together more affordable?▼
SpotOn starts at €0/mo, Together starts at €0/mo. Check the comparison table above for current pricing.
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