Shadow vs Together
A detailed comparison to help you choose between Shadow and Together.
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Verdict: Together wins
Together scores 4.3/5, edging out Shadow at 3.7/5. 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.
Shadow AI-powered live prompts for high-stakes calls | Together Ultra-fast scalable AI model development platform | |
|---|---|---|
| Rating | 3.7 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | usage-based |
| Starting Price | Free tier available | Pricing varies |
| Best For | Reps and small sales teams on macOS who sell in live conversations and want the answer surfaced mid-call and the follow-through — email, CRM, next steps — handled from what was actually said. | 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 | api accessteam features | team featuresapi access |
| Visit Shadow → | Visit Together → |
Shadow
Pros
- + Citation-first live assist: answers arrive sourced to specific past calls, including playable clips of how a teammate handled the same objection
- + Follow-up drafts and CRM updates are ready before I hang up, with an approval gate on every email, Slack message and CRM write
- + The free tier is a genuine trial — 5 hours of calls with the full product and no credit card, enough to test it on real conversations
- + Team hygiene checks surfaced issues I would have missed, like 2 of 6 committed deals with no next meeting on the calendar (sourced to calendar and CRM)
- + 60-plus languages supported live across transcription, assist and follow-up drafts
Cons
- - Windows is still in development — macOS is the only generally available platform today
- - Team pricing is unpublished ('let's talk'), so I could not evaluate per-seat value without a sales conversation
- - Headline capabilities including forecasts, deal risk scoring and SSO/SAML are roadmap items, not shipped features
- - SOC 2 Type II is 'certification process in progress', and PII redaction plus audit logs are roadmap — a gap for compliance-sensitive rollouts
- - Very young track record: two Product Hunt launches in 2026, no on-platform reviews and no independent analyst coverage at time of checking
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, Shadow or Together?▼
Together scores higher at 4.3/5 vs 3.7/5 for Shadow, making it the better-rated option in our comparison.
What is the difference between Shadow and Together?▼
Shadow: Shadow assists sales reps during calls, preparing briefs beforehand, surfacing answers live and writing the follow-up and CRM update afterwards. Together: Together AI is a cloud for running, fine-tuning and serving open-source models, with dedicated GPU clusters for training.
Is Shadow or Together more affordable?▼
Shadow starts at €0/mo, Together starts at €0/mo. Check the comparison table above for current pricing.
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