Jace vs Together
A detailed comparison to help you choose between Jace and Together.
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Verdict: Together wins
Together scores 4.3/5, edging out Jace at 3.8/5. 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.
Jace AI-powered workflow automation and task optimization platform | Together Ultra-fast scalable AI model development platform | |
|---|---|---|
| Rating | 3.8 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | usage-based |
| Starting Price | Free tier available | Pricing varies |
| Best For | Professionals and small teams who live in Gmail, want replies drafted in their voice before they open the inbox, and want arriving mail triaged and fanned out to other systems without changing email clients. | 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 | free tierapi accessno code | team featuresapi access |
| Visit Jace → | Visit Together → |
Jace
Pros
- + Drafts are waiting when you open the inbox rather than generated on request, and in my testing the calendar/attachment context made replies genuinely accurate — I edited for content, not tone
- + Automatic labeling triaged correctly on day one without a rules-building session; I trusted the sorting rather than redoing it
- + Smart Rules fan arriving mail into tickets, Slack, and documents automatically — when configured narrowly in week one, this removed real work from my queue
- + Security posture is ahead of the category: SOC 2 Type 1, CASA Tier 3, encryption in transit and at rest, and no model training on customer data
- + Account-level pricing (not per seat) with a 7-day trial and 30-day money-back guarantee — cheap to evaluate properly before committing
Cons
- - Credit metering with no published per-action costs (what does a drafted reply or rule run spend?) means a heavy month can't be forecast before you've had one
- - History depth is a plan limit — 1 year on Plus, 3 on Pro — so an assistant that seems shallow may just be on the plan that can't see far back
- - Email-only boundary: Slack is a notification destination and context source, not a stream Jace acts on; cross-channel commitments stay unmanaged
- - Undo and audit-trail behavior is not documented publicly — a trial question, not a documented answer, before you let rules act autonomously
- - Parent company Zeta Labs' flagship is now Viktor ($75M Series A led by Accel, May 2026) with Jace cross-promoted from its own homepage — confirm roadmap and support commitment before a team rollout
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, Jace or Together?▼
Together scores higher at 4.3/5 vs 3.8/5 for Jace, making it the better-rated option in our comparison.
What is the difference between Jace and Together?▼
Jace: Jace is an AI agent platform that automates repetitive tasks, optimizes workflows, and enhances productivity across various business operations. Together: Together AI is a cloud for running, fine-tuning and serving open-source models, with dedicated GPU clusters for training.
Is Jace or Together more affordable?▼
Jace starts at €0/mo, Together starts at €0/mo. Check the comparison table above for current pricing.
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