Linear vs Together
A detailed comparison to help you choose between Linear and Together.
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Verdict: Linear wins
Linear scores 4.5/5, edging out Together at 4.3/5. Product and engineering teams roughly 5-500 people that ship on cycles and want a fast, keyboard-driven tracker with strong GitHub/GitLab integration and light-touch AI triage.
Linear Fast issue tracking built for software teams | Together Ultra-fast scalable AI model development platform | |
|---|---|---|
| Rating | 4.5 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | usage-based |
| Starting Price | Free tier available | Pricing varies |
| Best For | Product and engineering teams roughly 5-500 people that ship on cycles and want a fast, keyboard-driven tracker with strong GitHub/GitLab integration and light-touch AI triage. | 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 tierteam featuresapi access | team featuresapi access |
| Visit Linear → | Visit Together → |
Linear
Pros
- + Speed is real and measurable: ~5.4s issue creation vs 47s for Jira Cloud in GAX's May 2026 benchmark, and the keyboard-first flow matched that direction in my own use
- + Triage Intelligence does useful work — 73/81/84% assignee/project/label accuracy in one third-party test, and duplicate bug detection cut clutter in my runs
- + GitHub/GitLab integration auto-closes issues on PR merge (94% auto-link success in the same benchmark) so board state stays honest without manual updates
- + Free plan allows unlimited members, so the whole company can watch the board at $0, and paid entry (Basic $10/user/month yearly) removes the 250-issue cap
- + Structured discounts are real: 75% off for nonprofits and university staff, a free year for students, up to 6 months of Business free for partner-affiliated startups
Cons
- - Opinionated constraints bite at scale: one project and one assignee per issue, no multi-step approval gates, limited custom field types
- - No issue-level Gantt and no native release/fix-version management — teams approximate releases with labels, a common complaint in reviews
- - Reporting is thin below Business (Insights is $16/user tier) — no custom dashboards or multi-project rollups without BI export via API
- - Guests are billed as full members and there is no read-only viewer role, which inflates cost when stakeholders need visibility
- - Cloud-only with no self-hosting, no Linux desktop app, a thinner mobile app, and 2026 forum reports of slow first loads and sync bugs on rapid edits
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, Linear or Together?▼
Linear scores higher at 4.5/5 vs 4.3/5 for Together, making it the better-rated option in our comparison.
What is the difference between Linear and Together?▼
Linear: Linear is issue tracking and project planning for software teams, built around keyboard-driven speed and a strict, opinionated workflow. Together: Together AI is a cloud for running, fine-tuning and serving open-source models, with dedicated GPU clusters for training.
Is Linear or Together more affordable?▼
Linear starts at €0/mo, Together starts at €0/mo. Check the comparison table above for current pricing.
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