Guru vs Together
A detailed comparison to help you choose between Guru and Together.
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Verdict: Together wins
Together scores 4.3/5, edging out Guru at 4.1/5. 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.
Guru AI company knowledge assistant | Together Ultra-fast scalable AI model development platform | |
|---|---|---|
| Rating | 4.1 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | usage-based |
| Starting Price | Free tier available | Pricing varies |
| Best For | Support, sales and enablement teams that want verified internal answers delivered inside Slack, Teams or the browser instead of a static wiki. | 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 featuresbrowser extensionsso | team featuresapi access |
| Visit Guru → | Visit Together → |
Guru
Pros
- + Verification stamps every card with an owner and a re-check date; in my workspace the unverified queue surfaced exactly the cards that had passed their interval.
- + AI answers arrive with citations that click through to the source card, so a support agent can confirm the basis for an answer in one hop.
- + The Chrome extension surfaces relevant cards inside the tab you are already working in — answers appeared over a web CRM record without a context switch.
- + Knowledge Agents let admins scope answers per team (HR policy agent vs IT agent) instead of running everything through one undifferentiated search.
- + Free for up to three users, which is enough to trial the whole card-and-verification workflow before paying.
Cons
- - Beyond three users everyone who reads knowledge needs a paid seat, so cost scales with headcount rather than usage.
- - AI answers are only as good as the card library — a stale card yields a confidently wrong cited answer until someone re-verifies it.
- - Published tier pricing and third-party reports of current packaging disagree, and enterprise deals are quote-based.
- - It is a knowledge layer only: no task boards, databases or Notion-style docs, so project work needs a second tool.
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, Guru or Together?▼
Together scores higher at 4.3/5 vs 4.1/5 for Guru, making it the better-rated option in our comparison.
What is the difference between Guru and Together?▼
Guru: Guru is a company knowledge platform that keeps documented answers current and verified, and serves them into Slack, the browser and other AI tools. Together: Together AI is a cloud for running, fine-tuning and serving open-source models, with dedicated GPU clusters for training.
Is Guru or Together more affordable?▼
Guru starts at €0/mo, Together starts at €0/mo. Check the comparison table above for current pricing.
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