Evalyze vs Together
A detailed comparison to help you choose between Evalyze and Together.
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Verdict: Together wins
Together scores 4.3/5, edging out Evalyze at 3.3/5. 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.
Evalyze AI-powered investor targeting and pitch refinement | Together Ultra-fast scalable AI model development platform | |
|---|---|---|
| Rating | 3.3 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | usage-based |
| Starting Price | Free tier available | Pricing varies |
| Best For | Pre-seed to Series B founders running a raise without a banker who want readiness scoring, deck feedback, and investor matching first and approval-gated outreach second. | 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 accessno code | team featuresapi access |
| Visit Evalyze → | Visit Together → |
Evalyze
Pros
- + Free Starter tier answers real questions without a card: 3 Startup Analyzer reports, 30 investor matches in one campaign, and the full 350-850 readiness score (evalyze.ai/pricing)
- + Two separate scoreboards — 350-850 Investor Readiness for the startup, 0-100 Deck Score for the pitch — instead of one blended vanity number
- + Outreach is approval-gated by design ('Nothing sends until you approve it') and sends from your own Gmail, protecting deliverability and keeping investor replies in your inbox
- + Pro bundles two 30-minute strategy calls per month plus a warm-intro pathway session — human help inside a software price
- + The vendor's own blog names the product's limits: model bias against non-US and non-traditional startups, no AI-generated warm intros, no guarantee of investor replies or funding
Cons
- - Pricing conflict at time of writing: the pricing page shows Pro at $49 list / $17 per month ($199 billed annually) while an Oct 19, 2026 blog post advertises $12.50 per month billed annually
- - Training-data claims are inconsistent across vendor pages — 8,000+ analyzed fundraises on the blog vs 50,000+ funded decks on the Pitch Coach page
- - Self-serve outreach is Gmail-only; LinkedIn outreach and actual warm-intro paths are locked to the Managed done-for-you tier
- - No independent hands-on reviews exist; the Sept 2025 Show HN post drew minimal engagement and all testimonials are vendor-published
- - Deck iteration is metered (3 Pitch Coach analyses per month on Pro) and file caps (10 MB free, 50 MB Pro) exclude video-heavy decks
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, Evalyze or Together?▼
Together scores higher at 4.3/5 vs 3.3/5 for Evalyze, making it the better-rated option in our comparison.
What is the difference between Evalyze and Together?▼
Evalyze: AI platform that helps startups identify and target the right investors while optimizing their pitch decks for better fundraising outcomes. Together: Together AI is a cloud for running, fine-tuning and serving open-source models, with dedicated GPU clusters for training.
Is Evalyze or Together more affordable?▼
Evalyze starts at €0/mo, Together starts at €0/mo. Check the comparison table above for current pricing.
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