Gusto vs Together
A detailed comparison to help you choose between Gusto and Together.
How to choose
Neither tool has user reviews on ToolSpotter yet, so we don't pick a winner. Compare them on what they're built for: Gusto — Best for US small businesses with 5–50 employees that want payroll, taxes and benefits handled in one system by a non-specialist.; Together — 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..
Gusto Payroll & HR (VS) | Together Ultra-fast scalable AI model development platform | |
|---|---|---|
| Rating | 4.3 (1 review) | 4.3 (1 review) |
| Pricing Model | paid | usage-based |
| Starting Price | Pricing varies | Pricing varies |
| Best For | Best for US small businesses with 5–50 employees that want payroll, taxes and benefits handled in one system by a non-specialist. | 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 | — | team featuresapi access |
| Visit Together → |
Gusto
Pros
- + Full-service payroll with unlimited runs and automatic federal/state/local tax filings included on every paid plan (gusto.com/pricing)
- + Setup wizard and payroll worksheet made my first test run a review-and-approve job, not a calculation job — a first-time employer can configure a 5-person team in under an hour
- + Transparent per-person pricing with a published plan comparison; a 10-person team is $109/month on Simple at time of writing
- + Benefits stack (health, 401(k), HSA/FSA, workers' comp, commuter) is integrated with payroll rather than a separate portal
- + Premium includes payroll migration from a previous provider plus same-day and instant pay — the only plan where the white-glove service exists
Cons
- - US-only: no international payroll or EOR, so any overseas hire needs a second system
- - Base fee distorts value for micro-teams — $79/month effective for 5 people on Simple versus $6.49/person at 100 (peopleopsclub.com)
- - Multi-state payroll, time tracking and configurable PTO are gated to Plus at $80 + $12/person, a 91% monthly jump for a 25-person company over Simple
- - Add-ons can exceed the next tier: Plus-level features bought on Simple ran ~$214/month versus $200 for Plus itself on published rates
- - Priority support is sold separately ($30/month + $3/person per published add-on rates) and compliance alerts sit on Premium only
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
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