Redactable vs Together
A detailed comparison to help you choose between Redactable and Together.
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Verdict: Together wins
Together scores 4.3/5, edging out Redactable 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.
Redactable AI powered document redaction made simple | 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 | Legal, compliance and records teams with a steady monthly redaction queue who need permanent removal with certificates and audit logs they can defend afterwards. | 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 accessgdpr compliantfree tier | team featuresapi access |
| Visit Redactable → | Visit Together → |
Redactable
Pros
- + Finalizing a document removes the underlying text rather than covering it — in my testing select-all-and-copy and a text-extraction pass both came back clean after redaction
- + Redaction certificates and audit logs are included on both paid tiers, producing the artefact courts and FOIA officers ask for after the fact
- + Advanced OCR brings scanned files and image-based PDFs into scope, and pages per document are unlimited — a 1,000-page file counts once against the monthly quota
- + Flexible detection layers (AI wizard, category, search, manual) plus reusable templates and bulk find-and-replace for repeated document shapes
- + Clio integration returns a redacted duplicate into the right case folder; SOC 2, HIPAA and CJIS-aligned security claims suit regulated buyers
Cons
- - Document quotas are the binding constraint: 3 free documents total, then Starter caps at 15/month before the jump to $79/month for 150 — a cliff, not a ramp
- - Output is PDF only and text cannot be edited in place — a Capterra and a G2 reviewer both flag these as hard limits
- - AI detection is a draft, not a verdict: review is mandatory because one missed identifier is an asymmetric failure
- - Large batches are a reported weak spot — G2 reviewers cite upload limits and slow processing on big jobs
- - No air-gapped or self-hosted option, so organisations that cannot send documents to a cloud are disqualified outright
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, Redactable or Together?▼
Together scores higher at 4.3/5 vs 3.8/5 for Redactable, making it the better-rated option in our comparison.
What is the difference between Redactable and Together?▼
Redactable: Automated AI tool for fast, secure document redaction that removes sensitive information while maintaining document integrity and compliance. Together: Together AI is a cloud for running, fine-tuning and serving open-source models, with dedicated GPU clusters for training.
Is Redactable or Together more affordable?▼
Redactable starts at €0/mo, Together starts at €0/mo. Check the comparison table above for current pricing.
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