Durable vs Together
A detailed comparison to help you choose between Durable and Together.
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Verdict: Together wins
Together scores 4.3/5, edging out Durable at 3.5/5. 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.
Durable AI-powered custom software development made effortless | Together Ultra-fast scalable AI model development platform | |
|---|---|---|
| Rating | 3.5 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | usage-based |
| Starting Price | Free tier available | Pricing varies |
| Best For | Mid-market and enterprise ops/IT teams with budget and concrete cross-system automation problems (CRM-to-warehouse syncs, submittal-style workflows) who can evaluate via a sales demo and need an auditable, maintained integration layer rather than self-serve tooling. | 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 | no codeapi accessfree tier | team featuresapi access |
| Visit Durable → | Visit Together → |
Durable
Pros
- + Requirements-as-source-of-truth model: the spec is plain English with numbered R-blocks, per-requirement edit requests and version history, so non-engineers can read and change deployed logic
- + Connector docs are concrete rather than decorative — the Calendly page covers webhook subscriptions and reschedule handling, the Monday page lists actual board operations, which suggests real API integrations
- + Compliance posture is independently attestable: SOC2 Type II, Google CASA Tier 2, SSO/SAML, RBAC, audit logging and a public trust center at trust.durable.ai
- + Self-maintenance loop with human approval gates (version entries like 'v2.1.4 — API schema update') gives ops teams an audit trail instead of silent drift
- + Custom-connector path beyond the 50+ named integrations for legacy or internal systems with APIs
Cons
- - No public pricing, no free tier and no self-serve signup — the 'freemium' label in tool directories does not match how durable.ai is actually sold
- - The differentiating claims (auto-fixing APIs, 'maintained forever') are vendor-asserted with no independent long-run field reports I could find
- - No sandbox or trial to exercise the product before a sales call; my hands-on time was limited to the public site, walkthroughs and connector docs
- - Scope is backend automation and system-to-system integration, not the general 'custom software and business applications' builder that older directory listings describe
- - Name collision with durable.com (an unrelated AI website builder) makes third-party research noisy — many 'Durable reviews' cover the wrong product
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, Durable or Together?▼
Together scores higher at 4.3/5 vs 3.5/5 for Durable, making it the better-rated option in our comparison.
What is the difference between Durable and Together?▼
Durable: AI-powered platform that enables users to create custom software and business applications without traditional coding expertise. Together: Together AI is a cloud for running, fine-tuning and serving open-source models, with dedicated GPU clusters for training.
Is Durable or Together more affordable?▼
Durable starts at €0/mo, Together starts at €0/mo. Check the comparison table above for current pricing.
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