Decision comparison
Together AI vs Cohere
Together AI and Cohere serve fundamentally different segments of the AI platform market. Together AI excels as an open-source model marketplace with cost-effective serverless inference, while Cohere delivers purpose-built enterprise NLP with native retrieval and compliance features.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
These are different kinds of product — Model Hosting Platform and Foundation Model Provider.
Quick Comparison
| Decision factor | Together AI | Cohere |
|---|---|---|
| Best For | Teams needing access to diverse open-source models with flexible serverless inference and dedicated GPU options | Enterprises requiring production-grade NLP with built-in retrieval, classification, and data privacy controls |
| Pricing Model | Pay as you go. Serverless inference is priced per 1M tokens by model, with separate input and output rates; paid chat models range from $0.04 input (Tev1 4B Experimental) to $3.00 input and $15.00 output (Kimi K3). Dedicated inference on demand: $5.49 per GPU per hour for NVIDIA HGX H100 and $8.99 for HGX B200; other GPUs and reserved capacity are quoted by sales. GPU clusters on demand from $3.99 per GPU per hour (H100). LoRA fine-tuning from $0.34 per 1M tokens (SFT), with a minimum charge per job from $4.00. Verified 2026-10-05 against https://www.together.ai/pricing. Together AI GPU Clusters on demand cost $3.99 per GPU-hour for NVIDIA HGX H100, $5.99 for HGX H200 and $8.19 for HGX B200. Code Sandbox is $0.0446 per vCPU-hour and $0.0149 per GiB RAM-hour, Code Interpreter is $0.03 per 60-minute session, and Shared Filesystem storage is $0.16 per GiB per month. Verified 2026-10-05 against https://www.together.ai/pricing. On Together AI serverless inference, Qwen3.7-Max costs $1.50 per 1M input tokens and $4.50 per 1M output tokens; gpt-oss-120B costs $0.15 input and $0.60 output. Verified 2026-10-05 against https://www.together.ai/pricing. | Trial API keys are free but rate-limited and not for production use; Production API keys are pay-as-you-go. Command R is $0.15 input and $0.60 output per 1M tokens; Command R7B is $0.0375 input and $0.15 output. Embed 5 Pro is $0.12 and Embed 5 Fast $0.08 per 1M tokens. Rerank 4 Fast is $2 and Rerank 4 Pro $2.50 per 1K searches. Parse 5 is $1.50 per 1K pages. Model Vault dedicated instances start at $3.00 per hour or $2,000 per month. North, Compass and private deployment are custom-priced. Verified 2026-10-05 against https://cohere.com/pricing. |
| Model Selection | Extensive open-source model catalog including Llama, Mistral, and community fine-tunes with rapid new model additions | Proprietary Command R family optimized for enterprise tasks including generation, embeddings, and reranking |
| Deployment Options | Serverless inference endpoints and dedicated GPU clusters starting at $3.99/hour per H100 GPU | Cloud API, private cloud deployment, and on-premises options with data residency guarantees for compliance |
| Enterprise Features | Custom fine-tuning from $3/M tokens, dedicated GPU clusters for isolation, and pay-as-you-go billing | SOC 2 compliance, data residency controls, private deployments, custom fine-tuning, and dedicated support |
| Developer Experience | Simple REST API with OpenAI-compatible endpoints and a Python SDK to start building | Well-documented API with specialized endpoints for RAG, classification, and embeddings plus Coral chat interface |
Together AI
- Best For:
- Teams needing access to diverse open-source models with flexible serverless inference and dedicated GPU options
- Pricing Model:
- Pay as you go. Serverless inference is priced per 1M tokens by model, with separate input and output rates; paid chat models range from $0.04 input (Tev1 4B Experimental) to $3.00 input and $15.00 output (Kimi K3). Dedicated inference on demand: $5.49 per GPU per hour for NVIDIA HGX H100 and $8.99 for HGX B200; other GPUs and reserved capacity are quoted by sales. GPU clusters on demand from $3.99 per GPU per hour (H100). LoRA fine-tuning from $0.34 per 1M tokens (SFT), with a minimum charge per job from $4.00. Verified 2026-10-05 against https://www.together.ai/pricing. Together AI GPU Clusters on demand cost $3.99 per GPU-hour for NVIDIA HGX H100, $5.99 for HGX H200 and $8.19 for HGX B200. Code Sandbox is $0.0446 per vCPU-hour and $0.0149 per GiB RAM-hour, Code Interpreter is $0.03 per 60-minute session, and Shared Filesystem storage is $0.16 per GiB per month. Verified 2026-10-05 against https://www.together.ai/pricing. On Together AI serverless inference, Qwen3.7-Max costs $1.50 per 1M input tokens and $4.50 per 1M output tokens; gpt-oss-120B costs $0.15 input and $0.60 output. Verified 2026-10-05 against https://www.together.ai/pricing.
- Model Selection:
- Extensive open-source model catalog including Llama, Mistral, and community fine-tunes with rapid new model additions
- Deployment Options:
- Serverless inference endpoints and dedicated GPU clusters starting at $3.99/hour per H100 GPU
- Enterprise Features:
- Custom fine-tuning from $3/M tokens, dedicated GPU clusters for isolation, and pay-as-you-go billing
- Developer Experience:
- Simple REST API with OpenAI-compatible endpoints and a Python SDK to start building
Cohere
- Best For:
- Enterprises requiring production-grade NLP with built-in retrieval, classification, and data privacy controls
- Pricing Model:
- Trial API keys are free but rate-limited and not for production use; Production API keys are pay-as-you-go. Command R is $0.15 input and $0.60 output per 1M tokens; Command R7B is $0.0375 input and $0.15 output. Embed 5 Pro is $0.12 and Embed 5 Fast $0.08 per 1M tokens. Rerank 4 Fast is $2 and Rerank 4 Pro $2.50 per 1K searches. Parse 5 is $1.50 per 1K pages. Model Vault dedicated instances start at $3.00 per hour or $2,000 per month. North, Compass and private deployment are custom-priced. Verified 2026-10-05 against https://cohere.com/pricing.
- Model Selection:
- Proprietary Command R family optimized for enterprise tasks including generation, embeddings, and reranking
- Deployment Options:
- Cloud API, private cloud deployment, and on-premises options with data residency guarantees for compliance
- Enterprise Features:
- SOC 2 compliance, data residency controls, private deployments, custom fine-tuning, and dedicated support
- Developer Experience:
- Well-documented API with specialized endpoints for RAG, classification, and embeddings plus Coral chat interface
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Together AI | Cohere |
|---|---|---|
| GitHub commits, 90d(Developer adoption) | 473 | 6 |
| GitHub stars(Developer adoption) | 11 | 406 |
| Search interest(Market interest) | 9 | 1 |
| Hacker News mentions, 90d(Community interest) | 1 | 0 |
| Hugging Face downloads(Product adoption) | 23.9k | 118.3k |
| Hugging Face likes(Product adoption) | 2.3k | 5.2k |
| npm weekly downloads(Developer adoption) | 84.9k | 336.2k |
| PyPI weekly downloads(Developer adoption) | 418.0k | 2.7M |
| Product Hunt comments(Community interest) | Not available | 2 |
| Product Hunt rating(Community interest) | Not available | 4.9/5 |
| Product Hunt reviews(Community interest) | Not available | 14 |
| Product Hunt votes(Community interest) | Not available | 132 |
As of October 26, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Together AI
October 26, 2026Package vulnerabilities
PyPI · together@3.0.0 · npm · together-ai@1.0.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Cohere
October 26, 2026Package vulnerabilities
PyPI · cohere@7.3.0 · npm · cohere-ai@8.3.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
Together AI
Cohere
Feature Comparison
| Feature | Together AI | Cohere |
|---|---|---|
| Model Access & Inference | ||
| Language Model Inference | Serverless access to 100+ open-source models including Llama 3, Mistral, and Qwen families from $0.04/M input tokens | Proprietary Command R and Command R+ models optimized for enterprise RAG and generation from $0.15/M input tokens |
| Embedding Models | Open-source embedding models available via serverless inference with support for multiple embedding architectures | Purpose-built Embed models with multilingual support in 100+ languages from $0.08/M tokens |
| Model Fine-Tuning | Fine-tuning for open-source models starting at $3/M tokens with support for LoRA and full parameter tuning | Enterprise fine-tuning for Command R models with custom training data and private model deployment options |
| Retrieval & Search | ||
| Semantic Search | Embedding-based search supported through open-source models; users build their own retrieval pipeline | Native Embed + Rerank pipeline providing end-to-end semantic search at $1 per 1,000 rerank searches |
| RAG Support | RAG workflows built by combining open-source LLMs with external vector databases and retrieval frameworks | Built-in RAG with grounded generation, inline citations, and automatic document connector integrations |
| Reranking | Community reranking models available through the model catalog for custom reranking implementations | Dedicated Rerank endpoint returning relevance scores for search results at $1 per 1,000 searches |
| Infrastructure & Deployment | ||
| Serverless Inference | Auto-scaling serverless endpoints with pay-per-token pricing and no minimum commitment required | Managed API endpoints with rate-limited free tier and production pay-as-you-go access |
| Dedicated Compute | Dedicated GPU clusters from $3.99/hour per H100 GPU providing guaranteed capacity and isolation | Private cloud deployments with dedicated infrastructure available under enterprise agreements |
| On-Premises Deployment | Not currently offered; platform operates as a cloud-only managed inference service | On-premises deployment available for enterprise customers requiring full data control and air-gapped environments |
| Developer Tools & Integration | ||
| API Compatibility | OpenAI-compatible API format allowing easy migration from OpenAI with minimal code changes | Proprietary REST API with Python, TypeScript, Java, and Go SDKs plus LangChain and LlamaIndex integrations |
| Playground & Testing | Interactive playground for testing 100+ models side-by-side with parameter tuning and prompt iteration | Coral chat playground and API dashboard for testing generation, embeddings, and classification endpoints |
| Monitoring & Observability | Usage dashboard with token consumption tracking and cost monitoring across all deployed models | Production dashboard with usage analytics, latency monitoring, and API key management controls |
| Enterprise & Compliance | ||
| Data Privacy | Standard cloud data processing with no training on customer data and secure API communication | Enterprise-grade data privacy with data residency controls, SOC 2 Type II certification, and GDPR compliance |
| Access Controls | API key-based authentication with team management features for organizing access across projects | Role-based access controls, SSO integration, and audit logging for enterprise governance requirements |
| SLA & Support | Community support and documentation with enterprise support available for dedicated GPU customers | Enterprise SLAs with dedicated support engineers, custom onboarding, and priority issue resolution |
Model Access & Inference
Language Model Inference
Embedding Models
Model Fine-Tuning
Retrieval & Search
Semantic Search
RAG Support
Reranking
Infrastructure & Deployment
Serverless Inference
Dedicated Compute
On-Premises Deployment
Developer Tools & Integration
API Compatibility
Playground & Testing
Monitoring & Observability
Enterprise & Compliance
Data Privacy
Access Controls
SLA & Support
Which approach fits
Together AI and Cohere serve fundamentally different segments of the AI platform market. Together AI excels as an open-source model marketplace with cost-effective serverless inference, while Cohere delivers purpose-built enterprise NLP with native retrieval and compliance features.
When each approach fits
Choose Together AI if:
Choose Together AI if your team prioritizes access to a broad range of open-source models and wants flexibility in model selection. Together AI's serverless inference starting at $0.04 per million input tokens makes it highly cost-effective for experimentation and production workloads that benefit from the latest open-source innovations like Llama 3, Mistral, and community fine-tunes. The OpenAI-compatible API format simplifies migration, and dedicated GPU clusters from $3.99 per GPU per hour (H100) provide guaranteed capacity when you need consistent performance.
Choose Cohere if:
Choose Cohere if your organization needs production-grade enterprise NLP with built-in retrieval augmented generation, data privacy guarantees, and compliance certifications. Cohere's proprietary Command R models are specifically optimized for enterprise use cases like RAG with grounded citations, semantic search with reranking at $1 per 1,000 searches, and text classification. The platform's on-premises deployment options, SOC 2 compliance, and data residency controls make it suitable for regulated industries requiring strict data governance.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
How does Together AI pricing compare to Cohere for text generation?
Together AI's pricing table lists serverless inference prices by model and token type. For example, LFM2.5-8B-A1B is listed at $0.03 per 1M input tokens and $0.12 per 1M output tokens. Our research does not include Cohere pricing, so it cannot support a like-for-like cost comparison for text generation.
Can I use open-source models on Cohere or proprietary models on Together AI?
Cohere focuses exclusively on its proprietary Command R model family and does not host open-source models. Together AI specializes in open-source models and does not offer proprietary alternatives. If you need access to specific open-source architectures like Llama 3 or Mistral, Together AI is the clear choice. If you want Cohere's enterprise-optimized models with built-in RAG and classification, those are only available through the Cohere platform. Some teams use both platforms for different use cases.
Which platform is better for building RAG applications?
Cohere offers a more integrated RAG experience with its built-in Embed models from $0.08 per million tokens, Rerank endpoint from $2 per 1,000 searches, and Command R's grounded generation that automatically produces inline citations. Together AI supports RAG workflows but requires combining separate components: an open-source LLM for generation, an embedding model for indexing, and external vector databases like Pinecone or Weaviate. Cohere's approach reduces development complexity, while Together AI provides more flexibility in choosing individual components.
What are the key differences in enterprise and compliance capabilities?
Cohere is purpose-built for enterprise deployment with SOC 2 Type II certification, GDPR compliance, data residency controls allowing you to specify where your data is processed, and on-premises deployment options for air-gapped environments. Together AI provides standard cloud security practices and does not train on customer data, but currently lacks on-premises deployment and formal compliance certifications comparable to Cohere. For regulated industries like healthcare and finance that demand zero risk tolerance on data handling, Cohere's enterprise features provide significantly stronger governance capabilities.