Together AI VS Polygres
Compare Together AI VS Polygres, qual é a diferença entre Together AI e Polygres?
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Polygres turns Postgres into working memory for AI agents. Retrieve structured rows, connected relationships, semantic matches, and full-text results through one hybrid API. All without a separate vector store, graph database, or sync pipeline. Build grounded agents over the data you already have, with relational, graph, and vector context joined at the source. Use the managed cloud or self-host with our open source components. Polygres is currently free to use, just sign up!
Página de destino Polygres
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detalhes de Together AI
| Categorias | Ferramentas de Desenvolvimento AI, API de IA, Modelos de IA, Modelos de IA de Código Aberto, Grandes Modelos de Linguagem LLMs |
| Site Together AI | https://www.together.ai?utm_source=toolify |
| Hora Adicionada | Junho 22 2025 |
| Together AI Preços | -- |
detalhes de Polygres
| Categorias | Ferramentas de Desenvolvimento AI, Agência AI, AI Grafo de Conhecimento, API de IA, AI Motor de Busca para Produtividade |
| Site Polygres | https://polygres.com?utm_source=toolify |
| Hora Adicionada | Agosto 26 2026 |
| Polygres Preços | -- |
Comparação de uso
Como usar Together AI?
Users can interact with Together AI through easy-to-use APIs for serverless inference or deploy models on custom hardware via dedicated endpoints. Fine-tuning is available through simple commands or by controlling hyperparameters via API. GPU clusters can be requested for large-scale training. The platform also offers a web UI, API, or CLI to start or stop endpoints and manage services. Code execution environments are available for building and running AI development tasks.
Como usar Polygres?
Create a Polygres managed instance or install the open-source components on an existing PostgreSQL database. Connect your schema and use the SDK or retrieval API to submit a natural-language query with options such as graph traversal depth, filters, and token limits. Polygres combines relational, graph, vector, and text results into one ranked, token-ready context block that can be passed directly to an AI agent.
Compare os prós entre Together AI e Polygres
Principais recursos de Together AI
- Serverless Inference API for open-source models
- Dedicated Endpoints for custom hardware deployment
- Fine-Tuning (LoRA and full fine-tuning)
- Together Chat app for open-source AI
- Code Sandbox for AI development environments
- Code Interpreter for executing LLM-generated code
- GPU Clusters (Instant and Reserved) with NVIDIA GPUs (GB200, B200, H200, H100, A100)
- Extensive Model Library (200+ generative AI models)
- OpenAI-compatible APIs
- Accelerated Software Stack (e.g., FlashAttention-3, custom CUDA kernels)
- High-Speed Interconnects (InfiniBand, NVLink)
- Robust Management Tools (Slurm, Kubernetes)
Principais recursos de Polygres
- Unified relational, graph, vector, and full-text retrieval
- Native graph traversal over existing PostgreSQL foreign keys
- Fused hybrid search with dense HNSW, sparse, filtered, and semantic retrieval
- Token-ready context assembly for grounded AI agents
- Shortest-path, neighborhood, relationship, and multi-hop graph queries
- Managed cloud hosting and open-source self-hosting
- PostgreSQL-native data retrieval without a separate vector store or sync pipeline
Comparar casos de uso
Casos de uso para Together AI
- Accelerating AI model training and inference for enterprises (e.g., Salesforce, Zoom, InVideo)
- Building AI customer support bots that scale to high message volumes (e.g., Zomato)
- Developing production-grade AI applications by unlocking data for developers and businesses
- Creating next-generation text-to-video models (e.g., Pika)
- Building cybersecurity models (e.g., Nexusflow)
- Achieving simpler operations, improved latency, and greater cost-efficiency for AI models (e.g., Arcee AI)
- Developing custom generative AI models from scratch
- Performing multi-document analysis, codebase reasoning, and personalized tasks
- Managing complex tool-based interactions and API function calls
- Generating and debugging code with advanced LLMs
- Executing visual tasks with advanced visual reasoning and video understanding
- Data tasks such as classification and structured data extraction
Casos de uso para Polygres
- Build grounded AI agents that retrieve customer records, orders, tickets, and related history
- Investigate failed transactions by combining semantic search with order, payment, and dispute relationships
- Create user-memory systems that retrieve relevant past interactions and connected data
- Query Wikipedia-scale datasets using hybrid indexes and graph relationships
- Provide support agents with ranked, database-backed context
- Develop recommendation and discovery systems using existing PostgreSQL data
Plano diferente entre Together AI e Polygres
Together AI
Varies by model and token count
Prices are per 1 million tokens (input and output for Chat, Multimodal, Language, Code; input only for Embedding; image size/steps for Image models). Batch inference is available at an introductory 50% discount. Specific model prices range from $0.06 to $7.00 per 1M tokens depending on model size and type.
Varies by GPU type, per minute/hour
Deploy models on customizable GPU endpoints with per-minute billing. Supports various NVIDIA GPUs like RTX-6000, L40, A100, H100, H200. Prices range from $0.025/minute ($1.49/hour) for RTX-6000/L40 to $0.083/minute ($4.99/hour) for H200.
Per 1M Tokens processed
Pricing is based on model size, dataset size, and number of epochs. Supervised Fine-tuning (LoRA) ranges from $0.48 to $2.90 per 1M tokens. Full Fine-tuning ranges from $0.54 to $3.20 per 1M tokens. DPO (LoRA) ranges from $1.20 to $7.25 per 1M tokens. DPO (Full FT) ranges from $1.35 to $8.00 per 1M tokens.
Starting at $1.30/hour
State-of-the-art clusters with NVIDIA Blackwell and Hopper GPUs (H200, H100, A100) for optimal AI training and inference. H200 starts at $2.09/hr, H100 at $1.75/hr, A100 at $1.30/hr. GB200 and B200 pricing requires contact.
Per hour or per session
Together Code Sandbox is priced per vCPU ($0.0446/hour) and per GiB RAM ($0.0149/hour). Together Code Interpreter is priced per session ($0.03 for 60 minutes).
Polygres
$0 / forever
Open-source pgGraph and pgVector, runnable on any PostgreSQL instance, with community support.
$16 / month
Managed cloud database hosting with email and community Discord support. Currently free for now.
$256 / month minimum usage
Automated vertical and horizontal scaling, email and community Discord support, private Discord or Slack channel, and support hours from 8:00 AM to 5:00 PM.
$4,096 / month minimum usage
Coming soon. Includes dedicated database infrastructure, custom compliance, high availability, 24/7 support, and dedicated support and uptime SLAs.
Tráfego de Together AI
Together AI é aquele com 838.4K visitas mensais e duração média de 00:01:57.visit. Together AI tem uma página por visita de 3.15 e uma taxa de rejeição de 42.30%.
Tráfego mais recente do website
| Visitas Mensais | 838.4K |
| Duração média da visita | 00:01:57 |
| Páginas por visita | 3.15 |
| Taxa de salto | 42.30% |
Tráfego de Polygres
Polygres é aquele com 11.6K visitas mensais e duração média de 00:00:12.visit. Polygres tem uma página por visita de 1.40 e uma taxa de rejeição de 92.09%.
Tráfego mais recente do website
| Visitas Mensais | 11.6K |
| Duração média da visita | 00:00:12 |
| Páginas por visita | 1.40 |
| Taxa de salto | 92.09% |
Tráfego geográfico
Os principais 5 países/regiões para Together AI são:United States 28.99%, India 10.64%, Pakistan 6.88%, Thailand 3.39%, China 2.11%
Top 5 Países/regiões
 | 28.99% |
 | 10.64% |
 | 6.88% |
 | 3.39% |
 | 2.11% |
Tráfego geográfico
Os principais 1 países/regiões para Polygres são:United States 100.00%
Top 1 Países/regiões
| 100.00% |
Fontes de tráfego do website
As 6 principais fontes de tráfego para Together AI são:Direto 39.42%, vs_sourcesSearchOrganic 33.83%, Pesquisa paga 10.70%, vs_sourcesSearchPaid 7.11%, vs_sourcesSocialOrganic 4.46%, vs_sourcesGenAi 3.15%, E-mail 0.79%, vs_sourcesSocialPaid 0.31%, vs_sourcesDisplayAds 0.22%, vs_sourcesAffiliate 0.00%
Direto | 39.42% |
vs_sourcesSearchOrganic | 33.83% |
Pesquisa paga | 10.70% |
vs_sourcesSearchPaid | 7.11% |
vs_sourcesSocialOrganic | 4.46% |
vs_sourcesGenAi | 3.15% |
E-mail | 0.79% |
vs_sourcesSocialPaid | 0.31% |
vs_sourcesDisplayAds | 0.22% |
vs_sourcesAffiliate | 0.00% |
Fontes de tráfego do website
As 6 principais fontes de tráfego para Polygres são:Direto 57.14%, vs_sourcesSocialOrganic 21.37%, vs_sourcesSearchOrganic 13.47%, Pesquisa paga 5.27%, E-mail 2.75%, vs_sourcesGenAi 0.00%, vs_sourcesAffiliate 0.00%, vs_sourcesDisplayAds 0.00%, vs_sourcesSearchPaid 0.00%, vs_sourcesSocialPaid 0.00%
Direto | 57.14% |
vs_sourcesSocialOrganic | 21.37% |
vs_sourcesSearchOrganic | 13.47% |
Pesquisa paga | 5.27% |
E-mail | 2.75% |
vs_sourcesGenAi | 0.00% |
vs_sourcesAffiliate | 0.00% |
vs_sourcesDisplayAds | 0.00% |
vs_sourcesSearchPaid | 0.00% |
vs_sourcesSocialPaid | 0.00% |
Qual é melhor: Together AI ou Polygres?
Together AI pode ser um pouco mais popular do que Polygres. Como você pode ver, Together AI tem 838.4K visitas mensais, enquanto Polygres tem 11.6K visitas mensais. Assim, mais pessoas escolhem Together AI. Portanto, as chances são de que as pessoas recomendem Together AI mais em plataformas sociais.
Together AI tem uma duração Avg.visit de 00:01:57, enquanto Polygres tem uma duração Avg.visit de 00:00:12. Além disso, Together AI tem uma página por visita de 3.15 e uma taxa de rejeição de 42.30%. Polygres tem uma página por visita de 1.40 e uma taxa de rejeição de 92.09%.
Os principais usuários de Together AI são United States, India, Pakistan, Thailand, China, com a seguinte distribuição: 28.99%, 10.64%, 6.88%, 3.39%, 2.11%.
Os principais usuários de Polygres são United States, com a seguinte distribuição: 100.00%.
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