What is Together AI?
Together AI provides fast and cost-effective inference API for open-source LLMs with optimized serving. Together AI offers competitive alternative to OpenAI for open models.
This AI developer tools and ecosystem term is currently being developed. Detailed content covering features, use cases, integration approaches, and selection criteria will be added soon. For immediate guidance on AI tooling strategy, contact Pertama Partners for advisory services.
Together AI provides production-grade inference for popular open-source models at 50-70% lower cost than proprietary API providers, enabling mid-market companies to deploy capable AI features within tightly constrained operational budgets. The platform's optimized serving infrastructure delivers competitive latency and throughput without requiring in-house ML engineering expertise for model deployment, scaling, load balancing, and performance management. Companies processing over 100K API calls monthly save USD 1K-5K by routing appropriate workloads through Together AI while maintaining premium proprietary provider access for specialized tasks requiring frontier model capabilities unavailable in open-source alternatives.
- Optimized inference for open models.
- Fast serving (FlashAttention, quantization).
- Cost-competitive with self-hosting.
- Llama, Mistral, Mixtral, etc.
- Good alternative to OpenAI for open models.
- Growing model selection and features.
- Compare Together AI's per-token pricing against OpenAI and Anthropic APIs because open-source model hosting typically delivers 50-70% cost savings for equivalent capability tier tasks.
- Test inference latency from your deployment region because Together AI's data center locations may add 50-150ms round-trip time compared to locally available cloud provider alternatives.
- Leverage Together AI's fine-tuning API to customize open-source models on proprietary data without managing GPU infrastructure, reducing fine-tuning costs by 60-80% versus self-hosted setups.
- Implement fallback routing to alternative providers because single-vendor API dependency creates availability risk during capacity constraints, service incidents, and planned maintenance windows.
Common Questions
Which tools are essential for AI development?
Core stack: Model hub (Hugging Face), framework (LangChain/LlamaIndex), experiment tracking (Weights & Biases/MLflow), deployment platform (depends on scale). Start simple and add tools as complexity grows.
Should we use frameworks or build custom?
Use frameworks (LangChain, LlamaIndex) for standard patterns (RAG, agents) to move faster. Build custom for novel architectures or when framework overhead outweighs benefits. Most production systems combine both.
More Questions
Consider scale, latency requirements, and team expertise. Modal/Replicate for simplicity, RunPod/Vast for cost, AWS/GCP for enterprise. Start with managed platforms, migrate to infrastructure-as-code as needs grow.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
Anyscale provides managed Ray platform for scaling Python AI workloads from laptop to cluster. Anyscale simplifies distributed ML training and serving infrastructure.
Modal provides serverless compute for AI workloads with container-based deployment and automatic scaling. Modal abstracts infrastructure complexity for AI applications.
Banana.dev provides serverless GPU infrastructure for ML inference with automatic scaling and competitive pricing. Banana simplifies production ML deployment for startups.
RunPod offers on-demand and spot GPU cloud with container deployment and marketplace for ML applications. RunPod provides cost-effective GPU access for AI workloads.
Cursor is AI-powered code editor with advanced code generation, editing, and chat features built on VS Code. Cursor represents new generation of AI-native development environments.
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