Together AI Platform

Provides on-demand API access to 100+ pre-optimized open-source language models (Llama, Mistral, Qwen, etc.) without requiring users to manage infrastructure. Models are containerized and deployed across Together's distributed GPU cluster with automatic scaling, request routing, and load balancing. Users submit inference requests via REST/gRPC endpoints and receive responses within milliseconds, with billing based on tokens consumed rather than reserved capacity.

Enables users to fine-tune open-source base models on proprietary datasets using Together's managed training infrastructure. The platform handles data preprocessing, distributed training across multiple GPUs, checkpoint management, and model versioning. Users upload training data (JSONL format), specify hyperparameters, and Together orchestrates the training job using PyTorch distributed training with gradient accumulation and mixed precision. Fine-tuned models are automatically deployed to the inference API and versioned for rollback.

Provides role-based access control (RBAC) with granular permissions (read-only, inference, fine-tuning, admin). API keys can be scoped to specific models, endpoints, or operations. Key rotation and expiration policies are configurable. Audit logs track all API key usage and permission changes. Organization-level access control allows teams to manage multiple users and projects.

Allows organizations to reserve dedicated GPU clusters (single or multi-node) for exclusive use, bypassing shared inference queues and achieving predictable latency and throughput. Together provisions the cluster, handles GPU driver updates, networking, and monitoring. Users deploy their own models or use Together's pre-optimized models on the cluster via the same API, with full control over resource allocation and scaling policies. Billing is capacity-based (per GPU-hour) rather than usage-based.

Together's proprietary serving stack implements kernel-level optimizations including FlashAttention (fast attention computation), quantization (INT8/FP8), continuous batching, and request pipelining to maximize throughput and minimize latency. The stack automatically applies these optimizations to compatible models without user configuration. Throughput improvements are achieved through dynamic batching (combining multiple requests into single forward passes) and memory-efficient attention mechanisms that reduce VRAM usage by 30-50%.

Provides intelligent request routing and orchestration across multiple models based on latency, cost, and accuracy tradeoffs. Users define routing policies (e.g., 'use Mistral for simple queries, Llama for complex reasoning') and Together's platform automatically routes requests to the optimal model. The system includes fallback logic (if primary model is overloaded, route to secondary), A/B testing support for comparing model outputs, and cost-aware routing that selects cheaper models when quality is equivalent.

Enables asynchronous batch processing of large inference workloads through a job queue system. Users submit batch jobs (CSV, JSONL, or Parquet files) specifying the model and inference parameters. Together schedules the job across available capacity, processes requests in optimized batches, and returns results via callback webhook or downloadable result file. Batch processing is significantly cheaper than real-time inference due to lower latency requirements and ability to pack requests densely.

Provides built-in tools to benchmark and compare models across latency, throughput, cost, and quality metrics. Users can run standardized benchmarks (e.g., MMLU, HellaSwag) or custom evaluation datasets against multiple models simultaneously. The platform collects detailed performance metrics (p50/p95/p99 latency, tokens/second, cost per 1M tokens) and generates comparison reports. Benchmarking results are cached and reused across users to reduce redundant computation.

Exposes inference capabilities through multiple API protocols (REST, gRPC, WebSocket) with language-specific SDKs (Python, JavaScript, Go, Java). The REST API follows OpenAI-compatible format for easy migration from OpenAI. gRPC support enables low-latency streaming for real-time applications. WebSocket support allows persistent connections for chat applications. All protocols support streaming responses (token-by-token output) and structured output (JSON schema validation).

Provides real-time dashboards and APIs for monitoring inference usage, costs, and performance metrics. Users can track tokens consumed, API calls, latency percentiles, and error rates per model, endpoint, or user. Detailed logs include request/response payloads (with optional PII redaction), latency breakdowns, and error traces. Cost tracking shows per-request costs and projected monthly spend. Alerts can be configured for quota overages or performance degradation.

Enables versioning of fine-tuned models and controlled deployment across environments (staging, production). Users can create model versions, compare performance across versions, and gradually roll out new versions using canary deployments (e.g., 10% traffic to new version, 90% to stable). Rollback to previous versions is instant. Model metadata (training date, dataset, hyperparameters) is tracked automatically.