What can Together AI do?

multi-model serverless inference api with per-token pricing, batch inference api with 50% cost reduction and asynchronous processing, custom cuda kernel optimization for inference and training acceleration, managed storage with zero egress fees for model artifacts and data, dedicated gpu inference with private model deployment, fine-tuning platform with longer context and larger model support, image generation with flux, stable diffusion, and proprietary models, vision model inference with image understanding and analysis, embedding model inference for semantic search and similarity, reranking and moderation models for ranking and content filtering, gpu cluster provisioning with self-service scaling, secure code sandbox execution for ai agents and applications

Together AI

Model

Train, fine-tune-and run inference on AI models blazing fast, at low cost, and at production scale.

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12 capabilities

Capabilities12 decomposed

multi-model serverless inference api with per-token pricing

Medium confidence

Provides unified REST API access to 50+ hosted models (text, vision, image generation, embeddings) with automatic load balancing and pay-per-token billing. Requests are routed to optimized inference clusters running custom CUDA kernels (FlashAttention-4, ATLAS) for 2× claimed speedup. No infrastructure provisioning required; models scale elastically based on demand.

Solves for

I need to call multiple LLMs (Llama, Qwen, DeepSeek, GLM) from a single API without managing infrastructureI want to reduce inference latency and costs by using optimized kernel implementations instead of stock model servingI need to switch between models (e.g., smaller for cost, larger for quality) without code changes

Best for

startups and teams building multi-model AI applications without DevOps capacity

developers prototyping LLM agents that need model flexibility

cost-conscious builders wanting 50% savings vs. OpenAI/Anthropic for commodity models

Requires

Together AI API key (obtained from dashboard)

HTTP client library (curl, Python requests, JavaScript fetch, etc.)

Network connectivity to together.ai endpoints

Limitations

No documented SLA or uptime guarantees; reliability claims absent from source material

Latency benchmarks not provided (only relative speedup claims like '2× faster'); actual ms/token unknown

API documentation not included in source material; request/response formats, rate limits, authentication methods unspecified

What makes it unique

Unified API gateway across 50+ heterogeneous models (text, vision, image, audio, embeddings) with custom CUDA kernel optimization (FlashAttention-4, ATLAS runtime learners) for 2× claimed speedup, eliminating need to manage separate endpoints per model provider

vs alternatives

Faster and cheaper than calling OpenAI/Anthropic directly for open-source models (Llama, Qwen, DeepSeek) due to custom kernel optimization; more model variety than single-provider APIs but less mature documentation than established platforms

batch inference api with 50% cost reduction and asynchronous processing

Medium confidence

Processes large token volumes (up to 30B tokens per model) asynchronously via batch jobs, applying custom kernel optimizations to reduce per-token cost by 50% vs. serverless. Batches are queued, scheduled during off-peak GPU availability, and results are returned via webhook or polling. Ideal for non-latency-sensitive workloads like data labeling, content generation, or model evaluation.

Solves for

I need to process millions of tokens (e.g., labeling a dataset, generating synthetic data) at minimal costI want to run inference on large documents or corpora without paying premium serverless ratesI need to evaluate multiple models on a benchmark dataset and can tolerate hours of latency

Best for

data teams running batch data processing pipelines (ETL, labeling, augmentation)

researchers evaluating models on large benchmarks

cost-optimized production systems where latency is not critical (hours acceptable)

Requires

Together AI API key with batch API access

Input data in supported format (likely JSONL or CSV; exact format unspecified)

Ability to wait hours for results (no real-time processing)

Limitations

Specific batch pricing rates not provided in source material; only '50% discount' claim without absolute numbers

Maximum batch size (30B tokens per model) may require splitting very large jobs across multiple submissions

No documented SLA for batch completion time; scheduling depends on cluster utilization

What makes it unique

Dedicated batch queue with custom kernel scheduling that achieves 50% cost reduction by batching requests during off-peak GPU availability and applying FlashAttention-4/ATLAS optimizations at scale; supports up to 30B tokens per submission without per-token rate limiting

vs alternatives

Significantly cheaper than serverless for large-scale inference (50% claimed savings); more cost-effective than OpenAI Batch API for open-source models, but lacks documented completion SLA and integration patterns

custom cuda kernel optimization for inference and training acceleration

Medium confidence

Together AI develops and deploys custom CUDA kernels (FlashAttention-4, ATLAS runtime learners, speculative decoding variants) that optimize inference and training performance. FlashAttention-4 claims 1.3× speedup vs. cuDNN on NVIDIA Blackwell. ATLAS claims 4× faster LLM inference. Kernels are transparently applied to all hosted models without user configuration.

Solves for

I want faster inference without code changes or model modificationsI need to reduce inference latency and cost through hardware-level optimizationI want to leverage latest GPU architecture improvements (Blackwell) automatically

Best for

teams using Together's inference APIs (serverless, batch, dedicated) who benefit automatically

researchers studying inference optimization techniques

cost-sensitive applications where 2× speedup translates to 50% cost savings

Requires

Use of Together AI inference APIs (serverless, batch, or dedicated)

Compatible GPU hardware (NVIDIA Blackwell mentioned; others unspecified)

No explicit user configuration; optimization applied automatically

Limitations

Kernel optimization is transparent; no user control over which kernels are applied

Speedup claims (2× general, 1.3× FlashAttention-4, 4× ATLAS) are unverified and lack peer-reviewed benchmarks

Latency benchmarks not provided; only relative speedup claims

What makes it unique

Proprietary custom CUDA kernel stack (FlashAttention-4, ATLAS, speculative decoding) transparently applied to all hosted models, claiming 2× general speedup and 1.3× FlashAttention-4 speedup on NVIDIA Blackwell; eliminates need for manual kernel selection or tuning

vs alternatives

Automatic kernel optimization without user configuration vs. manual kernel selection in vLLM or TensorRT; claims faster than stock cuDNN implementations but lacks peer-reviewed benchmarks vs. competing optimization frameworks

managed storage with zero egress fees for model artifacts and data

Medium confidence

Provides cloud storage for model weights, training data, and inference artifacts with zero egress fees when used within Together's ecosystem. Eliminates data transfer costs for models deployed to Together's inference endpoints. Storage pricing and capacity limits not documented.

Solves for

I want to store model weights and training data without paying egress fees when deploying to TogetherI need to manage large model artifacts (10GB+) without worrying about data transfer costsI want to integrate storage with fine-tuning and inference workflows seamlessly

Best for

teams fine-tuning and deploying models on Together (zero egress benefit)

organizations managing large model artifact libraries

cost-optimized ML workflows where egress fees are significant

Requires

Together AI account with storage access

Model artifacts or data files in supported format (format unspecified)

Limitations

Storage pricing per GB not documented

Storage capacity limits not specified

Retention policies and data deletion procedures unknown

What makes it unique

Integrated managed storage with explicit zero egress fees for artifacts used within Together's inference/fine-tuning ecosystem, eliminating data transfer costs for model deployment workflows

vs alternatives

Zero egress within Together ecosystem vs. AWS S3 or GCP Cloud Storage where egress fees apply; less feature-rich than general-purpose cloud storage but optimized for ML artifact management

dedicated gpu inference with private model deployment

Medium confidence

Provisions dedicated GPU infrastructure for single-tenant model deployment, isolating inference workloads from shared serverless clusters. Models run on reserved GPUs with guaranteed availability and no noisy-neighbor interference. Supports custom container images and optimized kernel stacks (FlashAttention-4, ATLAS). Pricing model and hardware specs not documented.

Solves for

I need guaranteed low-latency inference for production applications with SLA requirementsI want to deploy a fine-tuned model privately without sharing GPU resources with other customersI need to run inference with custom preprocessing/postprocessing logic in a containerized environment

Best for

enterprises requiring dedicated infrastructure and compliance isolation

production systems with strict latency SLAs (sub-100ms requirements)

teams deploying proprietary or fine-tuned models that cannot share hardware

Requires

Together AI enterprise account or dedicated deployment agreement

Container image (Docker format implied but unconfirmed)

Model weights in supported format (safetensors, ONNX, or proprietary; unspecified)

Limitations

Pricing model not documented; no per-GPU-hour or per-request rates provided

Hardware specifications (GPU type, VRAM, CPU, network) not specified in source material

Minimum commitment period and scaling policies unknown

What makes it unique

Single-tenant GPU reservation with custom kernel stack (FlashAttention-4, ATLAS) and containerized deployment support, eliminating noisy-neighbor interference and enabling proprietary model hosting; purpose-built for production inference with guaranteed resource isolation

vs alternatives

More cost-effective than AWS SageMaker or Azure ML for dedicated inference due to custom kernel optimization; less mature than established platforms but offers tighter integration with Together's optimization stack

fine-tuning platform with longer context and larger model support

Medium confidence

Enables supervised fine-tuning of open-source models (Llama, Qwen, Gemma, etc.) with recent upgrades supporting larger models and longer context windows. Fine-tuning methodology (LoRA, QLoRA, full) not documented. Trained models are deployed to serverless or dedicated inference endpoints. Claims to improve accuracy, reduce hallucinations, and enable behavior control.

Solves for

I want to adapt a base model to my domain (e.g., legal, medical, code) using my own training dataI need to reduce hallucinations or enforce specific output formats through fine-tuningI want to create a specialized model variant without training from scratch

Best for

teams with domain-specific datasets (100+ examples minimum, likely)

builders needing to customize model behavior without full pre-training

enterprises requiring proprietary model variants for compliance or IP reasons

Requires

Together AI account with fine-tuning access

Training dataset in supported format (likely JSONL; exact format unspecified)

Base model selection from supported list (Llama, Qwen, Gemma, etc.)

Limitations

Fine-tuning methodology not documented; unclear if LoRA, QLoRA, full fine-tuning, or hybrid approach

Hyperparameter tuning options not specified; learning rate, batch size, epochs, warmup unknown

Training data format requirements not documented (likely JSONL with prompt/completion pairs, unconfirmed)

What makes it unique

Recent platform upgrades support larger models and longer context windows for fine-tuning (specific improvements unspecified), with integrated deployment to serverless/dedicated endpoints; methodology and hyperparameter controls not documented but claims domain-specific accuracy improvements and hallucination reduction

vs alternatives

Tighter integration with Together's inference stack than standalone fine-tuning services; less documented than OpenAI's fine-tuning API but potentially cheaper for open-source models

image generation with flux, stable diffusion, and proprietary models

Medium confidence

Hosts multiple image generation models (FLUX.2 pro/dev/flex/max, FLUX.1 schnell, Stable Diffusion 3/XL, Qwen Image 2.0, Google Imagen 4.0, ByteDance Seedream, Ideogram 3.0) via serverless API. Requests specify model, prompt, and quality/style parameters; outputs are image URLs. Pricing ranges $0.0019–$0.06 per image depending on model and resolution.

Solves for

I need to generate images from text prompts at scale for product mockups, marketing, or synthetic dataI want to compare multiple image generation models (FLUX vs. Stable Diffusion vs. Imagen) without managing separate APIsI need cost-optimized image generation (FLUX.1 schnell at $0.0027/image) for high-volume applications

Best for

content creators and marketing teams generating images at scale

AI product builders integrating image generation into applications

researchers comparing image generation model outputs

Requires

Together AI API key

Text prompt describing desired image

Model selection (FLUX.2 pro, FLUX.1 schnell, Stable Diffusion 3, etc.)

Limitations

No documentation on supported prompt formats, style parameters, or quality settings per model

Image resolution and aspect ratio options not specified

Latency per image generation not provided; only relative speedup claims

What makes it unique

Unified API access to 10+ image generation models (FLUX variants, Stable Diffusion, Qwen Image, Google Imagen, ByteDance Seedream, Ideogram) with per-image pricing ($0.0019–$0.06) and custom kernel optimization for faster generation; eliminates need to manage separate endpoints per model provider

vs alternatives

More model variety than Replicate or Hugging Face Inference API; cheaper per-image pricing for FLUX.1 schnell ($0.0027) vs. Replicate ($0.004); less mature API documentation than Stability AI's official API

vision model inference with image understanding and analysis

Medium confidence

Hosts vision-capable models (Kimi K2.6, K2.5, Qwen3.5-Vision 9B, Gemma 4 31B) that accept text prompts + image inputs and return text analysis/descriptions. Models process images via URL or embedded format (unspecified). Supports visual question answering, document analysis, scene understanding, and multimodal reasoning.

Solves for

I need to analyze images (OCR, scene understanding, object detection) without building a custom vision modelI want to ask questions about images (visual QA) and get detailed text responsesI need to process documents with images and text together for information extraction

Best for

teams building document processing pipelines (invoices, receipts, forms)

content moderation systems requiring image understanding

accessibility applications converting images to text descriptions

Requires

Together AI API key

Image input (URL or embedded format, unspecified)

Text prompt asking about the image

Limitations

Image input format not documented; unclear if URLs, base64, or multipart uploads supported

Image resolution and size limits not specified

Vision model capabilities (OCR accuracy, object detection, scene understanding) not benchmarked

What makes it unique

Unified API for multiple vision models (Kimi, Qwen, Gemma) with custom kernel optimization for faster image processing; supports multimodal reasoning combining text and image inputs without separate vision/language model calls

vs alternatives

More model variety than OpenAI's vision API; potentially cheaper for open-source vision models (Qwen3.5-Vision) vs. GPT-4V; less mature documentation than established vision platforms

embedding model inference for semantic search and similarity

Medium confidence

Hosts embedding models that convert text into dense vector representations for semantic search, similarity matching, and RAG applications. Models produce fixed-dimension embeddings (dimension size unspecified per model). Supports batch embedding requests for large-scale vector generation.

Solves for

I need to embed documents and queries for semantic search without managing embedding infrastructureI want to build a RAG system with vector similarity matching for retrievalI need to compute semantic similarity between text pairs at scale

Best for

teams building RAG applications requiring semantic search

search/recommendation systems using vector similarity

data teams generating embeddings for clustering or classification

Requires

Together AI API key

Text input (single string or batch of strings)

Embedding model selection (specific models unspecified)

Limitations

Embedding model names and dimensions not documented in source material

Batch embedding API not documented; single-request latency unknown

Vector dimension size per model not specified; unclear if 384, 768, 1536, etc.

What makes it unique

Unified embedding API with custom kernel optimization for faster vector generation; integrates with Together's inference stack for seamless RAG pipelines combining embedding + LLM inference

vs alternatives

Tighter integration with Together's LLM inference than standalone embedding APIs; less documented than OpenAI Embeddings API but potentially cheaper for open-source embedding models

reranking and moderation models for ranking and content filtering

Medium confidence

Hosts reranking models (for search result ranking) and moderation models (for content filtering/safety) as part of the model catalog. Reranking takes query + candidate documents and returns ranked scores. Moderation analyzes text for policy violations. Implementation details and specific models not documented.

Solves for

I need to rerank search results or retrieved documents by relevance to a queryI want to filter user-generated content for policy violations before processingI need to detect toxic, NSFW, or harmful content at scale

Best for

search and recommendation systems requiring multi-stage ranking

content moderation platforms processing user submissions

RAG systems needing semantic reranking of retrieved documents

Requires

Together AI API key

For reranking: query + list of candidate documents

For moderation: text content to analyze

Limitations

Specific reranking and moderation models not named in source material

Reranking scoring mechanism not documented (e.g., relevance score 0-1, ranking loss)

Moderation policy categories not specified (toxic, NSFW, spam, etc.)

What makes it unique

Integrated reranking and moderation models within unified inference platform; enables multi-stage ranking pipelines and safety filtering without separate API calls

vs alternatives

Tighter integration with Together's LLM/embedding stack than standalone reranking services; less documented than Cohere Rerank API but potentially cheaper for open-source models

gpu cluster provisioning with self-service scaling

Medium confidence

Provides on-demand GPU cluster provisioning (NVIDIA GPUs, types unspecified) for custom workloads, scaling from instant clusters to thousands of GPUs. Supports containerized workloads and custom training/inference scripts. Pricing and hardware specifications not documented. Recently launched as 'generally available' feature.

Solves for

I need to scale GPU compute from 1 to 1000+ GPUs for large-scale training or inferenceI want to run custom training scripts without managing cloud infrastructure (AWS, GCP, Azure)I need to provision temporary GPU clusters for batch processing without long-term commitments

Best for

ML teams training large models or running large-scale inference

researchers needing elastic GPU capacity for experiments

companies avoiding AWS/GCP/Azure infrastructure management

Requires

Together AI account with GPU cluster access

Container image or custom training script

Cluster size specification (number of GPUs)

Limitations

GPU types and specifications not documented (H100, A100, L40S, etc. unknown)

Pricing model not provided; per-GPU-hour rates unknown

Minimum cluster size and scaling granularity not specified

What makes it unique

Self-service GPU cluster provisioning with elastic scaling (1 to 1000+ GPUs) and custom workload support, integrated with Together's kernel optimization stack; eliminates AWS/GCP/Azure infrastructure management for ML teams

vs alternatives

Simpler provisioning than AWS SageMaker or Azure ML for custom workloads; less mature than established cloud platforms (recently launched) but potentially tighter integration with Together's optimization kernels

secure code sandbox execution for ai agents and applications

Medium confidence

Provides isolated code execution environments ('sandboxes') for running AI-generated code safely at scale. Sandboxes prevent malicious code execution and resource exhaustion. Used for AI agent development, code generation validation, and secure execution of LLM-generated scripts. Implementation details (containerization, resource limits, timeout policies) not documented.

Solves for

I need to safely execute code generated by LLMs without risking host system compromiseI want to build AI agents that can write and run code (e.g., data analysis, automation) securelyI need to validate generated code before deploying to production

Best for

AI agent platforms requiring safe code execution (e.g., code interpreter, data analyst agents)

LLM-powered development tools (code generation, debugging)

educational platforms teaching AI with code generation

Requires

Together AI account with sandbox access

Code to execute (language unspecified; likely Python)

Input data or parameters for code execution

Limitations

Sandbox resource limits (CPU, memory, disk, network) not documented

Timeout policies and execution time limits unknown

Supported languages and libraries not specified (Python only? Node.js? C++?)

What makes it unique

Isolated code sandbox execution environment for AI agents and LLM-generated code, preventing malicious execution and resource exhaustion; integrates with Together's inference platform for seamless agent development workflows

vs alternatives

Tighter integration with Together's LLM inference than standalone sandbox services; less documented than E2B or Replit's sandbox offerings but potentially more cost-effective for Together platform users

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with Together AI, ranked by overlap. Discovered automatically through the match graph.

Platform40

CoreWeave

Specialized GPU cloud with InfiniBand networking for enterprise AI.

inference-specific gpu pricing with 10x faster spin-up timesmodel serving and inference api deployment with vllm/tensorrt support

2 shared capabilities

Platform43

Baseten

ML inference platform — deploy models as auto-scaling GPU endpoints with Truss packaging.

gpu-accelerated model inference with per-minute billingpre-optimized model api marketplace with token-based pricing

2 shared capabilities

Platform40

Together AI Platform

AI cloud with serverless inference for 100+ open-source models.

serverless inference across 100+ open-source modelsbatch inference processing with job scheduling

2 shared capabilities

Platform40

Lambda Labs

GPU cloud for AI training — H100/A100 clusters, 1-click Jupyter, Lambda Stack.

inference deployment with gpu acceleration

1 shared capability

Platform40

Vast.ai

GPU marketplace with affordable distributed compute for AI workloads.

serverless gpu inference with automatic optimization and autoscaling

1 shared capability

API26

Groq

Accelerates AI inference, optimizes speed, scalability,...

cost-optimized inference pricing

1 shared capability

Best For

✓startups and teams building multi-model AI applications without DevOps capacity
✓developers prototyping LLM agents that need model flexibility
✓cost-conscious builders wanting 50% savings vs. OpenAI/Anthropic for commodity models
✓data teams running batch data processing pipelines (ETL, labeling, augmentation)
✓researchers evaluating models on large benchmarks
✓cost-optimized production systems where latency is not critical (hours acceptable)
✓teams using Together's inference APIs (serverless, batch, dedicated) who benefit automatically
✓researchers studying inference optimization techniques

Known Limitations

⚠No documented SLA or uptime guarantees; reliability claims absent from source material
⚠Latency benchmarks not provided (only relative speedup claims like '2× faster'); actual ms/token unknown
⚠API documentation not included in source material; request/response formats, rate limits, authentication methods unspecified
⚠Context window sizes not documented per model; varies by hosted model but not published
⚠No built-in request batching or caching layer; batch processing requires separate Batch API
⚠Specific batch pricing rates not provided in source material; only '50% discount' claim without absolute numbers

Requirements

Together AI API key (obtained from dashboard)HTTP client library (curl, Python requests, JavaScript fetch, etc.)Network connectivity to together.ai endpointsUnderstanding of which model to use (50+ options with no official comparison guide)Together AI API key with batch API accessInput data in supported format (likely JSONL or CSV; exact format unspecified)Ability to wait hours for results (no real-time processing)Webhook endpoint or polling mechanism for result retrieval

Input / Output

Accepts: text prompts (for text/chat models), text + image URLs (for vision models like Kimi K2.6, Qwen3.5-Vision), text prompts with style/quality parameters (for image generation models), JSONL or CSV files with prompts/documents (format unspecified in source), Up to 30B tokens per batch submission, standard model inference requests (no special input format required), model weights (safetensors, ONNX, or proprietary format; unspecified), training data (JSONL, CSV, or binary; format unspecified), inference artifacts (images, embeddings, etc.), text prompts, images (for vision models), custom binary data (if containerized preprocessing included), JSONL files with prompt/completion pairs (format inferred, not documented), Text data for instruction-following or chat fine-tuning, text prompts (natural language descriptions), optional style/quality parameters (format unknown), images (URL or embedded; format unspecified), optional: follow-up questions for multi-turn vision conversations, text strings (documents, queries, or text pairs), text query + document list (for reranking), text content (for moderation), container images (Docker format implied), training/inference scripts (Python, CUDA, etc.), data inputs (format depends on workload), code strings (language unspecified), input data or parameters

Produces: text completions (for LLMs), structured JSON (for function calling, if supported), image URLs (for image generation models), embedding vectors (for embedding models), text completions or structured outputs, results delivered via webhook callback or polling endpoint, same outputs as standard inference (no format changes), stored artifacts accessible to fine-tuning and inference pipelines, text completions, structured JSON, custom binary outputs (if containerized postprocessing included), fine-tuned model weights (format unspecified; likely safetensors), deployed model endpoint (serverless or dedicated), image URLs (format and expiration unknown), image metadata (dimensions, model used, generation time unknown), text analysis or descriptions, structured JSON (if function calling supported; unconfirmed), dense vectors (dimension size unspecified), optional: similarity scores (if pairwise comparison supported; unconfirmed), ranked scores or document order (for reranking), policy violation flags or confidence scores (for moderation), trained model weights, inference results, logs and metrics, execution results (stdout, stderr), return values or generated artifacts

UnfragileRank

Adoption15%(40% weight)

Quality23%(20% weight)

Ecosystem35%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

12 capabilities

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Train, fine-tune-and run inference on AI models blazing fast, at low cost, and at production scale.

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Capabilities12 decomposed

multi-model serverless inference api with per-token pricing

Medium confidence

Solves for

Best for

startups and teams building multi-model AI applications without DevOps capacity

developers prototyping LLM agents that need model flexibility

cost-conscious builders wanting 50% savings vs. OpenAI/Anthropic for commodity models

Requires

Together AI API key (obtained from dashboard)

HTTP client library (curl, Python requests, JavaScript fetch, etc.)

Network connectivity to together.ai endpoints

Limitations

No documented SLA or uptime guarantees; reliability claims absent from source material

Latency benchmarks not provided (only relative speedup claims like '2× faster'); actual ms/token unknown

API documentation not included in source material; request/response formats, rate limits, authentication methods unspecified

What makes it unique

vs alternatives

batch inference api with 50% cost reduction and asynchronous processing

Medium confidence

Solves for

Best for

data teams running batch data processing pipelines (ETL, labeling, augmentation)

researchers evaluating models on large benchmarks

cost-optimized production systems where latency is not critical (hours acceptable)

Requires

Together AI API key with batch API access

Input data in supported format (likely JSONL or CSV; exact format unspecified)

Ability to wait hours for results (no real-time processing)

Limitations

Specific batch pricing rates not provided in source material; only '50% discount' claim without absolute numbers

Maximum batch size (30B tokens per model) may require splitting very large jobs across multiple submissions

No documented SLA for batch completion time; scheduling depends on cluster utilization

What makes it unique

vs alternatives

custom cuda kernel optimization for inference and training acceleration

Medium confidence

Solves for

Best for

teams using Together's inference APIs (serverless, batch, dedicated) who benefit automatically

researchers studying inference optimization techniques

cost-sensitive applications where 2× speedup translates to 50% cost savings

Requires

Use of Together AI inference APIs (serverless, batch, or dedicated)

Compatible GPU hardware (NVIDIA Blackwell mentioned; others unspecified)

No explicit user configuration; optimization applied automatically

Limitations

Kernel optimization is transparent; no user control over which kernels are applied

Speedup claims (2× general, 1.3× FlashAttention-4, 4× ATLAS) are unverified and lack peer-reviewed benchmarks

Latency benchmarks not provided; only relative speedup claims

What makes it unique

vs alternatives

managed storage with zero egress fees for model artifacts and data

Medium confidence

Solves for

Best for

teams fine-tuning and deploying models on Together (zero egress benefit)

organizations managing large model artifact libraries

cost-optimized ML workflows where egress fees are significant

Requires

Together AI account with storage access

Model artifacts or data files in supported format (format unspecified)

Limitations

Storage pricing per GB not documented

Storage capacity limits not specified

Retention policies and data deletion procedures unknown

What makes it unique

Integrated managed storage with explicit zero egress fees for artifacts used within Together's inference/fine-tuning ecosystem, eliminating data transfer costs for model deployment workflows

vs alternatives

Zero egress within Together ecosystem vs. AWS S3 or GCP Cloud Storage where egress fees apply; less feature-rich than general-purpose cloud storage but optimized for ML artifact management

dedicated gpu inference with private model deployment

Medium confidence

Solves for

Best for

enterprises requiring dedicated infrastructure and compliance isolation

production systems with strict latency SLAs (sub-100ms requirements)

teams deploying proprietary or fine-tuned models that cannot share hardware

Requires

Together AI enterprise account or dedicated deployment agreement

Container image (Docker format implied but unconfirmed)

Model weights in supported format (safetensors, ONNX, or proprietary; unspecified)

Limitations

Pricing model not documented; no per-GPU-hour or per-request rates provided

Hardware specifications (GPU type, VRAM, CPU, network) not specified in source material

Minimum commitment period and scaling policies unknown

What makes it unique

vs alternatives

fine-tuning platform with longer context and larger model support

Medium confidence

Solves for

Best for

teams with domain-specific datasets (100+ examples minimum, likely)

builders needing to customize model behavior without full pre-training

enterprises requiring proprietary model variants for compliance or IP reasons

Requires

Together AI account with fine-tuning access

Training dataset in supported format (likely JSONL; exact format unspecified)

Base model selection from supported list (Llama, Qwen, Gemma, etc.)

Limitations

Fine-tuning methodology not documented; unclear if LoRA, QLoRA, full fine-tuning, or hybrid approach

Hyperparameter tuning options not specified; learning rate, batch size, epochs, warmup unknown

Training data format requirements not documented (likely JSONL with prompt/completion pairs, unconfirmed)

What makes it unique

vs alternatives

Tighter integration with Together's inference stack than standalone fine-tuning services; less documented than OpenAI's fine-tuning API but potentially cheaper for open-source models

image generation with flux, stable diffusion, and proprietary models

Medium confidence

Solves for

Best for

content creators and marketing teams generating images at scale

AI product builders integrating image generation into applications

researchers comparing image generation model outputs

Requires

Together AI API key

Text prompt describing desired image

Model selection (FLUX.2 pro, FLUX.1 schnell, Stable Diffusion 3, etc.)

Limitations

No documentation on supported prompt formats, style parameters, or quality settings per model

Image resolution and aspect ratio options not specified

Latency per image generation not provided; only relative speedup claims

What makes it unique

vs alternatives

vision model inference with image understanding and analysis

Medium confidence

Solves for

Best for

teams building document processing pipelines (invoices, receipts, forms)

content moderation systems requiring image understanding

accessibility applications converting images to text descriptions

Requires

Together AI API key

Image input (URL or embedded format, unspecified)

Text prompt asking about the image

Limitations

Image input format not documented; unclear if URLs, base64, or multipart uploads supported

Image resolution and size limits not specified

Vision model capabilities (OCR accuracy, object detection, scene understanding) not benchmarked

What makes it unique

vs alternatives

More model variety than OpenAI's vision API; potentially cheaper for open-source vision models (Qwen3.5-Vision) vs. GPT-4V; less mature documentation than established vision platforms

embedding model inference for semantic search and similarity

Medium confidence

Solves for

Best for

teams building RAG applications requiring semantic search

search/recommendation systems using vector similarity

data teams generating embeddings for clustering or classification

Requires

Together AI API key

Text input (single string or batch of strings)

Embedding model selection (specific models unspecified)

Limitations

Embedding model names and dimensions not documented in source material

Batch embedding API not documented; single-request latency unknown

Vector dimension size per model not specified; unclear if 384, 768, 1536, etc.

What makes it unique

Unified embedding API with custom kernel optimization for faster vector generation; integrates with Together's inference stack for seamless RAG pipelines combining embedding + LLM inference

vs alternatives

Tighter integration with Together's LLM inference than standalone embedding APIs; less documented than OpenAI Embeddings API but potentially cheaper for open-source embedding models

reranking and moderation models for ranking and content filtering

Medium confidence

Solves for

Best for

search and recommendation systems requiring multi-stage ranking

content moderation platforms processing user submissions

RAG systems needing semantic reranking of retrieved documents

Requires

Together AI API key

For reranking: query + list of candidate documents

For moderation: text content to analyze

Limitations

Specific reranking and moderation models not named in source material

Reranking scoring mechanism not documented (e.g., relevance score 0-1, ranking loss)

Moderation policy categories not specified (toxic, NSFW, spam, etc.)

What makes it unique

Integrated reranking and moderation models within unified inference platform; enables multi-stage ranking pipelines and safety filtering without separate API calls

vs alternatives

Tighter integration with Together's LLM/embedding stack than standalone reranking services; less documented than Cohere Rerank API but potentially cheaper for open-source models

gpu cluster provisioning with self-service scaling

Medium confidence

Solves for

Best for

ML teams training large models or running large-scale inference

researchers needing elastic GPU capacity for experiments

companies avoiding AWS/GCP/Azure infrastructure management

Requires

Together AI account with GPU cluster access

Container image or custom training script

Cluster size specification (number of GPUs)

Limitations

GPU types and specifications not documented (H100, A100, L40S, etc. unknown)

Pricing model not provided; per-GPU-hour rates unknown

Minimum cluster size and scaling granularity not specified

What makes it unique

vs alternatives

secure code sandbox execution for ai agents and applications

Medium confidence

Solves for

Best for

AI agent platforms requiring safe code execution (e.g., code interpreter, data analyst agents)

LLM-powered development tools (code generation, debugging)

educational platforms teaching AI with code generation

Requires

Together AI account with sandbox access

Code to execute (language unspecified; likely Python)

Input data or parameters for code execution

Limitations

Sandbox resource limits (CPU, memory, disk, network) not documented

Timeout policies and execution time limits unknown

Supported languages and libraries not specified (Python only? Node.js? C++?)

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Together AI

Capabilities12 decomposed

multi-model serverless inference api with per-token pricing

batch inference api with 50% cost reduction and asynchronous processing

custom cuda kernel optimization for inference and training acceleration

managed storage with zero egress fees for model artifacts and data

dedicated gpu inference with private model deployment

fine-tuning platform with longer context and larger model support

image generation with flux, stable diffusion, and proprietary models

vision model inference with image understanding and analysis

embedding model inference for semantic search and similarity

reranking and moderation models for ranking and content filtering

gpu cluster provisioning with self-service scaling

secure code sandbox execution for ai agents and applications

Related Artifactssharing capabilities

CoreWeave

Baseten

Together AI Platform

Lambda Labs

Vast.ai

Groq

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Together AI

Are you the builder of Together AI?

Get the weekly brief

Data Sources

Together AI

Capabilities12 decomposed

multi-model serverless inference api with per-token pricing

batch inference api with 50% cost reduction and asynchronous processing

custom cuda kernel optimization for inference and training acceleration

managed storage with zero egress fees for model artifacts and data

dedicated gpu inference with private model deployment

fine-tuning platform with longer context and larger model support

image generation with flux, stable diffusion, and proprietary models

vision model inference with image understanding and analysis

embedding model inference for semantic search and similarity

reranking and moderation models for ranking and content filtering

gpu cluster provisioning with self-service scaling

secure code sandbox execution for ai agents and applications

Related Artifactssharing capabilities

CoreWeave

Baseten

Together AI Platform

Lambda Labs

Vast.ai

Groq

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Together AI

Are you the builder of Together AI?

Get the weekly brief

Data Sources