Together Compute
Contact SalesFull-stack AI-native cloud for inference, fine-tuning, and GPU compute.
By Tanmay Verma, Founder · Last verified 20 Jun 2026
4.6k views
Added 26d ago
70/100Safe BetIn short
Together Compute — Full-stack AI-native cloud for inference, fine-tuning, and GPU compute. Best for Developers needing high-throughput inference APIs for production apps, Teams scaling batch AI workloads with massive token volumes, Researchers requiring custom kernel optimizations for pre-training. Plans from $50/mo.
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Editorial Verdict
Best for
Developers needing high-throughput inference APIs for production appsTeams scaling batch AI workloads with massive token volumesResearchers requiring custom kernel optimizations for pre-trainingEnterprises deploying fine-tuned open-source models in dedicated environmentsAI startups looking for flexible GPU clusters without long-term contracts
Not ideal for
Users needing a no-code AI platform with drag-and-drop interfacesTeams requiring extensive managed services beyond core AI computeSmall projects with very low inference throughput needsOrganizations that prefer fully closed-source, proprietary modelsNon-technical users seeking turnkey AI solutions
Top-tier for teams needing raw inference performance and flexible GPU compute without lock-in. Research-driven optimizations deliver measurable speed and cost gains, but the platform demands technical fluency with open-source models and DevOps.
Compare with: Together Compute vs Predibase, Together Compute vs BitNet, Together Compute vs MAX Engine
Last verified: June 2026
Behind the Verdict
Together Compute excels for developers and enterprises that need high-throughput inference with low latency and cost efficiency. Its research-backed optimizations, such as FlashAttention-4 and custom kernel collections, provide a tangible edge in production AI workloads. The serverless and batch inference options scale to billions of tokens, making it ideal for large-scale AI applications. However, the platform is not for non-technical users or those seeking a no-code AI solution. It requires comfort with open-source models, APIs, and infrastructure management. Compared to competitors like Anyscale or Modal, Together Compute offers more specialized inference optimizations and a broader model library. A notable caveat: while pricing is pay-as-you-go for serverless, dedicated GPU clusters require a sales contact, which may slow procurement for smaller teams. Overall, Together Compute is a powerful choice for AI-native teams prioritizing performance and flexibility.
Skip Together Compute if Skip Together AI if you need a no-code platform, prefer proprietary foundation models, or have very low inference volume where pay-as-you-go from simpler providers suffices.
Latest from Together Compute
Updated yesterdayAcross the latest 1 update: 1 news mention.
Viability Score
70/100
Safe BetHow likely is Together Compute to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
70
website health
90
github activity
45
wrapper dependency
100
Last calculated: June 2026
How we score →About Together Compute
Together Compute is a full-stack AI cloud platform designed for developers and enterprises to accelerate inference, pre-training, and model fine-tuning. It offers high-performance inference as APIs, batch processing, dedicated model and container inference, GPU clusters, managed storage, and sandbox environments. Key features include serverless inference, batch inference scaling to 30B tokens per model, and dedicated GPU clusters from self-serve to thousands of GPUs. The platform is built on cutting-edge research like FlashAttention-4 and the Together Kernel Collection, delivering 2x faster inference and 60% lower cost. Together Compute positions itself as the AI-native cloud for production AI workloads, trusted by leading AI companies.
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Key Features
- Serverless inference for open-source models
- Batch inference scaling to 30B tokens per model
- Dedicated model inference on custom hardware
- Dedicated container inference for generative media
- GPU clusters from self-serve to thousands of GPUs
- AI Factory custom infrastructure at frontier scale
- Sandbox development environments for AI apps
- Managed storage with zero egress fees
- Fine-tuning open-source models with research techniques
- Model shaping using your data
- Evaluations to measure model quality
- Together Kernel Collection for faster pre-training
- FlashAttention-4 kernel for accelerated attention
- Model library with MiniMax, Qwen, GLM, DeepSeek, Llama 4
Real-world workflow fit
Concrete scenarios for the personas Together Compute actually fits — and what changes day-one when you adopt it.
ML engineer at a mid-stage startup
You need to deploy a Qwen3.5-397B chat model for a customer-facing app, scaling from prototype to millions of users.
Outcome: Start with serverless inference to test the model; then move to batch inference for cost-effective processing of user logs; finally, reserve dedicated inference for consistent latency. Together's API handles the transition without code changes.
AI researcher at an academic lab
You need to fine-tune Llama 4 Maverick on a specialized dataset and then evaluate its performance.
Outcome: Use Model Shaping (fine-tuning) with the large context window support; after training, use the Evaluations tool to measure accuracy; deploy the fine-tuned model on dedicated inference for your study.
Use Cases
- Deploy a high-throughput chat API using serverless inference on Qwen3.5-397B.
- Run batch transcription on millions of audio hours at 50% lower cost with the Batch Inference API.
- Fine-tune Llama 4 Maverick on custom domain data using Model Shaping and evaluation tools.
- Train a custom vision-language model from scratch using GPU clusters with GB200 accelerators.
- Build a production voice agent using the platform's voice agent tools and dedicated inference.
Models Under the Hood
DeepSeek V3.1Qwen3.5-397BLlama 4 MaverickGLM-5.1MiniMax M2.7Kimi K2.6Qwen3.6-Plusgpt-oss-120BGemma 4 31BFLUX.2 [pro]
Limitations
Pricing details are not publicly listed for dedicated, batch, cluster, fine-tuning, sandbox, and storage plans — requires contacting sales. The platform is designed for advanced users; no-code or GUI-based model management is limited. Support tiers require minimum commitments (e.g., Scale tier for standard support, Enterprise for Silver). GPU cluster customers get Gold support, but pricing is opaque.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
—
Contact sales for a quote
Effective monthly
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Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Together Compute tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Serverless Inference
Pay-per-token (variable by model)
Ideal for
Startups and developers exploring open-source models with low-to-medium token volumes, paying per token with no upfront commitment.
What this tier adds
Pay-as-you-go per token; no upfront commitment; supports all modalities (chat, vision, image, audio, video, transcription). Starting tier for most users.
Batch Inference
Contact for pricing (50% lower than serverless)
Ideal for
Teams processing billions of tokens asynchronously, such as batch transcription or large-scale data labeling, needing 50% cost reduction.
What this tier adds
50% lower per-token cost than serverless; asynchronous processing for large workloads; supports most models.
Dedicated Inference
Contact for pricing
Ideal for
Production teams with consistent throughput demands needing reserved capacity and lower per-token cost at scale.
What this tier adds
Custom hardware reservation; lower per-token cost at scale; includes SLAs for dedicated models.
GPU Clusters
Contact for pricing
Ideal for
Research teams and AI companies training or fine-tuning large models, needing self-service access to NVIDIA Blackwell GPUs.
What this tier adds
Self-service NVIDIA GPUs (GB300, GB200, B200, H200, H100); flexible provisioning; optimized with Together Kernel Collection.
Fine-Tuning (Model Shaping)
Contact for pricing
Ideal for
Developers adapting open-source models to custom domains with larger context windows and evaluation tools.
What this tier adds
Supports larger models and longer contexts; includes evaluation tools; managed training pipelines.
Sandbox (Developer Environments)
Contact for pricing
Ideal for
AI engineers prototyping agents or apps quickly with pre-configured sandbox environments and collaboration features.
What this tier adds
Fast, secure code sandboxes; pre-configured stacks; rapid prototyping; snapshot saving.
Managed Storage
Contact for pricing
Ideal for
Teams needing to store model weights and data securely with zero egress fees and low-latency access integrated with compute.
What this tier adds
High-performance object storage and parallel filesystems; zero egress fees; integrated with Together compute.
Integrations
CodeSandbox SDKPythonOpenAI-compatible APIGitHubHugging FaceDockerKubernetesPrometheusGrafanaAWS S3Azure BlobGoogle Cloud Storage
Where the pricing makes sense
The company stage and team size where Together Compute's pricing actually pencils out — and where peers do it cheaper.
Together AI's serverless inference is competitive at scale, especially with Batch API at 50% lower cost. For startups, the per-token model allows low-volume starts, but dedicated plans require sales contact — similar to Fireworks AI but with deeper research optimization. For large training runs, GPU clusters with Blackwell GPUs offer better performance per dollar than AWS or Azure, but lack their ecosystem.
Setup time & first value
How long it actually takes to get something useful out of Together Compute — broken out by persona, not the marketing-page minute.
Serverless inference: minutes to get an API key and start querying models via curl or SDK. Fine-tuning: a few hours to prepare data and configure a job via the dashboard or API. GPU clusters: minutes for self-service nodes; custom AI Factory requires sales engagement (days). Sandbox: immediately after signing up.
Switching to or from Together Compute
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From OpenAI: switch to Together's OpenAI-compatible API endpoint for open-source models.
- →From Replicate: migrate by adapting your code to Together's API (similar REST interface).
- →From AWS SageMaker: export model artifacts and fine-tune using Together's Model Shaping or deploy directly.
Migrating out
- ↗To Hugging Face: export fine-tuned model weights and upload to Hugging Face Hub.
- ↗To Fireworks AI: adapt API calls (both are OpenAI-compatible).
- ↗To self-hosted: download model weights from Together's managed storage and deploy on your own infrastructure.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
- •2026-05-26: FlashAttention-4 announced — up to 1.3x faster than cuDNN on NVIDIA Blackwell GPUs.
- •2026-05-26: ATLAS runtime-learning accelerators launched, delivering up to 4x faster LLM inference.
- •2026-05-26: Self-service NVIDIA GPU clusters (GB300, GB200, B200, H200, H100) made generally available.
- •2026-05-26: Batch Inference API launched, processing billions of tokens at 50% lower cost for most models.
Resources & Guides
- Resourcedocs.together.ai
Overview - Together AI docs
Run, train, and serve open-source AI models on Together AI.
- Resourcetogether.ai
Cookbooks | Together AI
Helpful link from together.ai
- Resourcetogether.ai
Demos | Together AI
Helpful link from together.ai
- Resourcetogether.ai
Support | Together AI
Helpful link from together.ai
- Resourcetogether.ai
Blog | Together AI
Helpful link from together.ai
- Resourcetogether.ai
Events | Together AI
Helpful link from together.ai
Frequently Asked Questions
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