Together Compute
High-throughput inference and GPU compute for open-source AI models
Together Compute is the top pick for developer teams that need raw inference speed and flexible GPU compute. The research-backed kernels deliver real gains, and the per-token pricing on serverless inference keeps costs low. It's technical and CLI-heavy, so non-technical teams should look elsewhere. Compared to AWS SageMaker, you get less managed hand-holding but better performance per dollar. The recent Series C funding and Y Combinator partnership signal growth, but pricing opacity for dedicated tiers remains a hurdle for budget-conscious teams.
Verified 1d ago · liveness 80/100 · cite: rightaichoice.com/tools/together-compute
- Developers needing high-throughput inference APIs
- Teams scaling batch AI workloads with massive token volumes
- Researchers requiring custom kernel optimizations for pre-training
- Enterprises deploying fine-tuned open-source models
- Non-technical users seeking no-code AI tools
- Teams requiring extensive managed services beyond AI compute
- Projects with very low inference throughput needs
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Skip Together Compute if you need a no-code AI platform, require transparent self-serve pricing for dedicated compute, or rely on proprietary models not available on the platform.
Dedicated tiers like Provisioned Throughput and GPU Clusters require contacting sales, so actual costs are not transparent upfront.
Serverless inference pricing is competitive, with DeepSeek V4 Pro at $1.74/1M input tokens, undercutting many rivals. Batch API offers 50% discount, making it cost-effective for high-volume workloads. However, for dedicated compute, you'll pay a premium for performance, and pricing is sales-led. Compared to AWS SageMaker, you get better performance per dollar but less transparency. Lambda Labs may offer simpler pricing, but Together's kernel optimizations can justify the cost for
In short
Together Compute — High-throughput inference and GPU compute for open-source AI models. Best for Developers needing high-throughput inference APIs, Teams scaling batch AI workloads with massive token volumes, Researchers requiring custom kernel optimizations for pre-training. Free to use.
What's new in Together Compute
Checked yesterdayAcross the latest 3 updates: 1 feature update, 1 launch and 1 news mention.
Announcing our Series C
Together AI raises Series C funding to scale compute and model offerings.
On-demand B200s now available on Together GPU Clusters
B200 GPUs are now available on-demand for GPU clusters, providing access to latest hardware.
Together AI & Y Combinator announce partnership
Partnership delivers first dedicated YC GPU cluster to support startups.
What people actually say about Together Compute — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
33 mentions across 3 sources (Hacker News, YouTube, Lemmy) · researched Aug 13, 2026.
- +Research-backed kernels like FlashAttention-4 promise 2x faster inference.
- +Serverless inference covers over 100 open-source models.
- +Batch processing scales to 30B tokens for big workloads.
- +GPU clusters with latest hardware including H100, H200, and B200.
- +Enterprise features like ISO certification and uptime SLA.
- −Virtually no independent community reviews or benchmarks.
- −Advanced skill level required — not beginner friendly.
- −Pricing transparency poor; costs can escalate quickly.
- −Limited integration ecosystem compared to hyperscalers.
- −Lock-in to Together's kernels and storage may hinder portability.
- • Data transfer fees for large outputs (no explicit mention of zero egress only for storage)
- • Surge pricing during high demand
- • Cost of fine-tuning and evaluation jobs
Viability Score
How well maintained and how widely used is Together Compute? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Serverless inference for 100+ open-source models
- Batch inference scaling to 30B tokens
- Provisioned throughput with 99% uptime SLA
- Dedicated model inference on H100/H200/B200/GB200/GB300
- Dedicated container inference for generative media
- GPU clusters with on-demand B200s (Aug 2026)
- AI Factory custom infrastructure at frontier scale
- Sandbox development environments via CodeSandbox SDK
- Managed Storage with zero egress fees
- Voice agents for production deployment
- Model Shaping fine-tuning with your data
- Evaluations to measure model quality
- FlashAttention-4 kernel for 2x faster inference
- Together Kernel Collection for accelerated training
- OpenAI-compatible API for easy migration
About Together Compute
Together Compute is a full-stack AI cloud platform for developers and enterprises running open-source models in production. It packages high-performance inference, batch processing, fine-tuning, and GPU clusters behind a single API, so teams skip the infrastructure grind. Built on Together Research, it features kernels like FlashAttention-4 and ParallelKernelBench that deliver 2x faster inference, 60% lower cost, and 90% faster pre-training. You get serverless inference for 100+ models, batch jobs scaling to 30B tokens, dedicated endpoints, and on-demand clusters with H100, H200, B200, GB200, GB300, and the newest on-demand B200s. For teams needing more than inference, Together Compute covers the whole lifecycle: Model Shaping fine-tunes open models with your data, Evaluations measure quality, and Managed Storage holds weights and datasets with zero egress fees. Sandbox environments via CodeSandbox let you spin up secure dev environments for AI agents and apps. Voice agents are explicitly supported for production builds. The platform is positioned against hyperscaler AI clouds, trading breadth of managed services for raw speed and model diversity. Expect a technical, API-first experience. ISO 27001:2022 certification and a 99% uptime SLA on provisioned throughput make it enterprise-viable. Hosted models include DeepSeek V4 Pro, MiniMax M3, GLM-5.2, Qwen3.7-Max, and Llama 4. Together competes with AWS SageMaker and Lambda Labs, but wins on inference economics and research-driven kernel optimizations.
Behind the Verdict
Together Compute impresses with its research-driven performance: FlashAttention-4 kernels and the Together Kernel Collection provide tangible speedups in inference and pre-training. The platform is built for developers who value API-first workflows and deep integration with open-source models. The serverless inference pricing is transparent for many models, with clear per-token costs and batch discounts. However, dedicated tiers like Provisioned Throughput and GPU Clusters require contacting sales, which adds friction and hides real costs. The platform's strength is also its weakness: it's niche, technical, and not suited for non-technical users or teams needing a broad managed service catalog. For AI startups and research teams, the flexibility and performance are unmatched. For enterprises, the ISO 27001 and SLA are reassuring, but they must navigate sales-led pricing. Overall, Together Compute is a powerful tool for those who can handle its complexity, but it's not for everyone.
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Real-world workflow fit
Concrete scenarios for the personas Together Compute actually fits — and what changes day-one when you adopt it.
Deploying a chat API in production using serverless inference.
Outcome: Within minutes, you can have a scalable endpoint with per-token pricing, without managing infrastructure.
Fine-tuning a model for domain-specific tasks.
Outcome: Use Model Shaping to fine-tune with your data and Evaluations to measure quality, all integrated into the same API.
Scaling a batch processing pipeline for millions of documents.
Outcome: Batch Inference API processes up to 30B tokens at 50% lower cost, reducing time and money.
Use Cases
- Deploy a high-throughput chat API using serverless inference on Qwen3.7-Max.
- 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.
- Run a production coding agent with 31% more TPS than competing engines.
Models Under the Hood
as of 2026-08-14
Limitations
- Pricing details are not publicly listed for most services and require contacting sales.
- The platform is API and CLI oriented, with no mention of a no-code graphical interface.
- Support tiers may require minimum commitments, and GPU cluster pricing is opaque.
- Dedicated tiers like Provisioned Throughput and GPU Clusters have no self-serve pricing, which can slow down procurement.
- The platform's focus on open-source models means you won't find proprietary models like GPT-4o, which could be a limitation for teams needing those.
as of 2026-08-14
Verification history
We have re-verified Together Compute 15 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 15 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
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.
Free
$0
Ideal for
Developers exploring the platform with low-volume serverless inference needs.
What this tier adds
Free tier with limited rate limits and access to selected open-source models.
Serverless Inference
Pay per token
Ideal for
Teams needing scalable on-demand inference with transparent per-token pricing.
What this tier adds
Pay per token with model-specific rates; includes batch discount and caching discounts.
Provisioned Throughput
Contact sales
Ideal for
Production workloads requiring reserved capacity and SLAs.
What this tier adds
Reserved token capacity and 99% uptime SLA, with drop-in API compatibility.
Dedicated Inference
Contact sales
Ideal for
Teams needing dedicated hardware for custom model deployment and maximum control.
What this tier adds
Dedicated hardware with speed and control, ideal for large-scale inference.
GPU Clusters
Contact sales
Ideal for
Researchers and startups needing flexible GPU compute for training without long-term contracts.
What this tier adds
Instant clusters with H100/H200/B200/GB200/GB300, optimized for Together Kernel Collection.
AI Factory
Contact sales
Ideal for
Enterprises needing custom infrastructure at frontier scale for large-scale pre-training.
What this tier adds
Tailored AI factory infrastructure for massive training workloads.
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.
Serverless inference pricing is competitive, with DeepSeek V4 Pro at $1.74/1M input tokens, undercutting many rivals. Batch API offers 50% discount, making it cost-effective for high-volume workloads. However, for dedicated compute, you'll pay a premium for performance, and pricing is sales-led. Compared to AWS SageMaker, you get better performance per dollar but less transparency. Lambda Labs may offer simpler pricing, but Together's kernel optimizations can justify the cost for
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 first API call. Batch API: minutes to configure a job. Model Shaping: hours to prepare data and launch a fine-tuning job, depending on dataset size. GPU Clusters: instant provision, but negotiation with sales for dedicated capacity may take days.
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.
- →From OpenAI: OpenAI-compatible API allows drop-in migration, just change the endpoint and API key.
- →From AWS SageMaker: Export models and use Together's API; benefits from faster inference and lower cost.
- ↗To AWS SageMaker: Export fine-tuned weights and redeploy; but you lose Together's kernel optimizations.
- ↗To Lambda Labs: For simpler pricing, but may sacrifice inference performance.
Integrations
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
Tutorials & Learning
Official links
Tools that pair well with Together Compute
Common stack mates teams adopt alongside Together Compute, with the specific reason each pairing earns its keep.
MAX Engine
GPU-agnostic GenAI inference framework for serving, customizing, and optimizing open-source models.
SambaNova Cloud
Fastest RDU inference for open-source AI models, including MiniMax M2.7, DeepSeek-V3.1, and gpt-oss-120b.
Small Doge
Ultra-fast open-source small language models for edge inference
Alternatives to Together Compute
View allMAX Engine
GPU-agnostic GenAI inference framework for serving, customizing, and optimizing open-source models.
SambaNova Cloud
Fastest RDU inference for open-source AI models, including MiniMax M2.7, DeepSeek-V3.1, and gpt-oss-120b.
Small Doge
Ultra-fast open-source small language models for edge inference
Frequently Asked Questions
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