Multi-model prototyping
Test multiple open and hosted models behind a consistent API without provisioning a separate stack for each one.
Independent tool overview
Together AI at a glance
Together AI is a developer platform for running, fine-tuning, evaluating, and deploying open and third-party AI models. Its strongest fit is a team that wants a broad model catalog and an OpenAI-compatible API, with a path from pay-per-token serverless experiments to reserved single-tenant endpoints or GPU clusters.
Visit the official Together AI site ↗
- Product type
- AI model cloud and developer API
- API style
- OpenAI-compatible
- Deployment modes
- Serverless, batch, dedicated, and GPU clusters
- Modalities
- Text, vision, image, video, audio, embeddings, and more
- Free trial
- No
- Minimum self-serve purchase
- $5 in credits
- Security
- SOC 2 Type II and ISO 27001:2022
- Review date
- August 30, 2026
Overview
What Together AI is
Together AI provides one platform for language, vision, image, video, audio, embedding, reranking, and moderation workloads. Developers can start with shared serverless inference, use batch processing for non-interactive jobs, fine-tune supported models, or reserve hardware for predictable production performance.
The platform is infrastructure rather than a polished end-user chatbot. Teams are responsible for selecting models, evaluating output quality, implementing safeguards, monitoring costs, and designing the application around the API. The exact catalog, capabilities, context limits, and per-model prices change over time.
Together states that inputs and outputs are not stored by default, although temporary caching may be used unless configured otherwise, and model-training data sharing is opt-in. Enterprise options include private networking, VPC deployments, data-residency support, and security documentation for regulated workloads.
Use cases
Who Together AI is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Variable production traffic
Use serverless per-token billing when traffic is bursty or too early to justify reserved hardware.
Steady high-volume inference
Move suitable workloads to dedicated, single-tenant endpoints for predictable capacity and latency.
Custom model workflows
Fine-tune supported models or deploy compatible custom weights from Hugging Face or S3.
Multimodal AI applications
Combine chat, vision, generation, speech, embeddings, and safety models through one provider.
Capabilities
Core Together AI features
Serverless inference
Shared, pay-per-use endpoints with no provisioning or minimum runtime cost; model-specific rate limits still apply.
Dedicated endpoints
Reserved GPUs for a single model with autoscaling, configurable decoding, custom weights, and no shared-fleet rate limits.
Batch inference
Discounted asynchronous processing for workloads that do not need a real-time response.
Fine-tuning
Supports supervised fine-tuning and preference optimization with LoRA or full fine-tuning where available.
Evaluations
Provides API and interface workflows for pairwise comparison, scoring, and classification with model-based judges.
Official SDKs
Offers Python and TypeScript SDKs plus a REST API that can also work with OpenAI-compatible clients.
Privacy controls
Documents default non-storage of prompts and outputs, opt-in training data sharing, and enterprise deployment controls.
Process
How the Together AI workflow works
Step 1
Define the workload
Document quality, modality, latency, throughput, context, function-calling, data-governance, and budget requirements.
Step 2
Fund the account and create a key
Purchase at least $5 in credits for self-serve access, create a project-scoped API key, and keep it on the server rather than in client code.
Step 3
Benchmark candidate models
Use representative prompts and objective evaluation criteria instead of choosing from leaderboard claims alone.
Step 4
Prototype on serverless
Integrate the selected model, add retries and timeouts, and measure tokens, latency, errors, and output quality under realistic traffic.
Step 5
Add production controls
Implement moderation where needed, logging appropriate to the data policy, rate and spend controls, fallback behavior, and regression evaluations.
Step 6
Reassess deployment economics
Compare serverless spend and constraints with dedicated endpoint cost once utilization becomes steady enough to justify reserved capacity.
Cost
Together AI pricing and free plan
Together AI uses prepaid credits for self-serve access and currently requires a minimum $5 purchase; it does not offer a free trial. Serverless prices vary by model and modality, while dedicated endpoints bill by reserved GPU time. Fine-tuning, storage, clusters, and enterprise arrangements add separate costs.
Self-serve access
$5 minimum credit purchase
Prepaid credits are consumed across eligible Together AI services.
- No current free trial
- Usage reduces the credit balance in real time
- Optional auto-recharge
- Build tiers and some model access depend on cumulative spend
Serverless inference
Usage-based by model
Text models bill by input and output tokens; media models use modality-specific units.
- Current text rates span a wide range by model
- For example, gpt-oss-20B is listed at $0.05 input and $0.20 output per 1M tokens
- Batch workloads can receive discounts on eligible models
- No endpoint provisioning charge
Dedicated endpoints
From $3.99/GPU-hour
Reserved hardware is billed while the endpoint is running, regardless of request volume.
- H100 80GB: $3.99/hour
- H200 141GB: $5.49/hour
- B200 180GB: $9.95/hour
- Replica count and runtime multiply the cost
Scale and Enterprise
Contact sales
Commercial arrangements for higher limits, private deployments, data residency, support, and large compute workloads.
- VPC and private-networking options
- GPU clusters and custom infrastructure
- Contract billing and support terms
- Security and compliance documentation
Pricing checked . Check current pricing at the source ↗
Assessment
Together AI strengths and limitations
Where it stands out
- Broad, frequently updated catalog across text and non-text modalities
- OpenAI-compatible API reduces integration friction for common chat workloads
- Clear migration path from serverless prototyping to dedicated hardware
- Supports fine-tuning, custom models, evaluations, batch jobs, and larger GPU workloads
- Documented privacy defaults and enterprise security options
- Granular usage pricing can be economical when model choice is matched to the task
What to consider
- There is no current free trial, and self-serve use requires at least a $5 credit purchase
- The catalog and prices change, so model IDs and unit economics need ongoing review
- Serverless models have provider and model-specific rate limits and do not guarantee dedicated capacity
- Dedicated endpoints accrue hardware charges while running, including when request volume is low
- Model availability differs between serverless and dedicated deployment modes
- Using a hosted model API does not remove the need for application-level evaluation, security, safety, and fallback design
Compare
Together AI alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Coding
Replicate
A strong alternative for quickly running a large community model catalog with simple per-model APIs, especially for media generation.
Explore Replicate →Coding
Ollama
Better suited to developers who prefer to run supported models locally and control the runtime themselves.
Explore Ollama →Coding
Google AI Studio
A better fit for teams centered on Google's Gemini models and its first-party prototyping and application-building environment.
Explore Google AI Studio →Questions
Together AI FAQs
What is Together AI used for?
It is used to call hosted AI models, process batch jobs, fine-tune supported models, run evaluations, and deploy models on shared or reserved compute. It is aimed primarily at developers and AI teams.
Is Together AI free?
No free trial is currently offered. Together's support documentation says self-serve access requires a minimum $5 credit purchase, after which services consume the prepaid balance according to their current rates.
Is the Together AI API compatible with OpenAI's API?
Together provides an OpenAI-compatible API for common workflows, which can reduce migration work. Model names, supported parameters, limits, and edge behavior still differ, so compatibility should be tested rather than assumed to be complete.
What is the difference between serverless and dedicated inference?
Serverless uses shared capacity and bills by usage, making it useful for prototyping and variable traffic. Dedicated endpoints reserve GPUs, bill by running time, and offer more predictable performance and configuration for steadier workloads.
Does Together AI store prompts or train on customer data?
Together's current documentation says inputs and outputs are not stored by default, temporary caching may be used unless configured otherwise, and sharing data to train other models is opt-in. Teams should still verify contractual settings and configure the account for their own regulatory requirements.
Can Together AI host a custom model?
Yes. Its dedicated endpoints can deploy compatible text-generation or embedding model weights from Hugging Face or a presigned S3 source, subject to architecture and size requirements. Fine-tuned models can also incur separate hosting charges.
How should a team choose a model on Together AI?
Benchmark a small set against real tasks for quality, latency, cost, context needs, structured output, tool use, safety, and availability. The cheapest token rate or best public benchmark is rarely enough to choose a production model.
Bottom line
Our Together AI verdict
Together AI is a compelling infrastructure choice for teams that want open-model flexibility, multimodal APIs, and a credible route from experiments to reserved production capacity. The platform is less suitable for someone seeking a free playground or a finished consumer assistant. Its real value depends on disciplined model evaluation and cost monitoring, because the catalog is broad and the cheapest deployment mode changes with traffic shape.
Visit Together AI website ↗
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