What Together AI does
Together AI offers serverless and dedicated model inference, embeddings, image generation, batch, fine-tuning, custom models, and GPU infrastructure through unified APIs.
Together AI lets a team explore a large open-model catalog without first operating GPUs, then reserve endpoints when traffic or latency becomes predictable. Shared API conventions simplify experiments across models, and fine-tuning connects adaptation to deployment. That convenience can hide meaningful differences: chat templates, tokenizers, quantization, context truncation, tool schemas, safety tuning, multimodal preprocessing, and provider updates affect behavior even when request fields look similar. A replacement with the same benchmark rank can change refusals, dialect performance, JSON reliability, or long-context accuracy and requires a full application evaluation.
Pricing is workload-specific. Serverless text models publish separate input and output token rates; images, embeddings, reranking, video, and other modalities use their own units. Dedicated endpoints charge by GPU minute or hour while capacity is active, and fine-tuning, storage, batch, reserved commitments, and enterprise services have separate rates. Free or promotional credits, model availability, volume terms, taxes, idle capacity, cold starts, minimums, retries, and generated reasoning tokens can affect effective cost. The live pricing page and console are authoritative. Estimate p50 and tail traffic, prompt caching if offered, utilization, failed requests, and the staff cost of operating a dedicated route.
Before sending production traffic, confirm the current data privacy documentation and order terms for serverless, batch, fine-tuning, dedicated, and support workflows rather than assuming one retention rule covers all. Minimize prompts, redact personal and confidential data, keep keys server-side, separate tenants, control exports, and establish deletion, region, and incident procedures. Review each model's license and acceptable use. Build frozen test sets from real requirements rather than public benchmarks alone; measure quality, hallucination, bias, safety, tool behavior, latency, availability, and cost by subgroup; pin identifiers, detect silent change, and keep humans accountable for high-impact outputs.
How Together AI works
Together AI exposes shared serverless models through per-token or output-priced APIs and dedicated endpoints through reserved GPU hardware billed by time. Applications authenticate, select a model ID, submit text, image, embedding, or other supported inputs, and receive synchronous or streaming output and usage metadata. Batch handles asynchronous volume; fine-tuning creates model artifacts that can be served on supported infrastructure. Dedicated endpoints offer predictable capacity but may accrue cost while provisioned. Model catalog, quantization, context, rate limits, features, versions, and pricing vary. Together provides inference infrastructure, while the model license, behavior, prompt design, evaluation, and application controls remain the customer's responsibility.
How to set up Together AI
Map model and deployment requirements
Define modality, quality, context, concurrency, tail latency, region, uptime, license, retention, security, cost, human review, and whether traffic supports dedicated utilization.
Create protected environments
Use separate server-side keys, least privilege, budgets and alerts; verify feature-specific data terms, redact inputs, isolate tenants, and prohibit secrets and unnecessary PII.
Evaluate serverless candidates
Test fixed IDs on representative prompts, languages, subgroups, attacks, structured outputs, tools, long context, and safety, recording tokenizer, quantization, latency, errors, and cost.
Load-test the deployment choice
Compare shared and dedicated routes at realistic concurrency, streaming, bursts, retries, warm-up, utilization, batch, and failure conditions; define timeouts, backoff, and fallbacks.
Govern versions and production
Stage release, require human acceptance, monitor outputs, route, drift, latency, spend, rate limits, incidents, and catalog changes, and rerun evaluations before model or endpoint migration.
Together AI FAQs
How is Together AI inference priced?
Serverless rates depend on model and input/output units; dedicated endpoints depend on hardware time. Batch, fine-tuning, storage, commitments, and enterprise terms are separate.
When should a dedicated endpoint be used?
It can suit steady traffic, fine-tuned models, or predictable capacity, but idle provisioned time and operations can cost more than shared serverless inference.
Are all OpenAI-compatible models interchangeable?
No. Tokenization, prompts, parameters, context, tools, safety tuning, quantization, and output schemas differ. Validate every chosen model and route.
Can benchmark rankings choose the best model?
No. Public benchmarks can be contaminated or unlike the application. Test production-shaped tasks, failure severity, subgroup performance, latency, availability, and total cost.
How should sensitive data be handled?
Verify current feature-specific retention and region terms, minimize and redact inputs, secure keys, separate tenants, control logs and exports, and test deletion and incident processes.
Listing reviewed 2026-07-15. Product details and pricing can change; verify important terms on the provider's website.
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