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Orchestra vs Together AI

Together AI
GPU clusters, inference, and fine-tuning for teams that train and serve open-source models themselves.1,2
Orchestra
Measures every workflow against your definition of success, improves each one, and trains models your team owns.6

Side by side

Orchestra and Together AI compared on 3 dimensions
Primary jobTogether AIInference, fine-tuning, and GPU compute for open-source models. Teams call models through an OpenAI-compatible API, fine-tune them, or launch H100 and B200 clusters.1OrchestraEvaluates production work, improves the prompts and routes behind it, and trains specialist models your team owns.6
TrainingTogether AILoRA or full fine-tuning with supervised or preference (DPO) methods. Post-training runs SFT, DPO, and GRPO from your own code on GPUs you provision.2OrchestraRepairs prompts, schemas, parsers, and tool contracts first. For a recurring task, trains a specialist from accepted traces and expert corrections.6
MeasurementTogether AIAn evaluations API uses an LLM as judge. Your team writes the judge's instructions, labels, and pass threshold.4OrchestraOrchestra does the measurement for you. It builds task-specific benchmarks and compares models on the same held-out work.6

Together AI is built for teams that train their own models.Orchestra does the measurement for you.

Together AI gives technical teams GPU clusters and fine-tuning tools to build models themselves. Deciding which model is right for each workflow is still your team's job.

What Together AI gives your team

  • GPU clusters, code sandboxes, and managed storage for training and large jobs.3,1
  • Fine-tuning from data upload to a dedicated endpoint.2
  • Serverless access to open-source models through an OpenAI-compatible API.1,3
  • An LLM-as-judge evaluations API that your team configures.4

What Orchestra does for you

  • Builds task-specific benchmarks, such as whether the model updated the right record or followed your policy, and compares models on the same held-out work.6
  • Compares candidate models on held-out work for quality, response time, and cost.6
  • Routes to any provider, on or off Together AI.
  • Trains specialist models from accepted traces and expert corrections.6

Where Orchestra is different

  1. Measurement done for you

    With Together AI, your team writes the judge prompts and thresholds. Orchestra builds the benchmarks for each workflow and runs the comparison for you.4,6

  2. Every model, not one platform's

    Together AI serves open-source models on its own GPUs. Orchestra compares and routes across any model or provider.1

  3. Training decided by results

    Orchestra repairs prompts, schemas, parsers, and tool contracts first. For a recurring task, it trains a specialist from accepted traces and expert corrections.6

When each approach makes sense

  • Together AI makes sense when

    • You have a machine learning team that fine-tunes open-source models itself.2
    • You need GPU clusters for training or large batch jobs.1
    • You already run other GPU platforms alongside it, such as Fireworks or Baseten.

  • Orchestra makes sense when

    • You want to optimize and measure across every model, not one platform's.
    • You want the measurement done for you.
    • You need to route to any provider, on or off Together AI.
    • You want to improve prompts and routes, then train a specialist you own.6

Orchestra case studies

  • Orchestra case study

    Teaching a CRM agent to finish the job

    75.5%

    lower model cost than Sonnet 4.6

    Model spend per seven-task AutomationBench CRM run: $0.27 for Qwen 3.6 Plus with a revised prompt, $1.12 for Sonnet 4.6. The revised prompt also scored 13.1% higher on mean task score.

    Read the story
  • Orchestra case study

    Cutting response times for everyday operations

    5.2×

    faster median model response than Sonnet 4.6

    The small model passed every task check on 92.2% of attempts; Sonnet passed 100%. Both ran 30 AutomationBench workflows, with 90 attempts per model.

    Read the story
  • Orchestra case study

    Finding purchase intent in 40,000 comments

    $2.82

    model cost for 39,962 comments

    An Orchestra post-trained 30B open model labeled 39,962 YouTube comments from a Snowflake table and returned 39,949 valid outputs. Sonnet cost $12.48 on the same table.

    Read the story

These are Orchestra experiments that compare models on specific workloads. None of them compare Orchestra with Together AI.

Sources (9)Show

Sources reviewed October 2026. Together AI statements come from its own website and documentation. If something is not described there, we say so instead of assuming it is missing.

  1. 1.Together AI docs: Overview docs.together.ai/docs/introduction (opens in a new tab)
  2. 2.Together AI docs: Fine-tuning overview docs.together.ai/docs/fine-tuning-overview (opens in a new tab)
  3. 3.Together AI homepage www.together.ai (opens in a new tab)
  4. 4.Together AI docs: Evaluations docs.together.ai/docs/ai-evaluations (opens in a new tab)
  5. 5.Orchestra homepage
  6. 6.Orchestra platform
  7. 7.Case study: Teaching a CRM agent to finish the job
  8. 8.Case study: Cutting response times for everyday operations
  9. 9.Case study: Finding purchase intent in 40,000 comments

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