Side by side
| Dimension | Applied Compute | Orchestra |
|---|---|---|
| Primary job | Applied ComputeTrains custom models on a company's data, serves them, and keeps improving them from production use. AC2, the platform behind this work, is in private beta.1,2 | OrchestraEvaluates production work, improves the prompts and routes behind it, and trains specialist models your team owns.4 |
| Who it serves | Applied ComputeEngineering and machine learning teams building a custom model for their product.1 | OrchestraEvery department, from technical and non-technical product teams to engineering and machine learning teams. |
| How the work gets done | Applied ComputeEmbedded: its researchers sit with your engineers from the first eval to deployment. Managed: your team runs experiments and Applied Compute runs training, serving, and infrastructure.1 | OrchestraAutomated across workflows, with little hands-on work, in under seven days. Your existing OpenAI or Anthropic SDK points at Orchestra's gateway, and approved changes roll out gradually with the prior route kept as a fallback.4 |
| Evaluation approach | Applied ComputeTrains toward your evals using your own harness and graders, with rewards calibrated to your product KPIs.1 | OrchestraBuilds 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.4 |
| Model training | Applied ComputePost-trains open models with reinforcement learning. Production traces feed later runs through online RL and self-distillation.1,2 | OrchestraTrains specialist models from accepted traces and expert corrections.4 |
Applied Compute pairs software with embedded researchers.Orchestra brings specialized AI to every team.
Applied Compute's platform usually arrives with its researchers working inside your engineering team. Orchestra measures and improves AI work across the company without that dependency.
What Applied Compute offers
- Embedded: its researchers sit with your engineers, from co-designing the first eval to deploying the model.1
- Managed: your team designs and runs experiments, and Applied Compute handles training, serving, and infrastructure.1
- AC2, the platform its own researchers use, in private beta for teams that train and serve open models themselves.2
- Serverless or VPC deployment.1
What Orchestra offers
- Support for every department, technical and non-technical.
- 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.4
- Repairs prompts, schemas, parsers, and tool contracts first. For a recurring task, trains a specialist from accepted traces and expert corrections.4
- Approved changes roll out gradually, with the prior route kept as a fallback.4
Where Orchestra is different
A platform for every team
Technical and non-technical product teams, engineering, and machine learning teams all work in Orchestra. Applied Compute centers on the engineers building one custom model.1
Built for scale
Orchestra measures, improves, and trains for every workflow automatically, with little hands-on work from people, in under seven days. Progress does not depend on embedded researchers for each project.4
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.4
When each approach makes sense
Applied Compute makes sense when
Orchestra makes sense when
- You want specialized models across many workflows, not one project at a time.
- Teams across the company, technical and non-technical, rely on AI in production.
- You know what you want from the work, and every change is checked against that goal on your own examples.4
- You want to improve prompts and routes, then train a specialist you own.4
Both can be part of the stack when
- Applied Compute builds a custom model and Orchestra serves your work to it.
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.
- 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.
- Orchestra case study
Finding purchase intent in 40,000 comments
$2.82model 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.
These are Orchestra experiments that compare models on specific workloads. None of them compare Orchestra with Applied Compute.
Sources (7)ShowHide
Sources reviewed October 2026. Applied Compute statements come from its own website and documentation. If something is not described there, we say so instead of assuming it is missing.
- 1.Applied Compute homepage appliedcompute.com (opens in a new tab)
- 2.Applied Compute: Introducing the Applied Compute Agent Cloud (AC2) appliedcompute.com/platform/introducing-ac2 (opens in a new tab)
- 3.Orchestra homepage
- 4.Orchestra platform
- 5.Case study: Teaching a CRM agent to finish the job
- 6.Case study: Cutting response times for everyday operations
- 7.Case study: Finding purchase intent in 40,000 comments
Bring one workflow. See how it scores.
Pick a recurring task. We'll define what good looks like and compare models on your own examples.