Orchestra
Orchestra helps AI teams cut inference costs by automatically routing each request to the most efficient model and building specialist models when needed. The platform observes production workflows, evaluates candidate models against held-out benchmarks, and ships improvements only when they meet quality, cost, and latency standards. Its inference gateway handles streaming, provider integrations, timeouts, retries, and failure handling for reliable model serving.
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Teams building AI-powered products often default to expensive frontier models for every task, even when simpler or more specialized models would perform equally well. This approach drives up inference costs, increases latency, and makes it difficult to systematically improve model quality based on real production feedback.
Solution
Orchestra optimizes AI spend by automatically identifying the most efficient model for a given task and building specialist models when needed. The platform uses a three-step workflow: it observes production workflows to capture traces and expert corrections, evaluates candidate models against held-out benchmarks, and ships a new model once it meets quality, cost, and latency standards. This allows teams to route each request to the right model while continuously improving performance through targeted training and prompt optimization. The result is a measurable reduction in per-task inference cost without sacrificing output quality.
Target Audience
Primary customers are AI engineering and ML teams at companies running production language-model workloads who want to reduce inference costs and improve task-specific model quality.
Features
- Inference gateway that handles streaming, provider integrations, timeouts, retries, and failure handling for reliable model serving
- Model routing with observability into latency, errors, usage, and cost per task
- Evaluation harness that tests models against held-out examples with reproducible, versioned experiments
- Specialist model training through post-training, fine-tuning, and preference learning based on expert corrections
- Bounded rollout and fallback mechanisms that make introducing new routes safe
- Explicit handling of traces and model artifacts with careful data access boundaries