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OpenPipe pricing, features, company info, and alternatives
A factual product page for OpenPipe as a platform for logging, fine-tuning, evaluating, and hosting LLMs.
Last updated April 2026 · Pricing and features verified against official documentation
Pricing
Current public pricing tiers on file for OpenPipe, last verified Apr 23, 2026.
Training
From $0.48 / 1M tokens
Training pricing is based on model size; 8B and smaller models start at $0.48 per 1M tokens and 70B+ models at $2.90 per 1M tokens.
Hosted inference
From $0.30 / 1M input tokens
Per-token pricing is listed for select hosted models, with output pricing shown separately.
Hourly compute units
From $1.50 / CU hour
Hourly billing is listed for lower-volume or experimental models, with rates varying by model.
Enterprise
Custom
Custom solutions include volume discounts, on-premises deployment, dedicated support, SLAs, and extra security features.
What You Can Do With It
The main capabilities that shape how people use OpenPipe today.
Captures request and response logs, with tagging, filtering, export, and JSONL import for training data.
Supports supervised fine-tuning, DPO, and reward-model workflows built from datasets or logged traffic.
Hosts trained models on serverless, hourly, or dedicated deployments and exposes them through OpenAI-compatible clients.
Includes evaluations, criteria-based scoring, and fallback handling for production model workflows.
Best For
Who OpenPipe is most clearly built for.
Product teams that want to train custom LLMs from production traffic.
Developers who want one system for logging, fine-tuning, serving, and evaluating models.
Teams standardizing on OpenAI-compatible SDKs and API shapes.
Platforms
Where you can use OpenPipe today.
Web app
API
Python SDK
Node.js SDK
Access
How to integrate or build around OpenPipe.
Public API
Yes
Docs
Available
Alternatives
Other tools worth considering alongside OpenPipe.
AI observability and evaluation platform for tracing, scoring, and improving production AI applications.
Open-source LLM engineering platform for tracing, prompt management, evaluations, and analytics.
Framework-agnostic platform for observability, evaluation, and deployment of AI agents and LLM apps.
Developer platform for running, fine-tuning, and deploying open models.
Product Snapshot
OpenPipe is a web and API platform for collecting LLM request logs, building datasets, fine-tuning models, hosting deployments, and running evaluations. The public docs frame it around product teams that want to turn their own traffic into specialized models.
What You Can Do With It
- Capture request and response logs, tag them for filtering, and export them as JSONL for later training.
- Import existing training data from OpenAI-compatible JSONL files or create datasets from logged requests.
- Train supervised fine-tuned models, DPO models, and reward models from those datasets.
- Deploy trained models on serverless, hourly, or dedicated infrastructure and query them through OpenAI-compatible clients.
Why It Stands Out
It combines logging, dataset building, fine-tuning, hosting, and evaluation in one product, with SDKs and API routes that are meant to fit into OpenAI-style codebases.
Tradeoffs To Know
- Pricing is split across training, hosted inference, hourly compute units, and enterprise contracts, so the bill depends on which workflow you use.
- Some capabilities are documented as beta or preview features, including criteria access and caching.
- The public materials are developer-first, so this is mainly a web and API workflow rather than a consumer app surface.
Sources
- docs.openpipe.ai/pricing/pricing
- status.openpipe.ai
- docs.openpipe.ai
- docs.openpipe.ai/overview
- docs.openpipe.ai/getting-started/quick-start
- docs.openpipe.ai/getting-started/openpipe-sdk
- docs.openpipe.ai/features/datasets/overview
- docs.openpipe.ai/features/fine-tuning
- docs.openpipe.ai/features/deployments
- docs.openpipe.ai/features/evaluations
- docs.openpipe.ai/features/criteria
- docs.openpipe.ai/base-models
- docs.openpipe.ai/api-reference/post-chatcompletions
- docs.openpipe.ai/api-reference/post-createDataset
- docs.openpipe.ai/api-reference/get-listModels
- docs.openpipe.ai/api-reference/get-getModel