Langfuse
Helicone AI
LangSmith
LangChain
PromptLayer
Braintrust.dev
Openlayer
Portkey
Google Cloud Speech API
Twilio
Plivo
smooch
Gupshup.io
Nexmo
MessageBird
TeleSign
Langfuse is an open-source LLM engineering platform designed to empower developers by providing insights into user interactions with their LLM applications. We offer tools that help developers understand usage patterns, diagnose issues, and improve application performance based on real user data. By integrating seamlessly into existing workflows, Langfuse streamlines the process of monitoring, debugging, and optimizing LLM applications. Our platform's robust documentation and active community support make it easy for developers to leverage Langfuse for enhancing their LLM projects efficiently. Whether you're troubleshooting interactions or iterating on new features, Langfuse is committed to simplifying your LLM development journey.
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Based on our record, Google Cloud Speech API should be more popular than Langfuse. It has been mentiond 45 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse โ step by step. - Source: dev.to / about 1 month ago
Langfuse is the open-source standard for LLM observability. It traces every LLM interaction โ prompts, completions, latency, token usage, cost โ and provides the tooling to debug, evaluate, and optimize LLM applications in production. Think of it as "Datadog for LLM calls" with a focus on prompt engineering workflows. - Source: dev.to / about 2 months ago
You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / 2 months ago
Gateway or proxy attribution. A reverse proxy in front of the model-provider API records the request, computes the cost, and exposes per-customer breakdowns. Open-source options include Helicone, LiteLLM, Langfuse, and OpenLLMetry. Hosted equivalents serve as the AI cost observability layer for teams that want centralized visibility: LangSmith, Datadog LLM Observability, Arize Phoenix. Adds a network hop.... - Source: dev.to / 2 months ago
Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline โ the metadata.userId pattern is the universal part. - Source: dev.to / 3 months ago
If you want to roll your own solution, you can use APIs like Google Cloud Speech-to-Text or AssemblyAI:. - Source: dev.to / 6 months ago
*- Speech Pipelines * Copilots can generate ready-to-use code for speech-to-text and text-to-speech using APIs like OpenAI Whisper, Azure AI Speech, and Google Cloud Speech-to-Text. For example, a developer can ask for a transcription setup in Python and get working code within seconds โ useful for customer support, meeting notes, or language learning apps. - Source: dev.to / 11 months ago
Google Cloud Speech-to-Text API is a powerful tool for transforming audio into actionable insights. Its accuracy, scalability, and customization options make it a valuable asset for a wide range of applications. By understanding its features, capabilities, and best practices, you can unlock the full potential of speech recognition and build intelligent applications that understand and respond to the world around... - Source: dev.to / about 1 year ago
Google, YouTubeโs parent company, has invested heavily in speech recognition research. Their Cloud Speech-to-Text API is one of the most advanced in the world, and its technology forms the backbone of YouTubeโs captioning system. The API uses neural networks to process audio, identify phonemes (the smallest units of sound), and assemble them into words and sentences. - Source: dev.to / over 1 year ago
Cloud-based speech recognition solutions, such as Google Cloud Speech-to-Text and Microsoft Azure Speech, have gained popularity due to their accessibility, power, and scalability. Developers gain access to ready-to-use APIs with high-quality speech recognition models. However, behind this convenience are several important technical aspects that need to be considered when choosing a cloud solution. - Source: dev.to / over 1 year ago
Helicone AI - Open-source LLM Observability for Developers
Twilio - Brings voice and messaging to your web and mobile applications.
LangSmith - Build and deploy LLM applications with confidence
Plivo - Plivo simplifies your customer engagement.
LangChain - Framework for building applications with LLMs through composability
smooch - Smooch connects your business software to all the worldโs messaging channels for a more human customer experience.