Athina AI Alternatives

Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison and optimal prompt selection. Hallucinations have plagued LLMs since their inception. By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users.

Guardrails are crucial in AI maintenance, LangWatch safeguards you and your business from exposing sensitive data, prompt injection and keeps your AI from going off the rails, avoiding unforeseen damage to your brand. Understanding the behaviour of both AI and users can be challenging for businesses with integrated AI. Ensure accurate and appropriate responses by constantly maintaining quality through oversight. LangWatch’s safety checks and guardrails prevent common AI issues including jailbreaking, exposing sensitive data, and off-topic conversations. Track conversion rates, output quality, user feedback and knowledge base gaps with real-time metrics — gain constant insights for continuous improvement. Powerful data evaluation allows you to evaluate new models and prompts, develop datasets for testing and run experimental simulations on tailored builds.

Capture production data, evaluate outputs collaboratively, and fine-tune your LLM's performance. Know exactly what goes on in production with an in-depth log overview. Collaborate with product managers, domain experts and engineers to build reliable model outputs. Track AI metrics such as speed, quality scores, and token usage. Copilot automates evaluations and model improvements for your use case. Create, manage, and optimize prompts to achieve precise and relevant interactions between users and AI models. Compare foundation models, and fine-tuned versions to improve prompt performance and save tokens. Collaborate with your team to build a proprietary fine-tuning dataset for your AI models. Build custom fine-tuning datasets to optimize model performance for specific use cases.

Get the full picture of your model's performance. Log inputs and outputs and seamlessly enrich them with metadata and user feedback. Figure out how your model is really working, and where you can improve. Monitor for errors and discover underperforming cohorts and use cases. The best models are built on user data. Programmatically gather unusual or underperforming examples to retrain your model. Stop manually reviewing thousands of outputs when changing your prompt or model. Evaluate your LLM-powered apps programmatically. Detect and fix degradations quickly. Monitor new deployments in real-time and seamlessly edit the version of your app your users interact with. Connect your self-hosted or third-party model and your existing data sources. Process enterprise-scale data with our serverless streaming dataflow engine. Gantry is SOC-2 compliant and built with enterprise-grade authentication.

Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents. Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration. Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks.

Bring LLM-powered features to production with tools for prompt engineering, semantic search, version control, quantitative testing, and performance monitoring. Compatible across all major LLM providers. Quickly develop an MVP by experimenting with different prompts, parameters, and even LLM providers to quickly arrive at the best configuration for your use case. Vellum acts as a low-latency, highly reliable proxy to LLM providers, allowing you to make version-controlled changes to your prompts – no code changes needed. Vellum collects model inputs, outputs, and user feedback. This data is used to build up valuable testing datasets that can be used to validate future changes before they go live. Dynamically include company-specific context in your prompts without managing your own semantic search infra.

Langtail is an end-to-end platform that accelerates the development and deployment of language model (LLM) applications. It enables companies to rapidly experiment, collaborate, and launch production-grade LLM products. Key features include: 1. No-code LLM playground for prompt debugging and ideation 2. Collaborative workspaces for sharing prompts and insights 3. Comprehensive observability suite with logging and analytics 4. Evaluation framework for systematically testing prompt performance 5. Deployment infrastructure for serving prompts via API in multiple environments 6. Upcoming fine-tuning capabilities to improve models with user feedback Langtail empowers both technical and non-technical teams to find high-value LLM use cases, refine prompts for reliable performance, and deploy applications with ease. It's the all-in-one platform to take your LLM projects from prototype to production faster than ever.

Klu.ai is a Generative AI platform that simplifies the process of designing, deploying, and optimizing AI applications. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context. Klu accelerates building applications using language models like Anthropic Claude, Azure OpenAI, GPT-4, and over 15 other models, allowing rapid prompt/model experimentation, data gathering and user feedback, and model fine-tuning while cost-effectively optimizing performance. Ship prompt generations, chat experiences, workflows, and autonomous workers in minutes. Klu provides SDKs and an API-first approach for all capabilities to enable developer productivity. Klu automatically provides abstractions for common LLM/GenAI use cases, including: LLM connectors, vector storage and retrieval, prompt templates, observability, and evaluation/testing tooling.

Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.

No more file restrictions and complicated templates. Easily integrate LLMs like ChatGPT with vector databases, PDF OCR, and more. Chain prompts and transformations to build tailor-made AI experiences, from templates to adaptive chains. Prevent hallucinations and save money through our unique LLM features such as quality control, semantic cache, and more. We take care of your infrastructure management, hosting, and scaling. Relevance AI does the heavy lifting for you, in minutes. It can flexibly extract from all sorts of unstructured data out of the box. With Relevance AI, the team can extract with over 90% accuracy in under an hour.​ Add the ability to automatically group data by similarity with vector-based clustering.

Release high-quality LLM apps quickly without compromising on testing. Never be held back by the complex and subjective nature of LLM interactions. Generative AI produces subjective results. Knowing whether a generated text is good usually requires manual labor by a subject matter expert. If you’re working on an LLM app, you probably know that you can’t release it without addressing countless constraints and edge-cases. Hallucinations, incorrect answers, bias, deviation from policy, harmful content, and more need to be detected, explored, and mitigated before and after your app is live. Deepchecks’ solution enables you to automate the evaluation process, getting “estimated annotations” that you only override when you have to. Used by 1000+ companies, and integrated into 300+ open source projects, the core behind our LLM product is widely tested and robust. Validate machine learning models and data with minimal effort, in both the research and the production phases.

Query and compare a large selection of open-source and proprietary models at once. Replace costly APIs with cheap custom AI models. Customize foundational models on your private data to adapt them to your particular use case. Small fine-tuned models can perform on par with GPT-4 and are up to 90% cheaper. Airtrain’s LLM-assisted scoring simplifies model grading using your task descriptions. Serve your custom models from the Airtrain API in the cloud or within your secure infrastructure. Evaluate and compare open-source and proprietary models across your entire dataset with custom properties. Airtrain’s powerful AI evaluators let you score models along arbitrary properties for a fully customized evaluation. Find out what model generates outputs compliant with the JSON schema required by your agents and applications. Your dataset gets scored across models with standalone metrics such as length, compression, coverage.

OpenPipe provides fine-tuning for developers. Keep your datasets, models, and evaluations all in one place. Train new models with the click of a button. Automatically record LLM requests and responses. Create datasets from your captured data. Train multiple base models on the same dataset. We serve your model on our managed endpoints that scale to millions of requests. Write evaluations and compare model outputs side by side. Change a couple of lines of code, and you're good to go. Simply replace your Python or Javascript OpenAI SDK and add an OpenPipe API key. Make your data searchable with custom tags. Small specialized models cost much less to run than large multipurpose LLMs. Replace prompts with models in minutes, not weeks. Fine-tuned Mistral and Llama 2 models consistently outperform GPT-4-1106-Turbo, at a fraction of the cost. We're open-source, and so are many of the base models we use. Own your own weights when you fine-tune Mistral and Llama 2, and download them at any time.

Apply advanced coding and language models to a variety of use cases. Leverage large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. Apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. Detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security. Gain access to generative models that have been pretrained with trillions of words. Apply them to new scenarios including language, code, reasoning, inferencing, and comprehension. Customize generative models with labeled data for your specific scenario using a simple REST API. Fine-tune your model's hyperparameters to increase accuracy of outputs. Use the few-shot learning capability to provide the API with examples and achieve more relevant results.

Explore the power of choosing the right LLM for your needs and how to optimize for quality, speed, and cost-efficiency. Access all LLMs across all providers with a single API key and a standard API. Setup your own cost, latency, and output speed constraints. Define a custom quality metric. Personalize your router for your requirements. Systematically send your queries to the fastest provider, based on the very latest benchmark data for your region of the world, refreshed every 10 minutes. Get started with Unify with our dedicated walkthrough. Discover the features you already have access to and our upcoming roadmap. Just create a Unify account to access all models from all supported providers with a single API key. Our router balances output quality, speed, and cost based on user-specific preferences. The quality is predicted ahead of time using a neural scoring function, which predicts how good each model would be at responding to a given prompt.

The open-source ML observability platform. Evaluate, test, and monitor ML models from validation to production. From tabular data to NLP and LLM. Built for data scientists and ML engineers. All you need to reliably run ML systems in production. Start with simple ad hoc checks. Scale to the complete monitoring platform. All within one tool, with consistent API and metrics. Useful, beautiful, and shareable. Get a comprehensive view of data and ML model quality to explore and debug. Takes a minute to start. Test before you ship, validate in production and run checks at every model update. Skip the manual setup by generating test conditions from a reference dataset. Monitor every aspect of your data, models, and test results. Proactively catch and resolve production model issues, ensure optimal performance, and continuously improve it.

Confident AI offers an open-source package called DeepEval that enables engineers to evaluate or "unit test" their LLM applications' outputs. Confident AI is our commercial offering and it allows you to log and share evaluation results within your org, centralize your datasets used for evaluation, debug unsatisfactory evaluation results, and run evaluations in production throughout the lifetime of your LLM application. We offer 10+ default metrics for engineers to plug and use.

Superpowered AI is an end-to-end knowledge retrieval solution purpose-built for LLM applications. We turn complex infrastructure into a few API calls. Give your LLMs access to private information that wasn’t in its training data, like internal company documents. Store old messages in a Knowledge Base and retrieve the most relevant ones each time the user sends a new message. Reduce hallucinations by putting relevant factual information directly in your prompts and instructing the LLM to only use the information it’s been given. Using a knowledge retrieval solution like Superpowered AI lets you retrieve the right information and insert it into your LLM prompts, enabling you to deliver highly relevant responses to your users. Create a knowledge base directly from local files and folders, or from a URL, and query via REST API, all in less than 10 lines of code. State-of-the-art multi-stage knowledge retrieval pipeline to give you the most relevant results.

The prompt engineering platform to experiment with different prompt versions, evaluate and compare prompts across a suite of tests, optimize prompts with one-click, share, and more. Optimize your AI development workflow. Key features to help you get and identify the best prompts for your production use cases. Side-by-side comparison of prompts across test cases with evaluation. CSV import test cases, and define custom evaluation metrics. Improve LLM results with automatic prompt and template optimization. View and manage all prompt versions and create OpenAI functions. Access all of your prompts programmatically, including observability and analytics. Determine the costs, latency, and efficacy of each prompt. Start enhancing your prompt engineering workflow with Parea today. Parea makes it easy for developers to improve the performance of their LLM apps through rigorous testing and version control.

Ship AI faster with managed, cloud-hosted AI packages. Full, built-in support for GPT-4. No API tokens are necessary. Build with our low code framework. Integrations with all major models are built-in. Deploy for an instant API. Scale and share without managing infrastructure. Turn prompts, prompt chains, and basic Python into a managed API. Turn a clever prompt into a published API you can share. Add logic and routing smarts with Python. Steamship connects to your favorite models and services so that you don't have to learn a new API for every provider. Steamship persists in model output in a standardized format. Consolidate training, inference, vector search, and endpoint hosting. Import, transcribe, or generate text. Run all the models you want on it. Query across the results with ShipQL. Packages are full-stack, cloud-hosted AI apps. Each instance you create provides an API and private data workspace.

Whether prompt engineering, fine-tuning, or training, we are ready to meet your business demands. Easily integrate your new model into your production application using the Together Inference API. With the fastest performance available and elastic scaling, Together AI is built to scale with your needs as you grow. Inspect how models are trained and what data is used to increase accuracy and minimize risks. You own the model you fine-tune, not your cloud provider. Change providers for whatever reason, including price changes. Maintain complete data privacy by storing data locally or in our secure cloud.

Prototype and productionize generative AI apps, built for engineers, not just ML practitioners. No more switching between platforms or wrestling with different APIs, focus on creating, not configuring. Use a familiar interface to prompt engineer and work with AI. Use parameters to easily streamline your workbooks into reusable templates. Create workflows by chaining model outputs from LLMs, image, and audio models. Create organizations to manage workbooks amongst your teammates. Share your workbook to the public or specific organizations you define with your team. Comment on workbooks and easily review and compare workbooks with your team. Develop templates for yourself, your team, or the broader developer community, get started quickly with templates to see what people are building.

Open source is the core of Flowise, and it will always be free for commercial and personal usage. Build LLMs apps easily with Flowise, an open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. Open source MIT license, see your LLM apps running live, and manage custom component integrations. GitHub repo Q&A using conversational retrieval QA chain. Language translation using LLM chain with a chat prompt template and chat model. Conversational agent for a chat model which utilizes chat-specific prompts and buffer memory.

On our platform, you can quickly configure a pipeline, made up of layers (models providing computer vision functionality) that pass the output of one to the next, to create your desired functionality by layering our prebuilt functionality to match your desired behavior. If you have a niche case that our versatile prebuilts don’t encompass, either reach out to us and we will add it for you, or use our custom model creation to create it and add it to the pipeline yourself. Then easily integrate into your app with the ezML libraries implemented in a variety of frameworks/languages that support the most basic cases as well as realtime streaming with TCP, WebRTC, and RTMP. Deployments auto-scale to meet your product's demand, ensuring uninterrupted functionality no matter how big your user base grows.

Achieve peak model performance with the best data. Create & manage training data for any visual modality, debug models and boost performance, and make foundation models your own. Expert review, QA and QC workflows help you deliver higher quality datasets to your artificial intelligence teams, helping improve model performance. Connect your data and models with Encord's Python SDK and API access to create automated pipelines for continuously training ML models. Improve model accuracy by identifying errors and biases in your data, labels and models.

Depending on the use case, pick an appropriate app type or template. Provide input and configuration required for the app, and choose how the output is rendered. Add your existing data from sources like files, website URLs, sitemaps, YouTube links, Google Drive, and Notion exports. Attach these data sources to your app in the app builder. Save and publish your app. Access it from the dedicated app page or embed it in your website using the embed code. Use our APIs to run the app from your applications. Promptly provides embeddable widgets that you can easily integrate into your website. Use these widgets to build conversational AI applications or to add a chatbot to your website. Customize the chatbot look and feel to match your website, including a logo for the bot.

Make AI a trusted part of your business. Build better, deploy faster, and audit easily with the flexibility of a cloud-native platform and the power to deploy anywhere. Easily import models and search cataloged models from across your organization. Save time by annotating data rapidly with model-in-the-loop hinting. Understand the full provenance of your data, models, workflows, and inferences. Deploy models where you need them, including for edge and IoT use cases. Getting valuable insights from your data is not just for data scientists. With Chariot’s low-code interface, meaningful collaboration can take place across teams. Train models rapidly using your organization's production data. Deploy models with one click and monitor models in production at scale.

LlamaIndex is a “data framework” to help you build LLM apps. Connect semi-structured data from API's like Slack, Salesforce, Notion, etc. LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. LlamaIndex provides the key tools to augment your LLM applications with data. Connect your existing data sources and data formats (API's, PDF's, documents, SQL, etc.) to use with a large language model application. Store and index your data for different use cases. Integrate with downstream vector store and database providers. LlamaIndex provides a query interface that accepts any input prompt over your data and returns a knowledge-augmented response. Connect unstructured sources such as documents, raw text files, PDF's, videos, images, etc. Easily integrate structured data sources from Excel, SQL, etc. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.

Discover. Analyze. Optimize. Use [impaction.ai]'s intuitive semantic search to effortlessly sift through conversational data. Just type 'find me conversations where...' and let our engine do the rest. Meet Columbus, your intelligent data co-pilot. Columbus analyzes conversations, highlights key trends, and even recommends which dialogues deserve your attention. Armed with these insights, take data-driven actions to enhance user engagement and build a smarter, more responsive AI product. Columbus not only tells you what's happening but also suggests how to make it better.

Natural Language Processing is Artificial Intelligence that enables computers to analyze and understand human language. Pryon’s AI is trained to perform read, organize and search in ways that previously required humans. This powerful capability is used in every interaction, both to understand a request and to retrieve the accurate response. The success of any NLP project is directly correlated to the sophistication of the underlying natural language technologies used. To make your content ready for use in chatbots, search, automations, etc. – it must be broken into specific pieces so a user can get the exact answer, result or snippet needed. This can be done manually as when a specialist breaks information into intents and entities. Pryon creates a dynamic model of your content for automatically identifying and attaching rich metadata to each piece of information. When you need to add, change or remove content this model is regenerated with a click.

Build better AI with multiple models. AI models are a new kind of building block. Sieve is the easiest way to use these building blocks to understand audio, generate video, and much more at scale. State-of-the-art models in just a few lines of code, and a curated set of production-ready apps for many use cases. Import your favorite models like Python packages. Visualize results with auto-generated interfaces built for your entire team. Deploy custom code with ease. Define your environment compute in code, and deploy with a single command. Fast, scalable infrastructure without the hassle. We built Sieve to automatically scale as your traffic increases with zero extra configuration. Package models with a simple Python decorator and deploy them instantly. A full-featured observability stack so you have full visibility of what’s happening under the hood. Pay only for what you use, by the second. Gain full control over your costs.

Braintrust is the enterprise-grade stack for building AI products. From evaluations, to prompt playground, to data management, we take uncertainty and tedium out of incorporating AI into your business. Compare multiple prompts, benchmarks, and respective input/output pairs between runs. Tinker ephemerally, or turn your draft into an experiment to evaluate over a large dataset. Leverage Braintrust in your continuous integration workflow so you can track progress on your main branch, and automatically compare new experiments to what’s live before you ship. Easily capture rated examples from staging & production, evaluate them, and incorporate them into “golden” datasets. Datasets reside in your cloud and are automatically versioned, so you can evolve them without the risk of breaking evaluations that depend on them.

Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, and HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands.

Use Prompt Mixer to create prompts and chains. Combinе your chains with datasets and improve with AI. Develop a comprehensive set of test scenarios to assess various prompt and model pairings, determining the optimal combination for diverse use cases. Incorporate Prompt Mixer into your everyday tasks, from creating content to conducting R&D. Prompt Mixer can streamline your workflow and boost productivity. Use Prompt Mixer to efficiently create, assess, and deploy content generation models for various applications such as blog posts and emails. Use Prompt Mixer to extract or merge data in a completely secure manner and easily monitor it after deployment.

GradientJ provides everything you need to build large language model applications in minutes and manage them forever. Discover and maintain the best prompts by saving versions and comparing them across benchmark examples. Orchestrate and manage complex applications by chaining prompts and knowledge bases into complex APIs. Enhance the accuracy of your models by integrating them with your proprietary data.

Fine-tuning happens when you take a dataset and build out a model to use with AI. It isn't always easy to do this without code so we built a solution into RIku which handles everything in a very simple format. Fine-tuning unlocks a whole new level of power for AI and we're excited to help you explore it. Public Share Links are individual landing pages that you can create for any of your prompts. You can design these with your brand in mind in terms of colors and adding a logo and your own welcome text. Share these links with anyone publicly and if they have the password to unlock it, they will be able to make generations. A no-code writing assistant builder on a micro scale for your audience! One of the big headaches we found with projects using multiple large language models is that they all return their outputs slightly differently.

Delight your enterprise accounts with instant answers & insights from their business data. Balancing accuracy, latency, and costs is a nightmare and you are racing against time for the launch. SaaS teams use pre-configured agents or add custom rulebooks to create state-of-the-art copilots and deploy securely. User asks a question to our agents in simple english and gets output in smart insights and visualisation formats. Our advanced LLMs automatically detects & generates all your proactive insights. Our advanced LLMs automatically detects & priortize & executes action for you.

Fireworks partners with the world's leading generative AI researchers to serve the best models, at the fastest speeds. Independently benchmarked to have the top speed of all inference providers. Use powerful models curated by Fireworks or our in-house trained multi-modal and function-calling models. Fireworks is the 2nd most used open-source model provider and also generates over 1M images/day. Our OpenAI-compatible API makes it easy to start building with Fireworks. Get dedicated deployments for your models to ensure uptime and speed. Fireworks is proudly compliant with HIPAA and SOC2 and offers secure VPC and VPN connectivity. Meet your needs with data privacy - own your data and your models. Serverless models are hosted by Fireworks, there's no need to configure hardware or deploy models. Fireworks.ai is a lightning-fast inference platform that helps you serve generative AI models.

Freeplay gives product teams the power to prototype faster, test with confidence, and optimize features for customers, take control of how you build with LLMs. A better way to build with LLMs. Bridge the gap between domain experts & developers. Prompt engineering, testing & evaluation tools for your whole team.

By using the best-performing model for each request, we can achieve higher performance than any single model. Martian outperforms GPT-4 across OpenAI's evals (open/evals). We turn opaque black boxes into interpretable representations. Our router is the first tool built on top of our model mapping method. We are developing many other applications of model mapping including turning transformers from indecipherable matrices into human-readable programs. If a company experiences an outage or high latency period, automatically reroute to other providers so your customers never experience any issues. Determine how much you could save by using the Martian Model Router with our interactive cost calculator. Input your number of users, tokens per session, and sessions per month, and specify your cost/quality tradeoff.

Focus on building, we’ll take care of hosting. Just plug and play with our rest API. No limits, no headaches. We have all the inferencing capacity you need. Stop paying for nothing. We’ll only charge based on use. Our support team is our tech team too. So you’ll be getting support directly rather than jumping through hoops. The flexible infrastructure allows us to scale with you as you grow and handle any spikes in activity. Image and art generation from text to image or image to image with clear documentation with our stable diffusion API. Change the output's art style with additional models. MJ v4, Anything v3, Analog, Redshift, and more. Other stable diffusion versions like 2.0+ will also be included. Train your own stable diffusion model (fine-tuning) and deploy on Evoke as an API. We plan to have other models like Whisper, Yolo, GPT-J, GPT-NEOX, and many more in the future for not only inference but also training and deployment.

Native support for ChatGPT and LangChain. Bing, HuggingFace and more coming soon. Add your API documentation manually or import an existing OpenAPI specification. Access the request prompt, parameters, headers, body and more. Inspect detailed live metrics about how your plugin is performing, including latencies, errors, and more. Configure your own dashboards, track funnels and aggregated metrics.

It streamlines development and deployment, reduces cloud costs, and frees users from vendor lock-in. Configure the hardware resources, such as GPU, and memory, and specify your preference for using spot instances. dstack automatically provisions cloud resources, fetches your code, and forwards ports for secure access. Access the cloud dev environment conveniently using your local desktop IDE. Configure the hardware resources you need (GPU, memory, etc.) and indicate whether you want to use spot or on-demand instances. dstack will automatically provision cloud resources and forward ports for secure and convenient access. Pre-train and finetune your own state-of-the-art models easily and cost-effectively in any cloud. Have cloud resources automatically provisioned based on your configuration? Access your data and store output artifacts using declarative configuration or the Python SDK.

We’ve included some common examples, but the list is far from exhaustive. Our solution engineering team will work with you to customize Kolena for your workflows and your business metrics. Aggregate metrics don't tell the full story — unexpected model behavior in production is the norm. Current testing processes are manual, error-prone, and unrepeatable. Models are evaluated on arbitrary statistical metrics that align imperfectly with product objectives. ‍ Tracking model improvement over time as the data evolves is difficult and techniques sufficient in a research environment don't meet the demands of production.

Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies. Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.

Deeploy helps you to stay in control of your ML models. Easily deploy your models on our responsible AI platform, without compromising on transparency, control, and compliance. Nowadays, transparency, explainability, and security of AI models is more important than ever. Having a safe and secure environment to deploy your models enables you to continuously monitor your model performance with confidence and responsibility. Over the years, we experienced the importance of human involvement with machine learning. Only when machine learning systems are explainable and accountable, experts and consumers can provide feedback to these systems, overrule decisions when necessary and grow their trust. That’s why we created Deeploy.

Use BenchLLM to evaluate your code on the fly. Build test suites for your models and generate quality reports. Choose between automated, interactive or custom evaluation strategies. We are a team of engineers who love building AI products. We don't want to compromise between the power and flexibility of AI and predictable results. We have built the open and flexible LLM evaluation tool that we have always wished we had. Run and evaluate models with simple and elegant CLI commands. Use the CLI as a testing tool for your CI/CD pipeline. Monitor models performance and detect regressions in production. Test your code on the fly. BenchLLM supports OpenAI, Langchain, and any other API out of the box. Use multiple evaluation strategies and visualize insightful reports.

No-code, in minutes—inject AI prompts into your own website, your data, your workflow. We support the latest generative models and are always adding more. Use GPT4, ChatGPT, GPT3, DALL-E 2, Stable Diffusion, and more! Train AI to use your PDF, website, or document as context for its responses. Customize Pickaxes and embed them on your website, bring them into Google sheets, or access through our API

Construct your model directly as a live, interactive graph. Preview data flowing through your visualized model architecture. View and edit your visualized model architecture down to the atomic level. Graphbook provides X-ray transparency with no black boxes. Graphbook live checks data type and shape with understandable error messages, making your model debugging quick and easy. Abstracting out software dependencies and environment configuration, Graphbook allows you to focus on model architecture and data flow with the handy computing resources needed. Cerbrec Graphbook is a visual IDE for AI modeling, transforming cumbersome model development into a user-friendly experience. With a growing community of machine learning engineers and data scientists, Graphbook helps developers work with their text and tabular data to fine-tune language models such as BERT and GPT. Everything is fully managed out of the box so you can preview your model exactly as it will behave.

Embed and store documents, images, and more. High-performance retrieval workflows with no additional work. Connect your data via the UI or API. Baseplate handles embedding, storage, and version control so your data is always in-sync and up-to-date. Hybrid Search with custom embeddings tuned for your data. Get accurate results regardless of the type, size, or domain of the data you're searching through. Prompt any LLM with data from your database. Connect search results to a prompt through the App Builder. Deploy your app with a few clicks. Collect logs, human feedback, and more using Baseplate Endpoints. Baseplate Databases allow you to embed and store your data in the same table as the images, links, and text that make your LLM App great. Edit your vectors through the UI, or programmatically. We version your data so you never have to worry about stale data or duplicates.