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2023
The large language model
operations (LLMOps)
market map
LLMOps | 2
LLMOps | 3
From prompt engineering to machine learning
security, we break down the vendors helping
enterprises deploy large language models.
The launch of OpenAI’s ChatGPT sparked an unprecedented surge of
corporate interest in large language models (LLMs).
Big tech companies and research institutes are democratizing access to
LLMs, enabling enterprises to more easily take advantage of deep learning
capabilities. As enterprises adopt these models, they are focused on
improving their data infrastructure, fine-tuning models for specific use
cases, and monitoring LLMs for hallucination and bias.
As a result, tech vendors supporting large language model operations
(LLMOps) — the end-to-end workflow that organizations employ to build,
fine-tune, and deploy LLMs into production — are gaining traction.
In the market map below, we identify 90+ companies across 12 different
categories helping enterprises bring LLM projects from start to finish.
Note: Our map includes public, private, and recently exited companies in
LLMOps. This market map is not exhaustive of the space.
LLMOps | 1
LLMOps | 2
Market comparisons
LLMOps | 3
Market descriptions
Generative AI — large language model developers
The generative AI — large language model developers market offers
foundation models and APIs that enable enterprises to build natural
language processing applications such as content creation,
summarization, classification, chatbots, sentiment analysis, and more.
Enterprises can fine-tune and customize these large-scale language
models — pre-trained on vast amounts of text — for their specific use
cases.
Equity funding 2023 YTD: $15.3B|20 deals
Headcount 1-year change: -5%
Featured companies:
Together Mozilla.ai Contextual AI Mistral AI EleutherAI Databricks Hugging Face Google
OpenAI AI21 Labs Aleph Alpha Anthropic Cohere MosaicML Meta Inflection AI Adept
Stability AI LightOn Amazon Technology Innovation Institute
(A2A) payments (Banked and kevin.), and mobile wallets and remittances (DANA and
LemFi)
Data annotation
The data annotation market provides services for labeling large volumes
of data in preparation for training AI and ML models. This market
comprises both text and image & video annotation services. Most vendors
employ human annotators to classify and label datasets, with some
offering AI-powered automation tools to speed up the process.
LLMOps | 4
Equity funding 2023 YTD: $65M|3 deals
Headcount 1-year change: +20%
Featured companies:
CrowdWorks SuperAnnotate Datasaur Dataloop Labelbox Encord
Machine learning training data curation
The machine learning training data curation market offers solutions to
support data quality control in the AI algorithm training process. These
solutions help organizations complete key tasks, such as selecting the
best subsets of data for training models, triaging datasets for bias, and
identifying labeling errors. Ultimately, these solutions help minimize the
downstream effects of poor-quality data on AI performance.
Equity funding 2023 YTD: $2M|1 deal
Headcount 1-year change: +43%
Featured companies:
Scale Argilla Snorkel AI
Vector databases
The vector databases market focuses on providing databases optimized
for high-dimensional, vector-based data. These databases are designed to
efficiently store, manage, and query large volumes of vectors — i.e.,
mathematical representations of data points in multidimensional space.
LLMOps | 5
Vector databases cater to a wide range of applications, including machine
learning, natural language processing, recommendation systems, and
similarity search.
Equity funding 2023 YTD: $176M|5 deals
Headcount 1-year change: +68%
Featured companies:
Chroma Zilliz Pinecone Weaviate NNext Qdrant
AI development platforms
The AI development platforms market offers solutions that serve as
one-stop shops for enterprises that want to develop and launch in-house
AI projects. Vendors in this space enable organizations to manage all
aspects of the AI lifecycle — from data preparation, training, and validation
to model deployment and continuous monitoring — through a single
platform in order to facilitate end-to-end model development. Some
vendors offer “drag-and-drop” interfaces or “plug-and-play” solutions that
enable teams without in-depth AI expertise to build AI projects.
Equity funding 2023 YTD: $837M|11 deals
Headcount 1-year change: +21%
Featured companies:
Databricks Domino H2O.ai Iguazio BentoML MindsDB Valohai 2021.AI TurinTech
Lightning AI MosaicML Continual Clarifai Cerebrium DataRobot
LLMOps | 6
Prompt engineering
The prompt engineering market focuses on designing and generating
specific prompts for machine learning models. By carefully crafting the
input given to a language model, prompt engineering aims to guide the
model toward generating accurate outputs that align with the user’s
intentions. This process involves understanding the strengths and
weaknesses of the model and the task at hand. Ultimately, the solutions in
this space aim to improve model performance and reliability.
Equity funding 2023 YTD: $56M|3 deals
Headcount 1-year change: +18%
Featured companies:
Vellum PromptHero Orquesta PromptLayer Comet Weights & Biases Keytalk AI
Large language model (LLM) application development
The large language model (LLM) application development market includes
tools for customizing and refining pre-trained language models for specific
tasks and industries. Fine-tuning involves adjusting the weights of a model
or training the model on task-specific data to make it more accurate and
adaptable for particular applications. Companies in this market offer
services and tools to fine-tune large language models like GPT-3 or
open-source models.
Equity funding 2023 YTD: $314M|11 deals
Headcount 1-year change: +36%
LLMOps | 7
Featured companies:
Kern AI Vellum Lamini LlamaIndex Dify Taylor AI Hugging Face Rasa Explosion Fixie.ai
NuMind
Model deployment & serving
The model deployment & serving market bridges the gap between data
science and DevOps teams by taking trained machine learning models and
putting them into production. Vendors offer tools for machine learning
deployment on Kubernetes as well as serverless technology that can be
used to deploy AI in cloud and on-prem environments. Most deployment
vendors provide continuous model monitoring and governance tools.
Equity funding 2023 YTD: $29M|2 deals
Headcount 1-year change: -7%
Featured companies:
OctoML Modzy Seldon BentoML
Algorithmic auditing & risk management
The algorithmic auditing & risk management market provides solutions for
evaluating and mitigating risks associated with algorithmic
decision-making. These tools enable organizations to ensure algorithmic
fairness, transparency, and regulatory compliance. Vendors in this space
take a multifaceted approach to derisking AI, which includes data auditing,
model validation, metadata tracking, and post-production monitoring.
LLMOps | 8
Equity funding 2023 YTD: $1M|2 deals
Headcount 1-year change: +22%
Featured companies:
Armilla AI Credo AI
Model validation & monitoring
The model validation & monitoring market provides solutions that
continuously monitor the performance of AI models and provide real-time
visibility into model behavior. These solutions track outliers in predictions,
potentially biased outcomes, and suspected adversarial attacks. Demand
for these solutions is driven by the fact that AI model performance can
degrade over time if it continuously encounters real-world data that varies
significantly from its training data.
Equity funding 2023 YTD: $20M|3 deals
Headcount 1-year change: +13%
Featured companies:
Wallaroo Seldon Fiddler AI Vianai Systems Arthur Arize Superwise TruEra WhyLabs
Robust Intelligence TrojAI Aporia Evidently AI Censius
Machine learning security (MLSec)
The machine learning security (MLSec) market provides solutions
designed to protect machine learning models and algorithms from
LLMOps | 9
adversarial attacks, data poisoning, model evasion, backdoor injections,
and other cyber attacks. Vendors offer a range of products, including
intrusion detection systems, adversarial defense systems, secure machine
learning frameworks, and anomaly detection tools.
Equity funding 2023 YTD: $109M|4 deals
Headcount 1-year change: +35%
Featured companies:
Kobalt Labs Calypso AI Arthur WhyLabs TrojAI
Hardware-aware AI optimization
The hardware-aware AI optimization market provides software solutions
that optimize AI algorithms and models to run efficiently on available
hardware, such as GPUs and CPUs. These solutions also allow enterprises
to compress neural networks to run on edge devices or on-prem servers.
With optimization tools, businesses can speed up AI deployments, reduce
prediction latency, and improve model performance.
Equity funding 2023 YTD: No deals
Headcount 1-year change: +22%
Featured companies:
Neural Magic Run:AI Nota AI Deci
If you aren’t already a client, sign up for a free trial to learn more about our
platform.
LLMOps |
10
The large language model
operations (LLMOps)
market map
LLMOps | 2
LLMOps | 3
From prompt engineering to machine learning
security, we break down the vendors helping
enterprises deploy large language models.
The launch of OpenAI’s ChatGPT sparked an unprecedented surge of
corporate interest in large language models (LLMs).
Big tech companies and research institutes are democratizing access to
LLMs, enabling enterprises to more easily take advantage of deep learning
capabilities. As enterprises adopt these models, they are focused on
improving their data infrastructure, fine-tuning models for specific use
cases, and monitoring LLMs for hallucination and bias.
As a result, tech vendors supporting large language model operations
(LLMOps) — the end-to-end workflow that organizations employ to build,
fine-tune, and deploy LLMs into production — are gaining traction.
In the market map below, we identify 90+ companies across 12 different
categories helping enterprises bring LLM projects from start to finish.
Note: Our map includes public, private, and recently exited companies in
LLMOps. This market map is not exhaustive of the space.
LLMOps | 1
LLMOps | 2
Market comparisons
LLMOps | 3
Market descriptions
Generative AI — large language model developers
The generative AI — large language model developers market offers
foundation models and APIs that enable enterprises to build natural
language processing applications such as content creation,
summarization, classification, chatbots, sentiment analysis, and more.
Enterprises can fine-tune and customize these large-scale language
models — pre-trained on vast amounts of text — for their specific use
cases.
Equity funding 2023 YTD: $15.3B|20 deals
Headcount 1-year change: -5%
Featured companies:
Together Mozilla.ai Contextual AI Mistral AI EleutherAI Databricks Hugging Face Google
OpenAI AI21 Labs Aleph Alpha Anthropic Cohere MosaicML Meta Inflection AI Adept
Stability AI LightOn Amazon Technology Innovation Institute
(A2A) payments (Banked and kevin.), and mobile wallets and remittances (DANA and
LemFi)
Data annotation
The data annotation market provides services for labeling large volumes
of data in preparation for training AI and ML models. This market
comprises both text and image & video annotation services. Most vendors
employ human annotators to classify and label datasets, with some
offering AI-powered automation tools to speed up the process.
LLMOps | 4
Equity funding 2023 YTD: $65M|3 deals
Headcount 1-year change: +20%
Featured companies:
CrowdWorks SuperAnnotate Datasaur Dataloop Labelbox Encord
Machine learning training data curation
The machine learning training data curation market offers solutions to
support data quality control in the AI algorithm training process. These
solutions help organizations complete key tasks, such as selecting the
best subsets of data for training models, triaging datasets for bias, and
identifying labeling errors. Ultimately, these solutions help minimize the
downstream effects of poor-quality data on AI performance.
Equity funding 2023 YTD: $2M|1 deal
Headcount 1-year change: +43%
Featured companies:
Scale Argilla Snorkel AI
Vector databases
The vector databases market focuses on providing databases optimized
for high-dimensional, vector-based data. These databases are designed to
efficiently store, manage, and query large volumes of vectors — i.e.,
mathematical representations of data points in multidimensional space.
LLMOps | 5
Vector databases cater to a wide range of applications, including machine
learning, natural language processing, recommendation systems, and
similarity search.
Equity funding 2023 YTD: $176M|5 deals
Headcount 1-year change: +68%
Featured companies:
Chroma Zilliz Pinecone Weaviate NNext Qdrant
AI development platforms
The AI development platforms market offers solutions that serve as
one-stop shops for enterprises that want to develop and launch in-house
AI projects. Vendors in this space enable organizations to manage all
aspects of the AI lifecycle — from data preparation, training, and validation
to model deployment and continuous monitoring — through a single
platform in order to facilitate end-to-end model development. Some
vendors offer “drag-and-drop” interfaces or “plug-and-play” solutions that
enable teams without in-depth AI expertise to build AI projects.
Equity funding 2023 YTD: $837M|11 deals
Headcount 1-year change: +21%
Featured companies:
Databricks Domino H2O.ai Iguazio BentoML MindsDB Valohai 2021.AI TurinTech
Lightning AI MosaicML Continual Clarifai Cerebrium DataRobot
LLMOps | 6
Prompt engineering
The prompt engineering market focuses on designing and generating
specific prompts for machine learning models. By carefully crafting the
input given to a language model, prompt engineering aims to guide the
model toward generating accurate outputs that align with the user’s
intentions. This process involves understanding the strengths and
weaknesses of the model and the task at hand. Ultimately, the solutions in
this space aim to improve model performance and reliability.
Equity funding 2023 YTD: $56M|3 deals
Headcount 1-year change: +18%
Featured companies:
Vellum PromptHero Orquesta PromptLayer Comet Weights & Biases Keytalk AI
Large language model (LLM) application development
The large language model (LLM) application development market includes
tools for customizing and refining pre-trained language models for specific
tasks and industries. Fine-tuning involves adjusting the weights of a model
or training the model on task-specific data to make it more accurate and
adaptable for particular applications. Companies in this market offer
services and tools to fine-tune large language models like GPT-3 or
open-source models.
Equity funding 2023 YTD: $314M|11 deals
Headcount 1-year change: +36%
LLMOps | 7
Featured companies:
Kern AI Vellum Lamini LlamaIndex Dify Taylor AI Hugging Face Rasa Explosion Fixie.ai
NuMind
Model deployment & serving
The model deployment & serving market bridges the gap between data
science and DevOps teams by taking trained machine learning models and
putting them into production. Vendors offer tools for machine learning
deployment on Kubernetes as well as serverless technology that can be
used to deploy AI in cloud and on-prem environments. Most deployment
vendors provide continuous model monitoring and governance tools.
Equity funding 2023 YTD: $29M|2 deals
Headcount 1-year change: -7%
Featured companies:
OctoML Modzy Seldon BentoML
Algorithmic auditing & risk management
The algorithmic auditing & risk management market provides solutions for
evaluating and mitigating risks associated with algorithmic
decision-making. These tools enable organizations to ensure algorithmic
fairness, transparency, and regulatory compliance. Vendors in this space
take a multifaceted approach to derisking AI, which includes data auditing,
model validation, metadata tracking, and post-production monitoring.
LLMOps | 8
Equity funding 2023 YTD: $1M|2 deals
Headcount 1-year change: +22%
Featured companies:
Armilla AI Credo AI
Model validation & monitoring
The model validation & monitoring market provides solutions that
continuously monitor the performance of AI models and provide real-time
visibility into model behavior. These solutions track outliers in predictions,
potentially biased outcomes, and suspected adversarial attacks. Demand
for these solutions is driven by the fact that AI model performance can
degrade over time if it continuously encounters real-world data that varies
significantly from its training data.
Equity funding 2023 YTD: $20M|3 deals
Headcount 1-year change: +13%
Featured companies:
Wallaroo Seldon Fiddler AI Vianai Systems Arthur Arize Superwise TruEra WhyLabs
Robust Intelligence TrojAI Aporia Evidently AI Censius
Machine learning security (MLSec)
The machine learning security (MLSec) market provides solutions
designed to protect machine learning models and algorithms from
LLMOps | 9
adversarial attacks, data poisoning, model evasion, backdoor injections,
and other cyber attacks. Vendors offer a range of products, including
intrusion detection systems, adversarial defense systems, secure machine
learning frameworks, and anomaly detection tools.
Equity funding 2023 YTD: $109M|4 deals
Headcount 1-year change: +35%
Featured companies:
Kobalt Labs Calypso AI Arthur WhyLabs TrojAI
Hardware-aware AI optimization
The hardware-aware AI optimization market provides software solutions
that optimize AI algorithms and models to run efficiently on available
hardware, such as GPUs and CPUs. These solutions also allow enterprises
to compress neural networks to run on edge devices or on-prem servers.
With optimization tools, businesses can speed up AI deployments, reduce
prediction latency, and improve model performance.
Equity funding 2023 YTD: No deals
Headcount 1-year change: +22%
Featured companies:
Neural Magic Run:AI Nota AI Deci
If you aren’t already a client, sign up for a free trial to learn more about our
platform.
LLMOps |
10