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LangChain vs Together.ai: Detailed Comparison Guide
- Leanware Editorial Team
- 2 hours ago
- 11 min read
LangChain orchestrates LLM workflows, letting you connect models to tools, data sources, and custom logic with Python or JavaScript. It gives precise control over how a language model interacts with your application. Together.ai focuses on scalable model inference, hosting open-source models on cloud GPUs with a simple REST API, so you don’t have to manage infrastructure.
LangChain handles the task and decision logic, while Together.ai executes the models. Let’s compare their features, pricing, and performance to see where each fits best.
What is LangChain?
LangChain is a framework for building applications powered by language models. It provides components for prompt management, tool integration, retrieval systems, and agent workflows. The framework supports Python and JavaScript and has active development.
It structures LLM applications as chains of operations where each step processes input and passes results forward. You can connect models to vector databases, APIs, web scrapers, and custom functions through standardized interfaces. The framework handles common patterns like retrieval-augmented generation, conversational memory, and multi-step reasoning.
What is Together.ai?
Together.ai operates inference infrastructure for open-source language models. The platform hosts over 100 models including Llama 3, Mistral, Mixtral, and CodeLlama on optimized GPU clusters. Developers access these models through a REST API compatible with OpenAI's interface.
The service handles model deployment, scaling, and optimization. You don't manage GPU instances or model weights. Together.ai's infrastructure provides lower latency and higher throughput than running models yourself, particularly for open-source models that require significant compute resources.
Key Features Compared
AI Development Capabilities
LangChain focuses on orchestration and workflow management. You build chains that combine model calls with data retrieval, processing, and output formatting. The framework includes components for document loading, text splitting, embedding generation, and result aggregation.
Together.ai focuses on model inference. You send prompts and receive completions. The platform handles model serving, load balancing, and infrastructure scaling. Together.ai doesn't provide orchestration tools, you build that logic in your application or use a framework like LangChain.
Prompt Engineering & Management
LangChain includes prompt templates with variable substitution, few-shot examples, and output parsers. You can version prompts, test variations, and compose complex prompt structures. The framework integrates with LangSmith for prompt monitoring and A/B testing.
Together.ai accepts standard text prompts through its API. The platform doesn't include prompt management tools. You handle prompt construction and versioning in your application code or through external tools.
Agentic Process Automation
LangChain provides agent frameworks where models decide which tools to use and in what order. Agents can plan multi-step workflows, call functions, and adjust their approach based on intermediate results. The LangGraph extension adds graph-based agent execution for complex control flow.
Together.ai doesn't include agent capabilities. The API returns model completions but doesn't orchestrate multi-step workflows. You can build agents by calling Together.ai's API repeatedly with updated prompts, but the platform doesn't provide agent abstractions.
LLM API & Inference
Together.ai provides direct model inference through a REST API. The platform optimizes model serving for throughput and latency. Features include streaming responses, batched requests, and support for large context windows. The API matches OpenAI's format, making it compatible with most LLM libraries.
LangChain wraps inference APIs from multiple providers including OpenAI, Anthropic, and Together.ai. The abstraction lets you swap providers without rewriting code. However, this adds a small overhead compared to calling APIs directly.
Cloud GPU & Infrastructure
Together.ai manages GPU infrastructure, handling model deployment across distributed clusters. The platform uses techniques like tensor parallelism and optimized kernels to maximize throughput. You don't configure GPU instances or manage model serving.
LangChain doesn't provide infrastructure. You bring your own compute whether that's calling external APIs, running local models, or deploying to cloud platforms. LangChain applications run anywhere Python or JavaScript runs.
Integration and Deployment
Available Integrations
LangChain integrates with dozens of model providers, vector databases, and tools. Supported providers include OpenAI, Anthropic, Cohere, Hugging Face, and Together.ai. Vector database integrations cover Pinecone, Weaviate, Chroma, and Qdrant. The framework includes loaders for various file formats and data sources.
Together.ai integrates through its OpenAI-compatible API. Any tool supporting OpenAI's API format works with Together.ai by changing the base URL. This includes LangChain, LlamaIndex, OpenAI's SDKs, and numerous other libraries.
Deployment Options
LangChain applications deploy wherever you run your application code. Options include serverless functions, container platforms, traditional servers, and edge environments. The framework itself doesn't dictate deployment architecture. LangServe provides FastAPI-based serving but you can use any web framework.
Together.ai is a hosted service. You access it through API calls from your application. The platform handles all infrastructure, scaling, and model deployment. This simplifies operations but means you depend on Together.ai's availability and network latency.
API Access and Flexibility
LangChain API
LangChain provides modular components that you compose into applications. The API is extensive and flexible but requires learning the framework's abstractions. Components include LLMs, chat models, embeddings, retrievers, chains, and agents.
The modularity helps with complex applications but adds overhead for simple tasks.
You might write 50 lines of LangChain code for something that's 10 lines with direct API calls. The benefit appears when adding features like memory, retrieval, or multi-step workflows.
Together.ai API
Together.ai uses a simple REST API matching OpenAI's format. You POST JSON with your prompt and parameters, receiving completions in response. The API supports streaming, function calling, and structured outputs for compatible models.
Here's a basic example:
import requests
response = requests.post(
"https://api.together.xyz/v1/chat/completions",
headers={"Authorization": f"Bearer {api_key}"},
json={
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"messages": [{"role": "user", "content": "Explain quantum computing"}]
}
)The simplicity makes Together.ai easy to integrate but you build orchestration logic yourself.
Pricing Comparison
LangChain Pricing
LangChain is open source and free. Costs come from the services you integrate: model APIs, vector databases, and compute resources. LangSmith adds monitoring and tracing for $39 per month on the team plan, with usage-based pricing at higher tiers.
Together.ai Pricing
Together.ai charges per token, with costs varying by model. Smaller models like Llama 3.2 3B Instruct Turbo cost around $0.06 per million input tokens and $0.06 per million output tokens.
Mid-sized models such as Mistral 7B Instruct are about $0.20 per million tokens, while larger models like Mixtral 8x7B sit around $0.60 per million tokens. The largest models, including Llama 3.3 70B Instruct-Turbo, run approximately $0.88 per million tokens.
The platform uses a pay-as-you-go model with no minimum spend, so you only pay for the tokens processed. High-volume users can benefit from batch inference discounts, and dedicated GPU endpoints or fine-tuning carry separate token-based or hourly costs.
What's the minimum monthly spend to get started with each platform?
You can start using LangChain without any monthly cost, as the framework itself is fully open source. If you want to monitor and trace your workflows, LangSmith offers a free Developer plan with 5,000 traces per month, which is usually enough for prototyping and small applications. Only when you exceed that quota or require team-level features do paid plans come into play, starting around $39 per seat per month.
Together.ai also has no minimum monthly requirement and bills strictly based on token usage. This means you can begin with very low usage — even a few million tokens per month - and pay only for what you process, which often amounts to just a few dollars. This setup makes it easy to experiment with both platforms before committing to higher volumes or enterprise-level usage.
How much does it cost to run 1 million tokens through Together.ai vs using LangChain with OpenAI?
Together.ai: Costs depend on the model. For gpt-oss-120B, you pay $0.15 per million input tokens and $0.60 per million output tokens. Smaller models like gpt-oss-20B cost $0.05 per million input tokens and $0.20 per million output tokens. Billing is strictly token-based.
LangChain with OpenAI: LangChain itself is free, but costs come from OpenAI API usage. For 1 million tokens on GPT-4.1, input tokens cost $3.00, cached input $0.75, output $12.00, and fine-tuning training $25.00 per million tokens. LangChain helps track usage through LangSmith, but all charges are billed directly by OpenAI.
Performance and Model Support
Model Variety and Customization
Together.ai hosts over 100 open-source models covering various sizes and specializations. You can use general models like Llama and Mistral, code-focused models like CodeLlama and WizardCoder, or specialized models for specific tasks. The platform adds new models regularly as the open-source community releases them.
LangChain supports any model accessible through an API or locally. This includes commercial APIs like OpenAI and Anthropic, open-source models through Together.ai or Replicate, and local models through Ollama or llama.cpp. The abstraction layer lets you swap models without changing application logic.
Supported LLMs
Together.ai's model catalog includes:
Llama 3 (8B, 70B, 405B variants)
Mistral and Mixtral models
CodeLlama for code generation
Qwen and Yi models
DBRX, StripedHyena, and other architectures
LangChain integrates with these providers out of the box:
OpenAI (GPT-3.5, GPT-4, GPT-4 Turbo)
Anthropic (Claude models)
Together.ai (all hosted models)
Cohere, AI21, Hugging Face
Local models through various libraries
Which is faster for RAG applications: LangChain + OpenAI or Together.ai direct?
RAG applications need both retrieval and generation. LangChain handles the retrieval part through vector store integrations. The generation speed depends on your LLM provider.
Together.ai's Mixtral-8x7B generates tokens faster than GPT-4 due to the smaller model size and optimized serving. Latency typically runs 200-500ms for first token with Together.ai versus 500-1500ms for GPT-4. However, GPT-4 often needs fewer tokens to produce quality output.
For RAG specifically, use LangChain for orchestration with Together.ai as the inference backend. This combines LangChain's retrieval tools with Together.ai's fast inference.
What's the cold start time for Together.ai vs LangChain + Vercel?
Together.ai keeps models warm on their infrastructure. Cold starts don't exist in the traditional sense. First request latency is similar to subsequent requests, typically 200-800ms depending on model size.
LangChain applications on Vercel serverless functions experience cold starts of 1-3 seconds while the function initializes. The LLM API call time adds to this. Using Together.ai doesn't eliminate Vercel cold starts, but the inference itself stays fast.
User Experience and Support
LangChain is code-first, so you define application logic in Python or JavaScript and test chains through LangSmith, though most development happens in your IDE. Together.ai provides a web dashboard for API key management, usage tracking, and model browsing, while development itself is done via API calls.
Documentation for both platforms is sufficient to get started: LangChain offers detailed guides and code examples, though it can lag behind rapid updates, whereas Together.ai focuses on concise API usage and integration instructions.
Both have active communities and support channels, with LangChain offering a Slack community and GitHub issues, and Together.ai providing email support and Discord access.
What happens when Together.ai rate limits kick in and how do I handle them?
Together.ai implements rate limits based on your plan tier. Free tier users face stricter limits than paid customers. When you hit limits, requests return 429 status codes.
Handle rate limits by implementing exponential backoff in your code:
import time
def call_together_with_retry(prompt, max_retries=3):
for attempt in range(max_retries):
try:
response = call_together_api(prompt)
return response
except RateLimitError:
wait_time = (2 ** attempt) + random.random()
time.sleep(wait_time)
raise Exception("Max retries exceeded")Most LLM libraries including LangChain include retry logic. You can also request rate limit increases by contacting Together.ai support.
How do I debug LangChain chains when using Together.ai models?
LangSmith provides the best debugging experience for LangChain applications. It traces each step in your chain, showing inputs, outputs, and timing. This works regardless of which LLM provider you use.
For basic debugging without LangSmith, enable verbose mode in LangChain:
from langchain.chains import LLMChain
from langchain.llms import Together
llm = Together(model="mistralai/Mixtral-8x7B-Instruct-v0.1")
chain = LLMChain(llm=llm, prompt=prompt_template, verbose=True)This prints each step's execution to the console. For production debugging, LangSmith's tracing shows exactly what Together.ai receives and returns.
Use Cases
LangChain is strong for multi-step workflows:
Document Q&A: Retrieve and answer from documents.
Conversational agents: Maintain context and access tools.
Data pipelines: Extract and validate structured info.
Research assistants: Synthesize and report from multiple sources.
Together.ai is ideal for scalable model inference:
High-volume APIs: Handle millions of requests.
Cost-sensitive apps: Use open-source models.
Specialized models: Access models not available elsewhere.
Development/testing: Experiment without managing infrastructure.
Technical Considerations
Can I use LangChain with Together.ai's API, and what's the actual code to set it up?
Yes, LangChain includes a Together integration. Here's how to configure it:
from langchain_together import ChatTogether
llm = ChatTogether(
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
together_api_key="your-api-key" # or set TOGETHER_API_KEY env var
)
messages = [
("system", "You are a helpful assistant."),
("human", "Write a brief explanation of quantum computing")
]
response = llm.invoke(messages)For non-chat models, use the Together class:
from langchain_together import Together
llm = Together(model="codellama/CodeLlama-70b-Python-hf")
result = llm.invoke("def bubble_sort():")This setup lets you use Together.ai models within LangChain chains, combining Together's fast inference with LangChain's orchestration capabilities.
How long does it take to migrate from OpenAI to Together.ai using LangChain?
Migration takes 10-30 minutes if you're already using LangChain's abstraction layer. Change the LLM initialization from OpenAI() to Together() and adjust any model-specific parameters. Test thoroughly since different models have different capabilities and output formats.
If you're calling OpenAI's API directly without LangChain, migration takes longer. You need to rewrite API calls to match Together.ai's format or introduce LangChain as an abstraction layer.
What breaks when switching between LangChain versions (0.1.x to 0.2.x)?
LangChain 0.2.x introduced breaking changes in how chains and agents work. Major differences include:
New chain constructor syntax
Updated agent initialization patterns
Changed import paths for some components
Modified callbacks and streaming interfaces
Budget 2-4 hours to update a medium-sized application. The LangChain migration guide covers specific changes but you'll need to test thoroughly since the changes affect core abstractions.
Can Together.ai handle function calling like OpenAI, and how?
Yes, Together.ai supports function calling with an OpenAI-compatible API format. You define tools in your request and compatible models return structured function calls:
import requests
tools = [{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City and state, e.g. San Francisco, CA"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"]
}
}
}]
response = requests.post(
"https://api.together.xyz/v1/chat/completions",
headers={"Authorization": f"Bearer {api_key}"},
json={
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"messages": [{"role": "user", "content": "What's the weather in Paris?"}],
"tools": tools,
"tool_choice": "auto"
}
)Compatible models include Mixtral, Mistral, CodeLlama, and DeepSeek-R1. Check Together.ai's model documentation for current function calling support.
Getting Started
LangChain and Together.ai handle different parts of the LLM stack. Use LangChain to coordinate multiple LLM calls, retrieve data, or connect tools, and use Together.ai for fast, low-cost inference with open-source models.
Often, they’re combined: LangChain manages the workflow, while Together.ai handles model inference. You can also use them separately - LangChain works for local experiments or APIs like OpenAI, and Together.ai is enough for simple model completions.
You can also connect to us for guidance on integrating LangChain with Together.ai, optimizing workflows, or setting up cost-effective inference pipelines.
Frequently Asked Questions
Is LangChain better than Together.ai?
Neither is better because they serve different purposes. LangChain orchestrates AI workflows while Together.ai provides model inference. They complement each other rather than compete. Many production applications use LangChain for application logic with Together.ai as the model provider.
Can I use LangChain and Together.ai together?
Yes, they work together seamlessly. LangChain includes a Together integration that lets you use Together.ai models in chains and agents. This combination is common in production applications, giving you LangChain's orchestration features with Together.ai's fast inference and competitive pricing for open-source models.
What's the main difference between LangChain and Together.ai?
LangChain is an orchestration framework that helps you build complex AI workflows with tools, memory, and multi-step reasoning. Together.ai is an inference API that hosts open-source language models on optimized infrastructure. LangChain handles the "what to do" while Together.ai handles the "how to run the model.
Is LangChain free to use?
Yes, LangChain is open source and free to use. Costs come from the services you connect to it, like OpenAI's API, Together.ai, vector databases, or cloud hosting. LangSmith monitoring is optional and costs $39 per month for the team plan.
How much does Together.ai cost per month?
Together.ai uses pay-as-you-go pricing with no monthly minimums. Costs depend on which models you use and how many tokens you process. Light usage might cost $10-30 monthly. Production applications processing millions of requests typically spend $200-2000+ monthly depending on model choice and volume.
Which is easier for beginners: LangChain or Together.ai?
Together.ai is simpler for basic model inference since it's just API calls. LangChain has a steeper learning curve due to its abstractions and component system. However, LangChain becomes easier for complex applications since it handles orchestration patterns you'd otherwise build manually. Start with Together.ai for simple prompts, learn LangChain when you need chains or agents.
Does LangChain work with GPT-4?
Yes, LangChain supports all OpenAI models including GPT-4, GPT-4 Turbo, and GPT-3.5. It also works with Claude models from Anthropic, open-source models through Together.ai, and dozens of other providers. The abstraction layer lets you swap models without rewriting application code.
What models are available on Together.ai?
Together.ai hosts over 100 open-source models including Llama 3 (8B to 405B), Mistral, Mixtral, CodeLlama, Qwen, and specialized models for various tasks. The platform adds new models regularly as the open-source community releases them. Check their model catalog for the current list with pricing and context window details.
Can I self-host LangChain?
Yes, LangChain runs anywhere Python or JavaScript runs. Deploy it on your own servers, in containers, as serverless functions, or at the edge. The framework doesn't require external services, though you might integrate with hosted APIs for models or vector databases. Together.ai is a hosted service but you can call it from self-hosted LangChain applications.
Which is better for RAG applications?
LangChain excels at RAG with built-in components for document loading, text splitting, embedding generation, vector storage, and retrieval. Together.ai provides fast inference for the generation step. The best approach combines both: use LangChain's RAG components with Together.ai models for cost-effective, high-performance retrieval-augmented generation.