Choosing the right provider
Selecting the right LLM inference provider is a critical decision that affects cost, performance, reliability, and the overall user experience of your AI application. With dozens of providers offering various models, pricing structures, and feature sets, the choice can feel overwhelming. This guide will help you navigate the landscape and make an informed decision based on your specific needs.
Understanding the provider landscape
The LLM inference provider ecosystem can be broadly categorized into several types, each serving different use cases and priorities.
Proprietary model providers
These companies develop and host their own models exclusively through their APIs. They offer cutting-edge performance but come with vendor lock-in and typically higher costs.
OpenAI (GPT-4o, GPT-4 Turbo, o1, o3-mini): Industry leader with the most mature API ecosystem and extensive tooling support
Anthropic (Claude 3.5 Sonnet, Claude 3.5 Haiku, Claude 3 Opus): Known for strong reasoning capabilities and context window up to 200K tokens
Google (Gemini 2.0 Flash, Gemini 1.5 Pro): Deep integration with Google Cloud and competitive pricing
Cohere (Command R+, Command R): Focused on enterprise RAG and multilingual capabilities
Mistral AI (Mistral Large, Mistral Small): European provider with strong open-weights models and API services
Cloud platform providers
Major cloud vendors offer managed AI services that provide access to multiple models through a single platform. They excel in enterprise features like compliance, security, and integration with existing cloud infrastructure.
AWS Bedrock: Access to Claude, Llama, Titan, and other models with AWS security and compliance
Google Vertex AI: Unified platform for Gemini, PaLM, and third-party models with MLOps tools
Azure OpenAI Service: OpenAI models with Microsoft’s enterprise SLAs and compliance guarantees
Open-source model hosting providers
These providers specialize in hosting open-weights models like Llama, Mistral, and Qwen. They offer lower costs and more flexibility than proprietary providers.
Together AI: High-performance inference for open models with competitive pricing
Fireworks AI: Fast inference with sub-second latency for popular open-source models
Replicate: Easy deployment with pay-per-use pricing and extensive model library
Hugging Face Inference API: Access to thousands of community models with simple deployment
Groq: Ultra-fast inference using custom LPU hardware for supported models
Inference provider routing platforms
These platforms aggregate multiple providers behind a unified API, enabling intelligent routing, fallback, and cost optimization.
Infron: Enterprise-grade routing platform with semantic caching, automatic failover, and cost optimization
Portkey: Multi-provider gateway with observability and prompt management
OpenRouter: Community-focused routing with transparent pricing and model availability
Key factors to consider
When evaluating providers, consider these critical dimensions:
1. Model availability and selection
Different providers offer different models. Your choice depends on which models best fit your use case.
Questions to ask:
Do they offer the specific models you need (e.g., GPT-4o, Claude 3.5, Llama 3.3)?
How quickly do they support newly released models?
Can you deploy custom fine-tuned models?
Do they support both proprietary and open-source options?
Best practices:
Start with a provider that offers multiple model families to avoid early lock-in
Evaluate model quality for your specific tasks before committing
Consider providers that support both closed and open models for flexibility
2. Pricing structure and cost predictability
LLM inference costs can vary dramatically between providers. Understanding the pricing model is crucial for budget planning.
Common pricing models:
Per-token pricing: Most common; charged per 1K or 1M tokens (input + output)
Per-request pricing: Fixed cost per API call, regardless of token count
Subscription tiers: Monthly fees with included token quotas
Compute-time pricing: Charged by GPU/second (common for self-hosted options)
Cost optimization strategies:
Use routing platforms to automatically select cheaper models for simple queries
Enable semantic caching to avoid redundant API calls
Monitor token usage and optimize prompts to reduce costs
Consider open-source models for high-volume, less critical tasks
Example cost comparison (as of 2026):
OpenAI
GPT-4o
$2.50
$10.00
Anthropic
Claude 3.5 Sonnet
$3.00
$15.00
Gemini 2.0 Flash
$0.10
$0.40
Together AI
Llama-3.3-70B
$0.88
$0.88
Groq
Llama-3.3-70B
$0.59
$0.79
Note: Prices change frequently. Always check current pricing before making decisions.
3. Performance and latency
Performance varies significantly between providers, even for the same model. Key metrics include:
Time to First Token (TTFT): How quickly the first response token arrives (critical for interactive applications)
Inter-Token Latency (ITL): Time between successive tokens (affects streaming UX)
Throughput: Requests processed per second (important for high-volume applications)
Cold start time: Delay when scaling up resources (serverless deployments)
Performance best practices:
Test providers with your actual workload before production deployment
Consider geographic proximity—choose regions close to your users
For latency-sensitive apps, prioritize providers with <200ms TTFT
Monitor P95 and P99 latency, not just averages
4. Rate limits and quotas
Rate limits can become a bottleneck as your application scales.
Common limit types:
Requests per minute (RPM): Maximum API calls per minute
Tokens per minute (TPM): Maximum tokens processed per minute
Concurrent requests: Maximum simultaneous requests
Daily/monthly caps: Total usage limits within a time period
Strategies for handling rate limits:
Request limit increases proactively before hitting constraints
Use multiple API keys to distribute load
Implement request queuing and retry logic with exponential backoff
Consider routing platforms that automatically distribute requests across multiple providers
5. API compatibility and developer experience
Switching providers should be as painless as possible. API compatibility reduces migration friction.
OpenAI API compatibility: Most modern providers now offer OpenAI-compatible endpoints, allowing you to switch providers with minimal code changes. This includes:
Request/response format matching OpenAI’s specification
Drop-in replacement for OpenAI SDKs
Support for features like function calling, streaming, and vision
Developer experience factors:
Quality of documentation and examples
SDK availability (Python, JavaScript, Go, etc.)
Debugging and error messages
Community support and resources
6. Reliability and uptime
Provider reliability directly impacts your application’s availability.
Reliability indicators:
Historical uptime: Check status pages for past incidents
SLA guarantees: Enterprise providers typically offer 99.9%+ uptime SLAs
Geographic redundancy: Multi-region deployments reduce risk
Status transparency: Real-time status pages and incident communication
Building resilient systems:
Implement automatic failover to backup providers
Use routing platforms for built-in redundancy
Set appropriate timeouts and retry policies
Monitor provider health continuously
7. Data privacy and compliance
For enterprise applications, data handling and compliance are non-negotiable.
Key considerations:
Data retention policies: How long is your data stored?
Training data usage: Is your data used to train or improve models?
Regional compliance: GDPR, HIPAA, SOC 2, ISO 27001 certifications
Data residency: Can you control where data is processed and stored?
Zero data retention: Some providers offer zero-retention modes for sensitive data
Compliance by provider type:
Cloud platforms (AWS, Azure, Google): Strongest enterprise compliance
Proprietary providers: Typically offer enterprise plans with compliance guarantees
Open-source hosting: Varies widely; check individual provider certifications
8. Feature support
Advanced features can significantly enhance your application capabilities.
Common features to evaluate:
Streaming responses: Token-by-token output for better UX
Function calling: Tool use and structured outputs
Vision capabilities: Image understanding and multimodal inputs
JSON mode: Guaranteed valid JSON outputs
Fine-tuning support: Ability to customize models
Batch inference: Cost-efficient processing for non-real-time workloads
Decision framework: Which provider is right for you?
Use this framework to guide your selection based on your specific scenario.
Scenario 1: Early-stage startup or MVP
Priorities: Speed to market, simplicity, low initial cost
Recommended approach:
Start with OpenAI or Anthropic for proven quality and extensive documentation
Use their pay-as-you-go pricing to minimize upfront commitment
Once you validate product-market fit, explore cost optimization
Why: Focus on building features, not managing infrastructure. The slight premium is worth the reduced complexity.
Scenario 2: Cost-sensitive, high-volume application
Priorities: Cost efficiency, scalability, acceptable quality
Recommended approach:
Evaluate open-source model providers like Together AI, Fireworks, or Groq
Consider routing platforms to dynamically select cheaper models based on query complexity
Implement aggressive caching and prompt optimization
Why: At scale, even small per-token savings add up. Open-source models like Llama 3.3 or Qwen2.5 offer 70-90% cost savings with competitive quality.
Scenario 3: Enterprise with strict compliance requirements
Priorities: Security, compliance, SLAs, data residency
Recommended approach:
Use cloud platform providers like AWS Bedrock, Azure OpenAI, or Google Vertex AI
Ensure contracts include BAAs (for HIPAA), DPAs (for GDPR), and compliance certifications
Deploy within your existing cloud VPC for maximum control
Why: Enterprise features, compliance guarantees, and integration with existing security infrastructure are worth the premium.
Scenario 4: Latency-critical real-time application
Priorities: Sub-200ms response time, consistent performance
Recommended approach:
Test providers like Groq (specialized hardware) or Gemini Flash (optimized for speed)
Deploy in geographic regions closest to your users
Use streaming responses to improve perceived latency
Implement prefix caching for common prompt patterns
Why: User experience in chat, voice, or gaming applications depends on snappy responses.
Scenario 5: Research or experimentation
Priorities: Model variety, flexibility, rapid iteration
Recommended approach:
Use Hugging Face Inference API or Replicate for access to thousands of models
Leverage routing platforms to easily A/B test different models
Consider local inference tools like Ollama for offline experimentation
Why: Rapid experimentation requires access to diverse models without operational overhead.
Scenario 6: Multi-model, production-scale application
Priorities: Reliability, cost optimization, flexibility, observability
Recommended approach:
Adopt an inference provider routing platform like Infron or Portkey
Connect multiple underlying providers (OpenAI, Anthropic, open-source hosts)
Implement intelligent routing based on cost, latency, and availability
Enable semantic caching to reduce costs by 40-60%
Why: No single provider is perfect. Routing platforms give you the best of all worlds—reliability through redundancy, cost optimization through intelligent routing, and flexibility to adapt as models and providers evolve.
The multi-provider strategy
For production applications at scale, relying on a single provider creates unnecessary risk:
Single-provider risks:
Outages: When your provider goes down, your application goes down
Rate limits: Hitting limits during traffic spikes degrades user experience
Price changes: Sudden pricing increases directly impact your unit economics
Model deprecation: Providers occasionally sunset models, forcing migrations
Regional availability: Limited geographic coverage increases latency for global users
Benefits of multi-provider architecture:
Resilience: Automatic failover maintains uptime during provider outages
Cost optimization: Route simple queries to cheaper models, complex ones to premium models
Performance: Select providers with lowest latency for each region
Flexibility: Easily adopt new models and providers without infrastructure changes
Negotiation leverage: Provider diversity strengthens your negotiating position
Implementing multi-provider infrastructure:
The traditional approach requires building custom integration logic for each provider, managing different authentication schemes, implementing your own routing logic, and maintaining separate monitoring for each provider. This is complex and time-consuming.
The modern approach uses a routing platform that provides:
Unified API across all providers
Automatic failover and load balancing
Cost-based intelligent routing
Centralized observability and analytics
Built-in caching and rate limit management
Common pitfalls to avoid
1. Optimizing for cost too early
Many teams immediately jump to the cheapest option, sacrificing quality and velocity. In the early stages, speed of iteration matters more than marginal cost savings.
2. Ignoring rate limits
Hitting rate limits in production creates terrible user experiences. Always test your expected peak load against provider limits before launch.
3. Over-engineering for scale
Don’t build distributed multi-region infrastructure when you’re processing 100 requests per day. Start simple and scale when you actually need it.
4. Vendor lock-in through proprietary features
Deeply integrating provider-specific features (like OpenAI’s Assistant API) makes migration painful. When possible, use standard interfaces.
5. Not testing actual performance
Published benchmarks don’t reflect real-world performance on your specific workload. Always benchmark with your actual prompts and traffic patterns.
Migration and switching strategies
Switching providers shouldn’t require a complete rewrite. Here’s how to maintain flexibility:
1. Abstract your LLM interface
Create a thin wrapper around your LLM calls that isolates provider-specific logic. This makes switching providers a configuration change, not a code change.
2. Use OpenAI-compatible APIs
Most providers now offer OpenAI-compatible endpoints. By writing code against the OpenAI SDK format, you can easily swap providers by changing the base URL.
3. Leverage routing platforms
Routing platforms provide a single API that abstracts all provider differences. Switching becomes a dashboard configuration change, not a deployment.
4. Maintain provider-agnostic prompts
Avoid prompts that rely on provider-specific behavior. Test prompts across multiple providers to ensure consistent quality.
The future of LLM providers
The provider landscape is evolving rapidly. Key trends to watch:
Commoditization of base capabilities: As open-source models close the quality gap, differentiation will shift from raw model quality to reliability, latency, and ecosystem features.
Vertical specialization: Providers will increasingly specialize by industry (legal, medical, financial) or task type (code generation, reasoning, translation).
Edge and on-device inference: Local inference will grow for privacy-sensitive and latency-critical applications, reducing reliance on cloud providers.
Standardization of APIs: OpenAI API compatibility is becoming the de facto standard, making provider switching increasingly frictionless.
Consolidation and partnerships: Expect M&A activity as smaller providers are acquired by cloud platforms or consolidate for scale.
FAQs
Can I use multiple providers simultaneously?
Yes, and it’s increasingly common. Many teams use OpenAI for complex reasoning tasks, Anthropic for long-context analysis, and open-source models for high-volume classification. Routing platforms make this pattern easy to implement.
How do I handle model version updates?
Pin to specific model versions in production (e.g., gpt-4o-2024-08-06 instead of gpt-4o) to avoid unexpected behavior changes. Test new versions in staging before upgrading production.
What’s the difference between using a provider directly vs. through a routing platform?
Direct integration gives you maximum control and potentially lower latency (one less hop). Routing platforms add intelligent features like automatic failover, cost optimization, caching, and unified observability—but introduce a small latency overhead (typically 10-50ms).
Should I build my own multi-provider routing logic?
Only if you have specific requirements that existing platforms don’t meet. Building production-grade routing logic requires handling authentication, retries, rate limits, failover, caching, and monitoring for each provider—a significant engineering investment.
How do I evaluate new providers?
Create a benchmark suite with representative prompts from your application. Test for quality (human evaluation or LLM-as-judge), latency (TTFT and ITL), throughput, and cost. Run tests across multiple time periods to account for variability.
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