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Best LLM Inference Providers: Speed and Cost Benchmarks 2026

Deploybase · February 23, 2026 · LLM Pricing

Contents

Best LLM Inference Providers: Overview

Best LLM Inference Providers is the focus of this guide. Three categories of providers exist as of March 2026. Managed APIs (OpenAI, Anthropic, Cohere) handle everything. Specialized platforms (Together AI, Fireworks AI) chase speed and cost. Self-hosted (RunPod, Lambda) gives developers control at the cost of ops.

Managed APIs: Reliability and ease. Developers get API keys, no infrastructure. Pricing per token but opaque. Hidden tiers. Teams want simplicity more than cost savings.

Specialized platforms: For cost-conscious builders. Open models, no restrictions. Distillation and quantization for speed. Transparent pricing per request or token. Need to know the models.

Self-hosted: Minimize marginal cost. Rent GPUs, run the models. Hardware rental covers storage. Lots of ops work. For teams with engineering capacity and high throughput needs.

Latency Benchmarks

Two metrics: time to first token (TTFT) and time per token (TpT). Interactive apps need low TTFT. Batch jobs don't care.

OpenAI GPT-4o: 300-800ms TTFT depending on load. 40-60ms TpT. Variance spikes 2-4x during peak hours.

Anthropic Claude: 400-900ms TTFT. 50-70ms TpT. More consistent under load.

Together AI Llama 70B: 150-300ms TTFT. 20-30ms TpT. Dedicated hardware, no queueing. Beats managed APIs.

Fireworks AI Llama 70B: 120-250ms TTFT. 15-25ms TpT. Slightly better than Together.

Cohere Command R+: 200-500ms TTFT. 30-45ms TpT.

Model size matters. 7B-13B: 50-100ms TTFT. 70B+: 200-500ms.

Batch latency is different. 100 requests batched together amortize startup. Lower per-request time but 1-5 minutes total instead of milliseconds.

Throughput and Concurrency

Throughput: tokens per second. Concurrency: simultaneous requests without degradation.

OpenAI: 100-500 concurrent requests depending on tier. Exceeds 100K tokens/sec for large batches. Latency climbs under sustained load.

Together AI: Token-bucket rate limiter. 50-100K tokens/min default. Remove limits with dedicated deployments. Scales linearly until GPU exhaustion (~5000 concurrent tokens in flight).

Fireworks AI: Similar scaling. Flexible token limits, auto-scaling for spikes. Load-balancing keeps throughput consistent.

Self-hosted: One H100 does ~1000 tokens/sec. Multiple GPUs scale linearly via tensor parallelism. GPU memory limits concurrency; bigger batches = higher latency.

Cost Analysis

Price varies by model and volume. Small (7B-13B): $0.10-$0.50 per 1M tokens. Large (70B+): $1-$5 per 1M. Managed APIs charge 2-5x open models through convenience tax.

GPT-4o: $2.50 per 1M input, $10 per 1M output (public). Production tiers: $1-$2 input, $3-$5 output.

Llama 70B: $0.75 per 1M tokens (Together). $0.65 (Fireworks).

Self-hosted: RunPod H100 at $2.69/hr. 10K tokens in 10 seconds = $0.0075. ~$0.75 per 1M tokens. Matches or beats specialized platforms.

Monthly comparison (10M input + 5M output):

OpenAI: $25 + $50 = $75 Together AI Llama 2: $7.50 + $3.75 = $11.25 Self-hosted (RunPod): $30-$40

Self-hosted wins at scale. Specialized platforms win for cost-conscious teams without eng resources.

Model Availability

Managed APIs: Proprietary only. OpenAI: GPT-4o, GPT-4-turbo, GPT-3.5-turbo. Anthropic: Claude 3 variants. Cohere: Command R+. Limited, curated selection.

Specialized platforms: Open models. Together AI: Llama 2 (7B-70B), Mistral, Qwen, NousResearch, more. Fireworks similar. Latest open models land here first.

Self-hosted: Any model. GGUF quantized versions work. vLLM and text-generation-webui simplify it. Unlimited selection, manual hosting.

Fine-tuning: OpenAI does GPT-3.5/GPT-4. Anthropic doesn't. Together supports open model fine-tuning. Self-hosted uses standard training frameworks.

Production Features

Reliability: OpenAI 99.95% SLA. Anthropic 99.95% for production. Together/Fireworks 99.9%.

Retries: Managed APIs retry internally, transparent to developers. Specialized platforms vary - check docs. Self-hosted needs app-level retry logic.

Rate limits: OpenAI enforces per-minute/per-day (varies by tier). Together: per-month token budget. Fireworks: token-bucket. Self-hosted: just hardware capacity.

Monitoring: Managed APIs basic dashboards. Specialized: token tracking, latency histograms, error rates. Self-hosted needs Prometheus, Datadog, etc.

SLA credits: Managed APIs refund unplanned outages. Together/Fireworks don't.

FAQ

Q: Commit to one provider? No. Multi-provider reduces risk. Route cheap requests to cheap providers, complex ones to premium. Load-balance across APIs.

Q: Handle rate limits? Request queuing with exponential backoff. Monitor rate limit headers. Ask providers for increases when approaching caps.

Q: Cache outputs? Yes. Full responses for identical queries. Semantic caching is emerging for similar queries too. Saves 20-50% depending on workload.

Q: Privacy with managed APIs? Data goes to provider servers. Production agreements restrict training use. Sensitive data? Self-host or use privacy-focused APIs.

Q: Benchmark providers? Run identical prompts on each. Measure latency, cost, quality. 100+ requests to average variance. Document for ROI analysis.

Q: Vendor lock-in? Managed APIs lock you in. Code changes to switch. Use abstraction layers (LangChain) for flexibility. Open models reduce risk.

OpenAI API Pricing Anthropic API Pricing Together AI Pricing Fireworks AI Pricing LLM API Pricing Comparison GPU Pricing Comparison

Sources

Provider Documentation (March 2026) Third-party Latency Benchmarks Cost Analysis Studies Production ML Deployment Reports API Reliability Data