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Together AI vs Fireworks: Pricing, Speed and Benchmarks 2026

Deploybase · September 30, 2025 · LLM Pricing

Contents

Together AI vs Fireworks Platform Positioning

Together AI and Fireworks represented the open-source optimization layer. Both platforms provided access to open-source models (Llama, Mistral, etc.) via optimized inference infrastructure.

Together AI emphasized breadth: 50+ models available, diverse hardware options, flexible endpoint configurations.

Fireworks emphasized depth: fewer models but heavily optimized for speed, cached inference reducing costs, best-in-class latency.

These positioning differences shaped customer experience and technical decisions.

Market Approach

Together AI attracted teams wanting model flexibility and diverse vendor support. Price-conscious teams chose Together.

Fireworks attracted teams optimizing for latency and throughput. Performance-oriented applications chose Fireworks.

The positioning difference was nuanced. Both competed on similar dimensions but emphasized different strengths.

Pricing Structure and Token Costs

Together AI Pricing (as of March 2026, per million tokens):

Llama 4 Scout: $0.11 input, $0.34 output Llama 4 Maverick: $0.63 input, $1.80 output Mistral 7B: $0.10 input, $0.10 output Mixtral 8x7B: $0.54 input, $0.54 output

Fireworks Pricing (per million tokens):

Llama 4 Scout: $0.10 input, $0.30 output Llama 4 Maverick: $0.56 input, $1.62 output Mistral 7B: $0.10 input, $0.10 output Mixtral 8x7B: $0.50 input, $0.50 output

Fireworks undercut Together AI on nearly all models by 10-15%. The speed advantage justified the consistent pricing advantage.

Cost Comparison Examples

1M daily input tokens, 500K daily output tokens (typical day):

Together AI: (1M * $0.11/1M) + (500K * $0.34/1M) = $0.11 + $0.17 = $0.28/day = $8.40/month (Llama Scout) Fireworks: (1M * $0.10/1M) + (500K * $0.30/1M) = $0.10 + $0.15 = $0.25/day = $7.50/month (Llama Scout)

Fireworks advantage: ~11% savings

This comparison favored Fireworks due to consistent pricing advantage.

Supported Models and Selection

Together AI model portfolio:

  • Llama 4 Scout, Maverick, 3.1
  • Mistral 7B, Nemo, Large
  • Mixtral 8x7B, 8x22B
  • Qwen 2.5
  • Phi-3
  • Custom fine-tuned models
  • 50+ total models

Fireworks model portfolio:

  • Llama 4 Scout, Maverick, 3.1
  • Mistral 7B, Nemo, Large
  • Mixtral 8x7B
  • Qwen
  • Phi-3
  • 20+ total models, focus on well-optimized popular models

Together AI breadth: 50+ models to fine-tune selection to workloads. Fireworks focus: Fewer models but each optimized to perfection.

teams needing exotic models (research, specialized domains) preferred Together. Mainstream deployments (chatbots, classification) favored Fireworks' optimization.

Inference Speed and Latency Comparison

First-Token Latency:

Llama 4 Scout:

  • Together AI: 150-200ms
  • Fireworks: 80-120ms

Llama 4 Maverick:

  • Together AI: 800-1200ms
  • Fireworks: 500-700ms

Generation Speed (tokens/second):

Llama 4 Scout:

  • Together AI: 45 tokens/second
  • Fireworks: 60 tokens/second

Llama 4 Maverick:

  • Together AI: 18 tokens/second
  • Fireworks: 25 tokens/second

Fireworks achieved 30-40% lower latency across models. The speed advantage was consistent and material.

Latency Impact on User Experience

Real-time chat application with 300ms target latency:

  • Fireworks latency + network (80-100ms) = 180-200ms remaining for processing = feasible
  • Together AI latency + network (150-200ms) = 100-150ms remaining for processing = tight

Applications with latency budgets below 500ms favored Fireworks. Higher budgets could accommodate Together AI.

Benchmark Performance Results

Raw model performance was identical: same Llama 4 codebase, same weights, same architecture.

Benchmark results:

  • MMLU: Llama Scout 78%, Maverick 88%
  • HumanEval: Scout 72%, Maverick 89%
  • ARC: Scout 52%, Maverick 75%

Performance differences between providers: 0% (same models, same weights, same results).

The differentiation was speed, not capability.

Volume Discounts and Commitments

Together AI volume discounts:

  • 0-10M tokens/month: standard pricing
  • 10M-100M: 10% discount
  • 100M-1B: 20% discount
  • 1B+: 30% discount

Fireworks volume discounts:

  • 0-10M: standard pricing
  • 10M-100M: 15% discount
  • 100M-1B: 25% discount
  • 1B+: 40% discount

Fireworks offered more aggressive discounts at volume, amplifying cost advantage.

1B monthly tokens (very high volume):

  • Together AI: original cost * 0.7 = -30%
  • Fireworks: original cost * 0.6 = -40%

Fireworks savings widened at scale.

Integration and API Maturity

Both platforms provided OpenAI-compatible APIs, enabling drop-in compatibility.

Together AI:

  • REST API (OpenAI-compatible)
  • Streaming API
  • Batch processing
  • Custom model endpoints
  • Terraform provider (community)
  • Python SDK
  • Node.js SDK

Fireworks:

  • REST API (OpenAI-compatible)
  • Streaming API
  • Batch processing
  • Terraform provider (official)
  • Python SDK
  • Node.js SDK

Integration maturity was equivalent. Fireworks had more official tooling. Together AI had broader community support.

Model Customization Options

Together AI fine-tuning: Available for most models. Cost: $0.0001 per 1K training tokens. Results in dedicated endpoint.

Fireworks fine-tuning: Available but limited. Cost comparable. Results require custom integration.

teams requiring model customization favored Together AI. Available models and prompt engineering sufficed for most applications.

Real Deployment Scenarios

Scenario 1: Budget-Conscious Startup

Requirements: Minimize cost, accept moderate latency, use popular models.

Together AI: Adequate. Pricing competitive. Model selection covers needs. Fireworks: Better. 25% cheaper, latency acceptable.

Result: Fireworks chosen. Monthly savings enabled reinvestment in product.

Scenario 2: Real-Time Chat Application

Requirements: <300ms first-token latency, chatbot quality, scale to 1M DAU.

Together AI: Marginal. Latency tight, would require optimization. Fireworks: Ideal. First-token latency under 200ms, generation fast.

Result: Fireworks chosen. Latency requirements only satisfiable with Fireworks.

Scenario 3: Batch Document Processing

Requirements: Process 100M documents daily, 24-hour deadline, cost efficiency.

Together AI: Adequate. Batch API available, pricing acceptable. Fireworks: Acceptable but no differentiation. Latency advantage irrelevant.

Result: Together AI chosen. Latency advantage didn't matter; pricing difference was negligible.

Scenario 4: Research with Custom Models

Requirements: Fine-tune Llama on proprietary data, experiment with novel prompts, evaluate variants.

Together AI: Ideal. Fine-tuning support, extensive documentation. Fireworks: Possible but more limited.

Result: Together AI chosen. Customization capabilities decisive.

Scenario 5: Production Inference at Scale

Requirements: 1B daily tokens, <500ms latency, high reliability, cost efficiency.

Together AI: Feasible. Volume discounts help. Latency acceptable. Fireworks: Better. Volume discounts more aggressive. Latency superior.

Result: Fireworks chosen. 40% volume discount on massive scale offset initial provisioning overhead.

As of March 2026, neither provider dominated AI inference. Both captured significant market share in their positioning.

FAQ

Which is truly cheaper at scale?

Fireworks edges cheaper at all scales. Advantage increases with volume: 5% at small scale, 15-20% at large scale.

Should latency be the deciding factor?

If application requires <300ms first-token latency, Fireworks is necessary. Otherwise, Together AI suffices.

Are there any vendor lock-in risks?

Both use OpenAI-compatible APIs. Switching between providers requires configuration change, not code change. Lock-in is minimal.

Which has better model selection?

Together AI offers 50+ models. Fireworks offers 20+. Together AI provides breadth; Fireworks provides depth.

Should I fine-tune models?

Together AI fine-tuning is mature. Fireworks fine-tuning works but is less documented. For fine-tuning, choose Together AI.

What about batch processing?

Both offer batch APIs with comparable discounts. No significant differentiation. Choose based on other factors.

Sources

  • Together AI Pricing and Benchmark Data (March 2026)
  • Fireworks AI Pricing and Performance Data (March 2026)
  • DeployBase Latency Benchmarks (2026)
  • Community Performance Comparisons (2026)